Engineering

Abstract: Marine diesel engines generate high concentrations of sub-micron particulate matter (PM) that requires effective exhaust aftertreatment. While conventional wire-in-tube electrostatic precipitators (ESP) offer a low-drag solution, their practical efficiency is limited by particle re-entrainment at elevated flow velocities. This study investigates a novel application of corrugated cylindrical ducts—standard vibration-compensating couplings—as electrostatic collectors. A fully coupled two-dimensional axisymmetric COMSOL Multiphysics model was developed, integrating turbulent flow (k–ε), electrostatics, ion charge transport, and particle tracing. Numerical results demonstrate that while smooth and corrugated geometries yield identical theoretical Deutsch–Anderson efficiency (61.1% at U_in = 0.5 m/s, the corrugated profile significantly suppresses re-entrainment. The corrugations reduce wall shear stress by a factor of 7.7 to 13.5 at flow velocities of 0.3–0.8 m/s, maintaining aerodynamic conditions below critical particle detachment thresholds. With a pressure drop penalty representing less than 6% of the localized corona power, these findings show that existing marine exhaust infrastructure can be repurposed as high-efficiency, zero-re-entrainment particle collectors through the integration of cold plasma electrodes.

Abstract: Residential buildings must now be designed and retrofitted as adaptive climate-health-work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. The review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is an implementation-oriented framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance.

Abstract: Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately.This study develops a data-driven multi-task temporal fusion framework for joint 48 hahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar-stress series,local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal from 2021-03-11 to 2025-03-06. Target responses are aligned only at commonmeasuredtimestamps; no synthetic target observations are introduced. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer 10 encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. On the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack-response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data-owner restrictions.

Abstract: In the field of power management circuits (PMC) for low-power thermoelectric generators (TEG) intended for autonomous sensors, this article experimentally evaluates the alternatives commercially available. Considering their limitations in terms of minimum input voltage and power efficiency, this article also proposes and experimentally characterizes a circuit topology that combines and interconnects two different PMC alternatives so as to achieve the benefits of both. Thanks to this interconnection, the resulting circuit is able to start operating from a low input voltage (i.e., 40 mV of TEG open-circuit voltage), which is really attractive for TEGs under low thermal gradients, with a satisfactory power efficiency (i.e., up to 78 %).

Abstract: This study investigates the mathematical modeling of photovoltaic (PV) systems and the solution of the maximum power point tracking (MPPT) problem using numerical methods. Due to the nonlinear characteristics of PV systems, analytical solutions are often insufficient, therefore numerical methods offer a more suitable approach. In this study, the maximum power point was determined using Newton-Raphson, Bisection, and Secant methods, and the performance of these methods was analyzed comparatively. The PV system was modeled in MATLAB based on the single-diode equivalent circuit model and investigated under different irradiation and temperature conditions. The results show that the Newton-Raphson method provides fast convergence but exhibits sensitivity to the initial value, the Bisection method offers high stability, and the Secant method provides a balanced solution between speed and computational cost. This study is important in demonstrating the effectiveness of numerical methods in solving the MPPT problem in PV systems. These findings show that numerical methods offer effective and applicable solutions for MPPT in PV systems.

Abstract: The present work investigates fluid–-structure instabilities and stall flutter of a pitching NACA0012 airfoil through numerical simulations. The flow is modeled using the compressible Navier–-Stokes equations in a non-inertial rotating reference frame, while the structural dynamics are represented by a torsional spring–-mass–-damper system. The analysis focuses on the effects of reduced velocity, equilibrium angle of attack, and elastic axis position on the aeroelastic behavior. The results show a transition from steady flow to vortex-shedding regimes and, at higher reduced velocities, to limit-cycle oscillations. Increasing the equilibrium angle of attack leads to an earlier onset of instability and stronger aerodynamic forcing, while moving the elastic axis downstream has a similar destabilizing effect due to the larger aerodynamic moment arm. Frequency analysis highlights the progressive coupling between fluid and structural dynamics: vortex shedding dominates at low reduced velocity, whereas the structural frequency governs the response in the limit-cycle regime. The study provides a consistent description of the mechanisms driving stall flutter and of the parameters influencing aeroelastic stability.

Abstract: The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including policy conditions, fuel prices, end-use electrification rates, and CO2 capture rates, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into representative scenarios. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The results confirm diverse solution generation, identification of dominant factors, and extraction of five representative scenarios, enabling systematic distinction between common and variable elements characterizing net-zero pathways. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty.

Abstract: Grape bagasse is an abundant agro-industrial by-product and an important source of phenolic compounds with antioxidant properties. This study evaluated Soxhlet extrac-tion, supercritical fluid extraction (SFE), pressurized liquid extraction (PLE), subcriti-cal water extraction (SWE), and sequential extraction strategies for recovering bioac-tive compounds from grape bagasse. Box–Behnken designs were applied to SFE and PLE to evaluate process effects on extraction yield, while total phenolic content (TPC), total anthocyanin content (TAC), and antioxidant activity (ABTS) were additionally determined for PLE extracts. Hydroethanolic extractions showed greater selectivity toward phenolic compounds, whereas water-based extractions promoted higher yields associated with additional polar constituents. In SWE, increasing temperature en-hanced extraction yield and phenolic recovery, although anthocyanin contents de-creased under more severe thermal conditions. SWE provided higher extraction yields than PLE with comparable phenolic content and antioxidant activity, suggesting the recovery of additional highly polar non-phenolic compounds, whereas PLE resulted in higher extraction yields than SFE. Sequential extraction demonstrated that the first step accounted for most of the phenolic recovery and antioxidant activity, while the second aqueous step increased overall extraction yield. The sequential PLE–SWE route resulted in the highest TPC (198.0 mg GAE/g) and antioxidant activity (2321 μmol TE/g), demonstrating the potential of sequential extraction for grape bagasse fraction-ation and valorization.

Abstract: In electrical installations and equipment, electric current preferentially flows through me-tallic circuits that offer it less opposition, that is, less electrical resistance. In the human body, electric current also seeks to flow through paths with less electrical resistance, and may be diverted inside the body, damaging surrounding organs and tissues. Its path and the magnitude of the current influence the type of injuries associated with electrical acci-dents, usually burns, which can be internal and external. In all electrical installations, there is a need and obligation to implement effective protection systems, with the aim of operating them under safe conditions. In Low Voltage Electrical Installations (LVEI), the protection measures that must be considered are overcurrent protection, overvoltage pro-tection, but fundamentally the protection of people from the risk of electrocution, avoiding pathophysiological effects that can be irreversible.

Abstract: A numerical study of shock wave propagation through multiple raindrops is presented using a density-based compressible two-phase flow solver coupled with a sharp-interface volume-of-fluid (VoF) method. The piecewise linear interface calculation (PLIC) approach is employed to reconstruct gas–liquid interfaces and capture droplet deformation during shock interaction. The numerical framework is first validated using a one-dimensional gas–liquid shock tube problem and a shock–helium bubble interaction benchmark. The method is then applied to investigate shock interactions with single, double, and multiple raindrops under compressible flow conditions. Numerical results show that complex wave structures, including shock reflection, diffraction, and wave interference, develop during shock propagation through raindrop fields. Interactions between neighboring droplets lead to local pressure amplification and non-uniform flow structures.

Abstract: This paper presents an Adaptive Quantum Fuzzy Logic (ALFQ) control strategy applied to an induction motor (IM) supplied by a hybrid renewable energy system integrating photovoltaic (PV), wind, and thermoradiative (TR) energy sources. Conventional Vector Control (VC) and classical Fuzzy Logic Control (FLC) approaches are not fully developed in this study; instead, their main limitations and performances reported in the literature are used as comparative references in order to focus on the proposed quantum-based control strategy. The main objective is to investigate the dynamic response, robustness against parameter uncertainties, harmonic performance, and adaptability of the proposed ALFQ controller under varying operating conditions. The ALFQ approach combines fuzzy inference mechanisms with quantum-inspired optimization algorithms capable of automatically adjusting the membership functions and minimizing the speed tracking error. Simulation results obtained under MATLAB/Simulink demonstrate that the proposed ALFQ controller significantly improves the dynamic behavior of the induction motor drive. The obtained results show fast response time, quasi-zero overshoot, negligible steady-state error, reduced current ripple, and lower Total Harmonic Distortion (THD). The controller also exhibits excellent robustness against rapid load disturbances, rotor resistance variations, and nonlinear operating conditions. A comparative analysis based on literature results confirms that the proposed ALFQ strategy outperforms conventional VC and classical FLC approaches in terms of tracking accuracy, robustness, harmonic reduction, and global dynamic stability. These results demonstrate the effectiveness of quantum-inspired fuzzy optimization for high-performance induction motor drives supplied by hybrid renewable energy systems.

Abstract: The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in cocoa-producing regions such as Santander and Norte de Santander, Colombia. These by-products, constituting approximately 70% of the fruit’s total weight, are currently underutilized, generating pollution and wasting resources with high valorization potential. This article proposes the design and rigorous experimental validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), both exhibiting identical coefficients of variation (6.25%). Furthermore, the integration of an optimized extraction sequence combined with neural networks allows for maximizing by-product yields while reducing final residue generation by 40%. This tool not only contributes to the circular economy and alignment with the Sustainable Development Goals (SDGs 9 and 12) but also offers a tangible pathway to improve the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.

Abstract: Building Information Modeling (BIM) represents a building as a static snapshot of the model’s state. The IFC standard does not define a formal mechanism that would link the same physical element across successive phases of a building’s life cycle. Design, construction, and operation are recorded in separate IFC files, and the same element is assigned different GUIDs in each. The result is fragmentation of the element’s identity, loss of the history of property changes, and the inability to formulate cross-phase queries. This paper proposes the BIM-Phase ontology, based on the fundamental DOLCE ontology, which solves this problem by introducing a distinction between a building element as an endurant and its life cycle phases as perdurants. The ontology comprises nine classes, six object relations, and six axioms expressed in OWL 2 DL. Phase properties and relations are represented using a reification pattern, which maintains full compatibility with the expressiveness of OWL 2 DL. The ontology was validated using the example of a single-family residential building developed in Autodesk Revit. Three structural elements (external wall, floor slab, column) were tracked across three phases of the life cycle. Eight competency questions covering scalar, constitutional, and mereological changes were defined and mapped to ontology constructs, confirming that BIM-Phase enables the recording of changes and the formulation of cross-phase queries that are impossible in classic IFC.

Abstract: The Job Shop Scheduling Problem (JSSP) is a paradigmatic and strongly NP-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600-second wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from 6 × 5 up to 1000 × 1000 operations. OptalCP emerges as the most robust engine overall, certifying optimality on 57.5% of the instances with the smallest average optimality gap (3.55%), while Hexaly dominates on industrial-scale problems and produces the bulk of 31 new best-known upper bounds and one new best-known lower bound reported here. Solver competitiveness depends sharply on instance size and on the n/m ratio, with square instances confirmed as the hardest case. These findings support an instance-aware approach to CP solver selection in industrial scheduling.

Abstract: Earth blocks are attractive for low-cost housing because they use local soil, require less firing energy, and can provide good thermal mass, but their adoption in humid tropical regions is limited by moisture sensitivity. This review examines how agricultural waste ash stabilizers, with emphasis on palm kernel shell ash and related pozzolanic residues, influence moisture durability, dry/wet compressive strength behavior, and practical suitability of earth blocks for affordable housing. The paper synthesizes evidence from compressed earth block literature, pozzolanic material standards, and studies on ash-modified earthen masonry. It argues that wet-to-dry strength retention is a more realistic durability indicator than dry compressive strength alone because low-cost walls are exposed to wind-driven rain, capillary rise, damp surfaces, and imperfect maintenance. The review shows that ash stabilizers can improve particle bonding and pore refinement when properly processed, proportioned, compacted, and cured, but excessive ash, poor soil selection, or inadequate detailing can increase water absorption and reduce field reliability. The paper proposes a moisture-durability framework connecting material chemistry, block production, wall detailing, and tropical housing performance. It concludes that agricultural waste ash stabilized earth blocks are promising only when laboratory strength gains are tied to water-resistance testing and moisture-conscious architectural detailing.

Abstract: Long Range (LoRa) is a chirp spread spectrum (CSS) physical-layer technology that has become a leading candidate for low-power wide-area network (LPWAN) connectivity in the Internet of Things (IoT). At the receiver, the standard demodulator multiplies the incoming signal with a conjugate reference chirp and applies a one-dimensional discrete Fourier transform (DFT), reducing symbol detection to peak search in the frequency domain. While this non-coherent baseline is simple and robust under additive white Gaussian noise (AWGN), its symbol error rate (SER) degrades significantly in frequency-selective multipath channels, where parasitic spectral peaks distort the dominant tone. This paper presents a unified comparative study of seven LoRa detectors for spreading factor seven, six of which share a common one-dimensional DFT engine while a matched-filter bank operates directly in the time domain, with the six DFT detectors differing in their per-bin frequency-domain weighting and decision rule. The detector set spans the standard non-coherent DFT, a non-coherent matched filter bank, two coherent equalizers in the frequency domain (zero-forcing and minimum mean-square error), a phase-only equalizer, a maximal-ratio combiner with non-coherent decision, and an exhaustive maximum-likelihood detector that serves as a near‑optimal reference under the same preamble‑based CSI. To mitigate inter-symbol interference in the multipath case, every transmitted symbol is preceded by a cyclic prefix that converts the linear convolution with the channel into a circular convolution, enabling per-bin frequency-domain processing. Throughout the paper a deployment-realistic receiver model is adopted: the per-bin channel response is estimated by a frequency-domain least-squares estimator from a short preamble, and the noise variance is estimated blindly from the preamble residuals. The quality of the noise-variance estimator is reported separately as a diagnostic. Each detector is evaluated under both AWGN and a two-tap Rayleigh multipath channel through Monte Carlo simulation, and its execution time per call is recorded to provide a complementary view of computational cost. The framework introduced here clarifies how coherent processing, diversity combining, equalization, and exhaustive search trade detection performance against complexity within a single DFT-centric LoRa receiver architecture. The principal quantitative finding is that, under the two-tap Rayleigh multipath channel, the MMSE equalizer reaches SER ≈ 4.4×10⁻⁵ at SNR = −5 dB and tracks the exhaustive maximum-likelihood detector within 0.1 dB across the full SNR sweep, while costing only 1.26× the per-symbol time of the standard DFT receiver. Conversely, the standard non-coherent baseline hits an irreducible 16% error floor and the unregularized zero-forcing equalizer fails to reach the 10⁻² SER level at any SNR considered, isolating MMSE as the recommended choice in the multipath regime at every SNR for which a LoRa link is operationally viable.

Abstract: Electrochemical CO₂ reduction to ethanol is a promising route for circular-carbon fuel and chemical production, but practical implementation remains limited by coupled membrane, catalyst, transport and system-integration constraints. This Communication reassesses anion-exchange membranes (AEMs) and bipolar membranes (BPMs) using recent 2024–2026 literature. The central argument is that membrane selection is not a passive separation choice; it controls local pH, charge carriers, CO₂ availability, carbonate formation, water activity, proton/cation deliv-ery, product crossover and downstream techno-economic assessment (TEA) and life cycle assessment (LCA) burdens. AEM operation can create alkaline cathodic microenvironments that favor C–C coupling, but bicarbonate/carbonate formation imposes carbon-loss, salt-management and recovery penalties. BPM operation can improve pH separation and carbon management through water dissociation and bicarbonate acidification, but its viability depends on water-dissociation efficiency, co-ion exclusion, junction stability and voltage control. Recent ethanol-selective catalyst studies further show that copper oxidation state, grain boundaries, sub-surface dopants, ionomers, interfacial wettability and dynamic operation interact strongly with membrane-imposed microenvironments. The Communication pro-poses a membrane-centered decision framework linking AEM/BPM selection with ethanol selectivity, single-pass carbon utilization, energy efficiency, durability, TEA/LCA boundaries and future reactor design.

Abstract: Managing safety and operational efficiency in large-scale events requires tools capable of capturing complex crowd dynamics while supporting rapid and informed decision-making. This paper presents a Generative AI-powered digital twin framework that integrates agent-based crowd simulation, an API-based execution pipeline, and a Large Language Model (LLM)-driven conversational interface within a unified system. The proposed approach enables dynamic configuration, execution, and analysis of crowd scenarios under varying operational conditions, including high-demand and emergency evacuation contexts. Experimental results demonstrate the system’s ability to reproduce nonlinear crowd dynamics, detect congestion patterns, and evaluate evacuation performance, providing actionable insights for planning and safety assessment. A key contribution lies in the introduction of an API-based execution paradigm that exposes the full simulation lifecycle (configuration, validation, execution, and output retrieval) through programmatic interfaces, enabling reproducible and scalable what-if analysis. Additionally, the integration of an LLM-based conversational interface allows non-technical users to interact with complex simulation models through natural language, significantly improving accessibility and usability. The framework is validated through a TRL-4 prototype, demonstrating robust performance, scalability, and interaction reliability. Overall, the proposed system advances digital twins from static analytical tools to executable, interactive, and user-centric platforms for decision support in complex urban environments.

Abstract: Nowadays, the organisational landscape aiming to provide value through their product and service offerings relies on having the infrastructure necessary to deliver at the expected service levels, as well as contributing to business continuity and organisational resilience in the face of modern organisational performance disruptions. This requires appropriate adaptation of existing frameworks, methods, and models to their business models which have generated consistent deliverables across time and industries. The same is applicable for the Romanian Information Technology (IT) organisations, which face increasing pressure to deliver within the expected quality, time, and budget parameters. Therefore, this paper aims to assess how stakeholder relationship management components, viewed through the Malcolm Baldrige National Quality Award (MBNQA) excellence framework, with impact on organisational quality and its contribution to business continuity and organisational resilience in Romanian IT organisations. This is a pilot-type study with a sample of N = 52 participants, to explore the applicability of the MBNQA framework within the Romanian IT sector. The results suggest that the four components of MBNQA focused on stakeholders (Leadership and Governance, Workforce, Customers and Markets, Community Engagement) may be suitable to be considered in assessment tools on the Romanian IT market. The “Workforce” variable emerges as the strongest area to focus on for achieving quality in stakeholder relationship management (SRM). Given its pilot delimitation, this study provides can be seen as providing an initial foundation for applying MBNQA in a specific regional IT context. While limited by sample size and geographic focus, the findings justify expanding the research to include broader population segments. Future research could transition from this correlational design to longitudinal frameworks to validate the associations across other multiple geographical markets.

Abstract: The pronounced aero-hydrodynamic coupling effects of modern Wind-Assisted Propulsion System (WAPS) ships challenge the applicability of traditional stability frameworks, which are predicated on hydrostatic energy balance, in satisfying the dynamic constraints of the Second Generation Intact Stability Criteria (SGISC). This paper systematically reviews the methodological evolution of dynamic stability assessments for WAPS ships under extreme and damaged conditions. By introducing a "Hierarchy of Evidence" evaluation framework, this study delineates the applicability boundaries of aerodynamic Reduced-Order Models (ROM), extended 3/4-DOF maneuvering equations, and 6-DOF time-domain hybrid architectures, defining the role of high-fidelity CFD-VPP in establishing calibration benchmarks. The review also discusses the damping distortion mechanisms induced by multiphase flow sloshing under damaged conditions. Synthesized findings indicate that transitioning towards a 6-DOF time-domain coupled architecture provides clear advantages for capturing unsteady aerodynamic hysteresis and nonlinear interference. Meanwhile, surrogate models, such as Physics-Informed Neural Networks (PINNs), offer a potential pathway to mitigate the computational demands associated with long-term extreme value extrapolations. Ultimately, this review provides a methodological reference for the high-fidelity assessment of WAPS and the development of Digital Twin systems.