Engineering

Abstract: This study proposes a reproducible exploratory framework to link long-term territorial development with electricity demand in data-scarce contexts, and applies it to Ecuador’s Costa region. The pipeline combines three commonly available input streams: periodic census microdata, an official demand series, and macroeconomic aggregates. Socioeconomic heterogeneity across five non-uniform census rounds (1974, 1982, 1990, 2001, 2010) is summarized through Principal Component Analysis (PCA), and territorial indicators are projected to the demand horizon using low-order polynomial functions. Eleven regression specifications are compared on a log-transformed demand variable, and a rollingorigin backtesting scheme plus a 2020–2024 holdout are used for validation. The selected Trend OLS log model attains R2 = 0.551 and MAPE = 6.08%, and projects a regional demand of approximately 6,940 MW by 2050, equivalent to a compound annual growth rate of 3.45%. Beyond the Ecuadorian case, the results show that transparent, low-data pipelines based on harmonized census information, macroeconomic drivers and simple regression models can provide defensible medium- and long-term demand signals for planners in other emerging economies with limited high-frequency data.

Abstract: Events such as landslides and slope failures happen suddenly and can be catastrophic. To predict the onset of such events, as well as the flow and final deposition of the material, engineers make use of numerical modeling techniques. These events are associated with large deformation and mesh-based methods, such as the finite element method, are not capable of modeling them due to mesh distortion. The material point method (MPM) is a particle-based continuum method capable of modeling large deformation and material flow. In this paper, MPM is used to model the sudden and dynamic flow of material by modeling the collapse and runout of a non-cohesive sand column. The results from two- and three-dimensional models are compared to experiments, showing that MPM accurately predicts the free-surface profile of the material during collapse. Furthermore, the model accurately predicts the runout distance with an error of less than 5%.

Abstract: To alleviate the shortage of natural river sand and promote the utilization of aeolian sand, concrete was prepared by replacing river sand with Taklamakan Desert aeolian sand at different mass ratios. The effects of replacement ratio and curing age on compressive strength and microstructure were investigated using compressive strength tests, SEM, and EDS. A quadratic regression model was established by response surface methodology using replacement ratio, curing age, and Ca/Si ratio as variables. The results showed that compressive strength first increased and then decreased with increasing aeolian sand content, with the 20% replacement group achieving the highest strength. Strength increased with curing age, but the growth rate slowed after 28 days. SEM and EDS results indicated that suitable aeolian sand content promoted hydration product formation and matrix densification, whereas excessive replacement increased pores and interfacial defects. The Ca/Si ratio generally increased with curing age. The model showed good fitting accuracy, with R² = 0.9970, providing a reference for strength prediction and mix design optimization of aeolian sand concrete. Keywords: aeolian sand concrete; compressive strength; microstructure; Ca/Si ratio; response surface methodology

Abstract: The current work uses Iterated Fission Probability (IFP) routine that was recently implemented in OpenMC to calculate reactor kinetics parameters. IFP is calculated from the product of the multiplication factors tracked across the L+1 generations of fission progenies. Since IFP is an excellent estimator of adjoint flux, it is able to calculate Λ_eff , β_eff and β_i of the reactor. OpenMC calculation of the reactor itself has k_eff = 1.01687 with an effective mean neutron generation time, Λ_eff = 44.82 μs. The effective delayed neutron fraction, β_eff that we get is 0.007235 or 723.5 pcm. Other calculations of β_eff using prompt methods for reactors with similar designs gave us values between 724 pcm to 752 pcm. Our own calculations using the prompt method in OpenMC gave us an effective delayed neutron fraction of 734.1 pcm. The group β_i that we obtain is 24 pcm, 131.4 pcm,124.1 pcm, 284.0 pcm, 112.7 pcm and 47.4 pcm respectively. If we strip away the influences of β_eff on β_i , by looking at only the abundances of each delayed neutron group, a_i ; we are able to see that the a_i is similar to the abundances of just ²³⁵U in the six group abundances of ENDF/B-VIII.0 evaluated cross section library. When we adopt a different evaluated cross section library in OpenMC, changes in β_i is due to the different β_eff and λ_i adopted in these libraries.

Abstract: This paper presents the complete design and development of a dried whole blood cartridge designed for point-of-care (POC) clinical diagnostics. The system integrates a near-infrared (NIR) spectroscopy sensor with a disposable multilayer paper cartridge capable of collecting and analyzing small, controlled volumes of capillary blood (20 μL). The work emphasizes a technical and iterative design approach that combines product design with both additive and subtractive prototyping, supported by experimental validation. The development process involved multiple design iterations focusing on fluid transport, capillary dynamics, usability, and optical integration. Several materials and manufacturing processes, such as CNC (Computer Numerical Control) machining and Material Jetting (MJ), were explored to optimize channel geometry and flow behavior. Experimental results guided successive refinements, leading to a cartridge configuration that ensures efficient capillary action, minimal coagulation, and consistent optical alignment with the sensor’s analysis zone. The study underscores the importance of an integrated engineering approach that unites design methodology, material selection, and manufacturing processes to achieve a reliable and reproducible cartridge for point-of-care blood diagnostics. It demonstrates how iterative design, supported by experimenal testing, can effectively bridge the gap between experimental prototyping and practical implementation in medical device development.

Abstract: This paper introduces bending anisotropy of gun drill rods, caused by asymmetric cross-section, into chatter stability analysis. A dynamic model considering different stiffnesses along the two principal inertia axes is established. The Galerkin method and semi-discretization method are used to solve the governing equations and generate stability lobe diagrams. Parameter sensitivity analysis shows that drill rod length and material damping coefficient are high-sensitivity parameters, while coolant hole size and eccentric position are low-sensitivity ones. The results reveal the mechanism of bending anisotropy on stability and provide theoretical guidance for chatter suppression in deep-hole machining.

Abstract: This paper presents a method for continuously optimizing the turn-on and turn-off angles of a switched reluctance generator (SRG) operating in single-pulse mode and connected to an asymmetric bridge converter. The optimal angles are defined as those that minimize total SRG loss while ensuring accurate tracking of the terminal voltage reference. The Pearson correlation coefficient between SRG loss and selected SRG variables was calculated, with the highest correlation found for the average value of all phase currents. Therefore, the average phase current was selected as the variable to be minimized in a perturb-and-observe (P&O) method used to determine the optimal turn-on angle at a given operating point. The turn-off angle was calculated to maintain the terminal voltage at its reference value. The method was validated using both a conventional SRG simulation model and an advanced model that accounts for mutual coupling, iron losses, and remanent magnetism, and was further verified experimentally on an 8/6 SRG rated at 1.1 kW under various load conditions, terminal voltages, and rotor speeds.

Abstract: This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes.

Abstract: Determining the compatible prestress and geometry under self-weight constitutes a key challenge in the form-finding of cable-truss structures. To overcome the limitations of experience-dependent trial methods and enhance computational efficiency, this paper proposes an automated and integrated methodology by synergistically combining a simplified mechanical model with an improved Particle Swarm Optimization (PSO) algorithm. The core of the method lies in formulating the form-finding process as an optimization problem, where the horizontal inclination angles of the lower chord cables serve as the design variables for all radial cable-truss frames. To efficiently solve this high-dimensional optimization problem, an improved PSO algorithm, which introduces logistic chaotic mapping for particle initialization and a mutation operator within the iterative loop. Ablation studies confirm the individual contribution of each algorithmic enhancement. The algorithm intelligently searches for the optimal angle set, thereby simultaneously resolving the prestress and geometry. The proposed approach is rigorously validated through two representative numerical examples: a circular Type I and an elliptical Type II cable-truss, considering both cases with and without self-weight. The results demonstrate that the improved PSO-based solution achieves prestress distributions and nodal coordinates in excellent agreement with established benchmark data. More importantly, it attains this high precision with significantly reduced computational cost in terms of particle swarm size and iteration number. In conclusion, this improved PSO‑based approach provides an efficient, accurate, and automated tool for the integrated prestress‑geometry design of cable‑truss structures, demonstrating strong potential for practical engineering application.

Abstract: We conduct a theoretical analysis on the diffusiophoretic motion of a dielectric droplet in a cylindrical pore in the presence of an induced diffusion potential, such as in the NaCl electrolyte solution. The fundamental electrokinetic governing equations are solved using a patched pseudo-spectral method based on Chebyshev polynomials, coupled with a geometric mapping scheme to handle the irregular solution domain. The impact of boundary confinement effect on droplet mobility is examined in detail. Interesting electrokinetic phenomena are found in this work, such as mobility reversal in narrow cylindrical pores with the droplet moving against the direction expected based on the classical Coulomb electrostatic law due to the strong boundary confinement effect. Two critical points of κa are found, where κ is the electrolyte strength and a is the droplet radius. The spinning orientation on the droplet surface changes each time past them. The profound boundary confinement effect, both electrostatically and hydrodynamically, is responsible for these peculiar phenomena. The results presented here has direct applications in microfluidic and nanofluidic operations as well as drug delivery applications.

Abstract: Adaptive Sidelobe Cancellation (ASLC) is a core technology for modern radar systems to suppress active sidelobe jamming. From the perspective of disrupting the ASLC system’s ability to stably track the jamming direction, this paper proposes a distributed jamming method based on random phase perturbation. The method employs two spatially separated jamming sources that simultaneously transmit coherent signals. By actively applying controllable random jumps to the relative phase between the two sources, the equivalent wavefront direction of the synthesized signal at the radar receiver changes rapidly, forming a non-stationary jamming that destroys the null-tracking capability of ASLC. An analytical model of the ASLC cancellation ratio under random phase perturbation is established, with a focus on analyzing the effects of time synchronization accuracy and phase synchronization accuracy on jamming performance. Monte Carlo simulation results show that the proposed method can reduce the average ASLC cancellation ratio from 26.80 dB to 19.73 dB (a decrease of 7.07 dB). This study provides a theoretical basis and parameter design references for the engineering implementation of distributed cooperative jamming.

Abstract: The high cost and complexity of Industry 4.0 laboratory infrastructure limit the adoption of Digital Twin concepts in engineering education. This paper proposes a low-cost Digital Twin framework for sustainable manufacturing education integrating SAP NetWeaver, Node-RED, and AI-based decision support. The framework adopts a layered architecture that connects PLC-based simulation, IoT middleware, enterprise resource planning systems, and intelligent decision-making components. Node-RED enables real-time data exchange, while SAP NetWeaver provides enterprise-level integration through OData services. An AI module supports decision-making for production and inventory management. The proposed framework is implemented as a functional prototype, demonstrating end-to-end integration without requiring physical manufacturing equipment. Competency-based mapping aligns the framework with Industry 4.0 engineering skills, supporting its use in academic environments. A sustainability assessment highlights reductions in infrastructure cost, energy consumption, and resource usage compared to traditional laboratory approaches. The results indicate that the framework provides a scalable and accessible solution for teaching Digital Twin concepts, contributing to sustainable engineering education in resource-constrained contexts.

Abstract: The integration of satellite and terrestrial networks within the same spectrum is a key enabler for extending mobile connectivity in future communication systems. In this context, the Direct Connectivity between Mobile Satellite Service and International Mobile Telecommunications user equipment (DC-MSS-IMT) paradigm, currently under study within the International Telecommunication Union [1], foresees the use of terrestrial IMT frequency bands by satellite systems to directly serve conventional mobile devices. This paper presents an experimental study to assess the coexistence between a terrestrial 5G-NR receiver and a co-channel interfering signal representative of a Low Earth Orbit (LEO) satellite downlink. A controlled laboratory setup in conducted configuration was implemented to ensure repeatability and accurate control of interference conditions. Measurements were performed over four carrier frequencies representative of IMT bands (763 MHz, 1482 MHz, 2150 MHz, and 2635 MHz) [2], considering different traffic load conditions (100% and 50%) and Doppler shifts associated with satellite motion. The interference impact was evaluated in terms of receiver desensitization, defined as the increase in the total received power relative to the baseline noise level [3]. The results show that a 1 dB desensitization threshold is consistently reached when the interfering signal power is approximately 5–6 dB below the receiver noise floor, corresponding to an interference-to-noise ratio (I/N) of about −6 dB. This behavior is observed across all tested frequency bands, traffic conditions, and Doppler scenarios, indicating limited sensitivity to frequency offsets within the considered range. The findings confirm the validity of commonly adopted coexistence criteria and provide experimentally derived reference values to support ongoing regulatory and technical studies on spectrum sharing between satellite and terrestrial IMT systems.

Abstract: This paper investigates how software configuration, hardware type, user background and context of use influence the usability of Virtual Reality (VR) systems in engineering product development. A VR usability assessment approach that combines task-based questionnaires, the System Usability Scale (SUS) and the NASA-TLX questionnaire was evaluated systematically across six experiments involving students, junior engineers and senior engineers in academic and industrial settings. The results demonstrate that user background and task context are at least as signifi-cant as the underlying hardware or software in influencing perceived usability and acceptance. Standalone headsets achieve higher usability scores with inexperienced users, whereas PC-based systems are still necessary for high-precision engineering tasks. Professional engineers primarily evaluate VR in terms of workflow integration, precision and return on investment, whereas students focus more on novelty and the interaction experience. Based on these findings, practical design recommendations have been derived for se-lecting a VR system, adapting interaction concepts, and implementing VR in product development processes. The study highlights that VR should not be deployed as a one-size-fits-all solution, but rather as a tool that is both context-specific and us-er-centered. It also demonstrates how systematic, iterative usability evaluation can directly support the successful industrial integration of VR technologies.

Abstract: As a third-generation semiconductor material, indium phosphide (InP) exhibits complex anisotropic etching characteristics, showing significantly varying etching morphologies under different temperature, concentration, and surfactant conditions. This complexity poses challenges in controlling the etching evolution process and predicting its three-dimensional structures. To address the simulation of InP etching structures and surface morphology, this study first establishes an atomic model of the InP etching system and analyzes how different atomic structures influence crystal plane etching rates. Subsequently, based on the microscopic activation energy theory, we propose an atomic removal determination function (InP-RPF) for InP etching substrates, numerically elucidating the relationship between macroscopic crystal plane etching rates and microscopic atomic removal probabilities. Furthermore, we develop an evolutionary Monte Carlo etching system model (InP-EMC), employing evolutionary algorithms to continuously optimize the energy parameters in the InP-RPF function, thereby adjusting the removal probabilities of various atomic types on the substrate and validating the simulated etching rates. Experimental comparisons demonstrate that the InP-EMC model accurately constrains atomic removal probabilities using limited crystal plane etching rate data, achieving simulation accuracy exceeding 90% for full-crystal-plane etching rates, mask etching structures, and surface morphology characteristics.

Abstract: Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re) and its laminar separation bubbles significantly complicate aerodynamic predictions. This study provides an experimental and numerical data for a custom-designed airfoil tested at Re = 68k–159k and angles of attack α = 0°–17.5°. Lift, drag, and pressure coefficient (C_p) distributions were measured experimentally. The XFOIL, the fully turbulent 3D k-ω SST, and the γ-Re_θ transition RANS models were validated against the experimental data using multiple quantitative metrics. The γ-Re_θ model demonstrated superior performance, achieving lift Maximum Absolute Percent Error of 1.6–3.4%, near-zero bias, and coefficient of determination > 0.99. It accurately captured the laminar separation bubble pressure plateau at mid-chord, with mean gross-averaged C_p percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. In contrast, the k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL showed poor reliability in transitional flow regimes. Sensitivity analyses confirmed the robustness of the γ-Re_θ model across the tested Re and α ranges. The generated experimental dataset, combined with the validated transition-sensitive RANS approach, provides a strong foundation for low-Re airfoil and DAWT diffuser design. Future work should extend experimental measurement below Re = 5x10⁴ and above 2x10⁵, including post-stall conditions and system level designing.

Abstract: The recycling of polyethylene terephthalate (PET) is gaining increasing importance, as it enables the conversion of plastic waste into valuable raw materials and contributes to a circular economy. Recent research has primarily focused on optimizing the depolymerization step of PET glycolysis, while downstream processes often overlooking the at least equally critical downstream steps in recovering the monomer bis(2-hydroxyethyl) terephthalate (BHET). The implementation of a water‑free PET glycolysis process eliminates challenges related to internal solvent and homogeneous catalyst recycling that commonly occur in conventional processes. This study therefore focuses on BHET crystallization and filtration as key downstream unit operations. Two nucleation strategies, gassing and seeding, were investigated and compared with experiments without a nucleation strategy. The aim was to achieve reproducible process control during crystallization and to obtain crystals with good filterability, which is essential for efficient washing and high product purity. Experiments without a nucleation strategy showed poor reproducibility. In contrast, gassing and seeding improved crystallization control, particularly regarding nucleation temperature and relative crystallization yield. However, these strategies also resulted in significantly prolonged filtration times due to differences in filter cake properties. The anisotropic crystals exhibited a broad particle size distribution with a high fraction of fine particles, leading to small and heterogeneous pores in the filter cake. Limited crystal growth was identified as the main cause of the unfavorable filtration behavior.

Abstract: This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices (imageability, enclosure, human scale, greenness, and walking index) were quantified to assess their influence on pedestrian experience. Street width was explicitly incorporated as a morphological variable to examine its relationship with perceptual qualities using spearman correlation analysis and visual trend analysis using Pearson correlation. The results reveal consistent relationships between visual composition and perceptual outcomes, particularly strong associations between imageability, enclosure, and vegetation structure, as well as trade-offs between enclosure and sky visibility. In contrast, variables such as human scale and walking index show weak or negligible associations with street width, suggesting that pedestrian presence and activity patterns in heritage contexts are more strongly influenced by landscape elements, water features, and spatial continuity than by dimensional factors alone. Findings highlight how urban renewal strategies, such as streetscape enhancement and cultural preservation, shape pedestrian movement and spatial perception. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a robust alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasizing morphological continuity, human-scale design, and green infrastructure as critical determinants of walkability. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which affect pedestrian comfort and use of space. These findings support evidence-based policy and design strategies for optimizing historic urban streetscapes, with implications for balancing heritage conservation and modern pedestrian needs. Future research may refine perceptual metrics and extend the approach across diverse urban contexts.

Abstract: The Sultanate of Oman enjoys plenty of solar energy and wind energy; both have been exploited successfully in the country. However, geothermal energy has not been exploited yet in Oman. This natural heat source deserves more studies to assess its technical potential and economic feasibility compared to other electricity generation technologies in Oman. The current study fills this gap by presenting a techno-economic assessment (TEA) of a small 30-MW geothermal power plant in Oman, operating on a binary (two-fluid) cycle, with a drilling depth of 2 km. The analysis was performed using the renowned software tool SAM (System Advisor Model) of the United States National Renewable Energy Laboratory (NREL). The current results suggest a levelized cost of energy (LCOE) of 8.68 cents/kWh (0.0868 US$/kWh) or 33.4 baisa/kWh (0.0334 OMR/kWh). When compared with electricity tariff or solar photovoltaic (PV) power purchase agreement (PPA) rates in Oman, it was found that geothermal-based electricity is too expensive. Furthermore, the estimated geothermal LCOE is more than three times the LCOE value of self-owned photovoltaic (PV) power systems in Oman, which is around 10 baisa/kWh (0.010 OMR/kWh). The estimated first-year electricity generation for the geothermal power plant model is 261.268 GWh/year, leading to a specific electricity generation of 8,709 kWh/kW/year. This is about five times the specific power generation from PV power plants. The study is augmented by sensitivity analyses and regression models to help understand the impact of multiple input parameters. The study provides novel results regarding decision-making for geothermal power investment in Oman.

Abstract: The rapid adoption of Wide Band Gap (WBG) and Ultra-Wide Band Gap (UWBG) semiconductor technologies, most notably Silicon Carbide (SiC) and Gallium Nitride (GaN), is reshaping wafer-level electrical testing beyond conventional silicon-based probing infrastructures.[1,2] Modern SiC devices require blocking voltage verification in the 650 V–3.3 kV range, extending beyond 6.5 kV, while GaN HEMTs operate with voltage slew rates exceeding 50–150 V/ns and current slew rates above 1–5 kA/µs. Un-der these conditions, probe cards evolve from passive interconnects into multi-physics systems coupling electrical, thermal, and mechanical domains.[3,4] Vertical MEMS probe card architectures enable high contact density, per-contact currents of 2–10 A (aggregated >1–3 kA), and loop inductance in the single-digit nanohenry range. This work analyzes probe-to-wafer contact physics, including constriction resistance (10–50mΩ) and wear under high current (>10⁵ A/cm²) and high-frequency conditions.[4] Electro-thermal limitations are discussed with focus on insulation integrity, partial discharge, di/dt-induced overshoot, and localized heating (>100–200 °C).[5,6,7] Emerging high-voltage solutions include ceramic insulation, controlled atmospheres, and on-board sensing. Wafer-level testing combines full-wafer screening with burn-in-like stress methodologies, where body diode characterization enables early defect detection in SiC devices. These results highlight the critical role of probe cards in WBG manufacturability and test reliability