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
Self-Powered Sensing
Self-powered sensing is a sensing operational principle under which a sensor node harvests all required electrical working energy directly from surrounding ambient energy sources instead of relying on external wired power supply connections or pre-installed chemical batteries [1]. Available ambient energy forms that can be harvested include mechanical vibration, kinetic motion, thermal temperature gradients, ambient light illumination and environmental electromagnetic radiation [2]. The harvested energy is conditioned, regulated and stored locally to power sensor transduction, signal processing and data transmission functions. This concept defines the energy supply mechanism of the sensing node. It imposes no inherent restriction on the types of physical measurands, sensor transduction principles or signal output formats of the sensing device itself, and is applicable across temperature, vibration, chemical, biological and other sensing modalities [3].
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  • 21 Sep 2026
Topic Review
Water Infrastructure
Water infrastructure is a collective term for the interconnected network of physical engineered assets dedicated to the collection, storage, treatment, conveyance, distribution and disposal of water for municipal, industrial, agricultural and environmental purposes [1]. Its constituent facilities include raw water intake structures, water treatment plants, storage reservoirs, pumping stations, pressurised water supply pipe networks, sanitary sewer collection pipelines and wastewater treatment facilities [2]. This concept refers exclusively to physical hardware systems and built assets. Institutional management mechanisms, regulatory policy frameworks, water resource allocation rules and pricing mechanisms are not components of the water infrastructure definition itself, although they interact with and govern the operation of these physical assets [3].
  • 13
  • 21 Sep 2026
Topic Review
Autonomous Systems
Autonomous systems are technical systems equipped with integrated sensing, state estimation, reasoning, decision-making and actuation subsystems that can perceive external environmental states, generate context-appropriate goal-oriented decisions and execute corresponding physical actions without continuous real-time human operator command intervention [1]. Autonomy operates within predefined functional and environmental boundaries set by high-level human mission objectives; it does not imply full independence from human-set top-level goals, nor self-generated mission purposes outside originally defined operational constraints [2]. Different real-world implementations exhibit varying degrees of autonomy, ranging from conditional partial autonomy under human supervision to high-level full autonomy in structured environments. The core conceptual characteristic lies in the closed-loop perception-decision-action mechanism, distinguishing it from open-loop purely automated equipment that executes fixed pre-programmed sequences without environmental perception and adaptive feedback [3].
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  • 20 Sep 2026
Topic Review
Human–Computer Interaction
Human–Computer Interaction (HCI) is an interdisciplinary academic and engineering field that investigates the reciprocal communicative relationships between human operators and computational systems across multiple layers of perception, cognition, action and feedback [1]. It examines how human users acquire information from system outputs, formulate cognitive intentions, execute control commands through input devices, and interpret system responses to accomplish intended task goals [2]. Its conceptual scope spans physical hardware interfaces, graphical software interfaces, haptic and auditory interaction channels, as well as the cognitive and ergonomic principles that govern effective and usable interaction design. HCI is not limited to any single device category or application domain; instead, it establishes fundamental principles governing human-machine communication that apply across desktop computing, mobile devices, industrial control systems and immersive environments, integrating perspectives from computer science, cognitive psychology, ergonomics, industrial design and information science [3].
  • 19
  • 21 Sep 2026
Topic Review
Supply Chain Management
Supply chain management refers to the integrated planning, coordination, execution, and control of all business activities associated with the flow of goods, services, information, and financial resources from raw-material sourcing through production, distribution, and final-product delivery to end customers across participating organisations within a supply-chain network [1]. It encompasses cross-organisational coordination among suppliers, manufacturers, logistics providers, distributors, and end-users, aiming to align material and information flows under defined operational, cost, and service-level objectives [2]. Unlike isolated intra-enterprise functional management, SCM emphasises end-to-end governance of the entire network boundary rather than optimisation of any single firm's internal departments in isolation. It covers both forward physical-product flow and reverse-logistics product returns, and it integrates demand forecasting, inventory planning, transportation scheduling, procurement decision-making, warehouse management, and customer-service fulfilment as interconnected components. The discipline treats the supply chain as an interdependent system in which local decisions by one member propagate effects throughout the network, and it seeks to balance total system cost against required service levels rather than minimising any single node's cost independently [3].
  • 14
  • 20 Sep 2026
Topic Review
Rock Mass Classification
Rock mass classification (or rock mass rating) is a quantitative empirical procedure by which the quality, competence, and expected engineering behavior of a jointed rock mass are summarized into a finite set of classes or an index number based on a small number of geomechanical parameters [1]. Typical input parameters include the uniaxial compressive strength of the intact rock, rock quality designation (RQD), joint spacing, joint condition (roughness, weathering, infilling), groundwater condition, and joint orientation relative to the excavation [2]. The two most widely used systems are the Rock Mass Rating (RMR) system of Bieniawski, which sums weighted ratings to classify the mass into five classes and to derive support recommendations, and the Q-system of Barton, Lien, and Lunde, which expresses rock-mass quality as a product of ratios of RQD, joint-set number, joint roughness, joint alteration, joint water reduction, and stress reduction factors [2][3]. Classification systems are distinguished from continuous rock-mass strength criteria (such as the Hoek–Brown failure criterion) by their empirical, table-based, and design-rule-oriented character.
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  • 18 Sep 2026
Topic Review
Solar Photovoltaic System
A solar photovoltaic (PV) system is an electrical system that converts incident solar radiation directly into electrical energy through the photovoltaic effect, in which photons absorbed by a semiconductor generate electron–hole pairs that are separated by a junction to produce direct-current electricity [1]. The fundamental conversion unit is the solar cell, a large-area semiconductor diode—most commonly based on crystalline silicon—whose p–n junction produces a photocurrent when illuminated and whose current–voltage characteristic defines its short-circuit current, open-circuit voltage, and maximum power point [2]. A complete PV system combines PV modules (interconnected arrays of cells) with balance-of-system components—including mounting structures, wiring, maximum-power-point-tracking electronics, and inverters that convert the generated direct current to alternating current for grid connection or off-grid storage [1]. The system is distinguished from solar thermal systems, which convert sunlight into heat rather than electricity, and from other power-generation sources by the direct solid-state conversion of photon energy to electrical energy without moving thermodynamic cycles [2]. The electrical output of a module is characterized under standardized reference irradiance and temperature conditions so that different systems can be compared on a common basis [3].
  • 19
  • 18 Sep 2026
Topic Review
Bootstrap Confidence Intervals
Bootstrap confidence intervals are interval estimates of a population parameter constructed by resampling the observed data with replacement, rather than by relying on an analytic sampling distribution or an assumed parametric form [1]. In the bootstrap procedure, a large number of bootstrap replicates are drawn from the original sample, each replicate having the same size as the sample and containing observations drawn uniformly at random with replacement [2]. The statistic of interest is recomputed on each replicate, yielding the bootstrap distribution of the statistic; this distribution serves as an empirical estimate of the sampling distribution from which standard errors, bias estimates, and confidence intervals are derived [1]. Several interval constructions exist: the percentile interval uses the empirical quantiles of the bootstrap distribution; the basic bootstrap interval reflects the difference between the estimate and its bootstrap quantiles; and bias-corrected and accelerated intervals adjust for bias and skewness in the bootstrap distribution [3]. Bootstrap confidence intervals are distinguished from analytic intervals by their reliance on computationally intensive resampling and by their weaker assumptions about the underlying distribution [2].
  • 21
  • 18 Sep 2026
Topic Review
Single-Slope Analog-to-Digital Converter
A single-slope analog-to-digital converter (single-slope ADC) is an integrating-type ADC that converts an input analog voltage into a digital word by measuring the time required for a linear ramp waveform to cross the input voltage [1]. The conversion begins with the reset of an integrator capacitor; the integrator then generates a voltage that ramps linearly at a known constant rate while a counter is incremented by a fixed-frequency clock [2]. When the ramp voltage equals the input voltage, a comparator stops the counter, and the counter reading is proportional to the input voltage, yielding the digital output [1]. Because the conversion relies on a single ramp and a single comparison per conversion, the architecture is simple, and its differential linearity is largely determined by the linearity of the ramp generator and the stability of the clock [2]. The single-slope ADC is distinguished from the dual-slope (dual-ramp) ADC, which uses an additional de-integration interval to cancel errors due to ramp slope and component drift [2]. Its conversion time is signal-dependent and relatively long, which places it among low-speed, high-resolution ADC architectures [3].
  • 26
  • 18 Sep 2026
Topic Review
Reynolds-Averaged Navier-Stokes
The Reynolds-averaged Navier–Stokes (RANS) equations are the time-averaged form of the Navier–Stokes equations obtained by decomposing each instantaneous flow variable into a mean (time-averaged) component and a fluctuating turbulent component, then averaging the equations over time or ensemble [1]. Substituting the decomposed velocity and pressure into the instantaneous Navier–Stokes equations and averaging introduces an additional unknown term—the Reynolds stress tensor, representing the momentum transport by turbulent fluctuations—so that the RANS system is no longer closed and requires a turbulence model to relate the Reynolds stresses to the mean flow field [1]. The closure problem is resolved by eddy-viscosity models, which express the Reynolds stresses through an eddy (turbulent) viscosity obtained from algebraic, one-equation, or two-equation transport models, or by Reynolds-stress transport models that solve transport equations for the individual stress components [2]. RANS equations predict only the statistically averaged mean flow rather than resolved turbulent motions, and are distinguished from direct numerical simulation, which resolves all scales, and from large-eddy simulation, which resolves large eddies while modeling only small scales [3].
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