Abstract: Accurate modeling of outdoor Wi-Fi propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity insight into multipath propagation but suffer from high computational cost and limited scalability in large 3D environments. This work investigates a hybrid approach that combines MATLAB-based ray-tracing simulations with Machine Learning to enable scalable Wi-Fi~7 network analysis. A large dataset is generated over a realistic simulated university campus, covering multiple frequency bands (2.4, 5, and 6~GHz), transmit power levels, and ray-tracing configurations with reflections and diffractions. Several regression models are evaluated, with emphasis on transformer-based architectures. Results show that the FT-Transformer accurately approximates ray-tracing outputs while reducing inference time from months to minutes. The proposed framework enables fast surrogate modeling of radio propagation and supports network planning and digital twin applications.

Abstract: Copper-aluminum layered composites offer a promising combination of high conductivity, light weight, and cost-effectiveness, making them attractive for applications in electric vehicles, electronics, and power transmission. However, achieving reliable interfacial bonding while avoiding excessive work hardening and brittle intermetallic formation remains a significant challenge. In this study, a Cu18150/Al1060/Cu18150 trilayer composite was fabricated through a three-stage high-temperature oxygen-free rolling process. Subsequently, the produced composite was subjected to annealing treatments to systematically investigate the effects of rolling passes, annealing temperature/time on interfacial evolution and mechanical behavior. Results indicate that rolling passes primarily influence interfacial topography and defect distribution. Fewer passes lead to wavy, mechanically bonded interfaces, while more passes improve flatness but reduce intermetallic continuity. Annealing temperature critically governs diffusion kinetics; temperatures up to 400 °C promote the formation of a uniform Al2Cu layer, whereas 450 °C accelerates the growth of brittle Al4Cu9, thickening the intermetallic layer to 18 μm and compromising toughness. Annealing duration further modulates diffusion mechanisms, with short-term (0.5 h) treatments favoring defect-assisted diffusion, resulting in a porous, rapidly thickened layer. In contrast, longer annealing (≥1 h) shifts toward lattice diffusion, which densifies the interface but risks excessive brittle phase formation if prolonged. Mechanical performance evolves accordingly; as-rolled strength increases with the number of rolling passes, but at the expense of ductility. Annealing transforms bonding from a mechanical to a metallurgical condition, shifting fracture from delamination to collaborative failure. The identified optimal process, single-pass rolling followed by annealing at 420°C for 1 hour, yields a balanced interfacial structure of Al2Cu, AlCu, and Al4Cu9 phases, achieving a tensile strength of 258.9 MPa and an elongation of 28.2%, thereby satisfying the target performance criteria (≥220 MPa and ≥20%).

Abstract: The global acceleration in electric vehicle (EV) adoption is projected to result in a substantial volume of spent traction motors reaching end of life EoL), especially in emerging economies. Addressing this challenge, the present study develops a comprehensive evaluation and decision-making framework to support the remanufacturing of EoL traction motors within Ghana’s circular economy context. The methodology integrates RUL prediction algorithms, a Multi-Stage Testing Protocol (MSTP), remanufacturability scoring using hybrid Multi-Criteria Decision Analysis (MCDA), and safe dismantling procedures aligned with Ghana’s EPA Act 917 and LI 2250. Tools such as vision-based screw detection, robotic disassembly path modelling, and non-destructive magnet removal are incorporated to ensure technical feasibility and operator safety. Results demonstrate the effectiveness of predictive models in estimating degradation patterns and confirm the technical viability of semi-automated disassembly workflows. The developed remanufacturing feasibility scoring tool enables objective selection of candidate motors for reuse, factoring performance, and environmental impact. This work offers a replicable, data-driven framework that strengthens local remanufacturing infrastructure, reduces reliance on critical raw materials, and advances sustainable motor lifecycle management in low and middle-income countries.

Abstract: Maritime safety is a crucial aspect in busy and complex shipping lanes, particularly in strait areas that are prone to accidents due to high vessel traffic and dynamic envi-ronmental conditions. This study aims to calculate a maritime safety index by consid-ering various factors, including vessel characteristics, ship encounter conditions, oper-ational time parameters, and oceanographic conditions such as currents and waves. The data used consist of questionnaires, AIS data, and oceanographic information, collected over a one-month period at three-hour intervals. The case study focuses on the Bali Strait and the Lombok Strait, with spatial segmentation into grid cells to sup-port spatial analysis. The safety index is calculated using two models: Model I com-bines vessel and encounter characteristics with temporal parameters, while Model II incorporates oceanographic factors into the assessment. Following the index calcula-tion, multivariate analysis conducted to identify the key factors that significantly in-fluence maritime safety levels. The results show that navigation risks in both straits are mainly influenced by vessel traffic, sailing hours, days of the week, and environmental conditions. In the Bali Strait, the highest risks occur near Ketapang and Gilimanuk Ports, while in the Lombok Strait, Padangbai, Lembar, and the ALKI II route show ele-vated risks. Multivariate analysis reveals that longer vessels, higher speeds, and dy-namic sea conditions dominate in Lombok, whereas older vessels and closer spacing are more critical in Bali.

Abstract: Commercial supersonic passenger transport has been absent from global aviation for more than two decades, largely due to regulatory, geographic, and economic constraints. While renewed interest in supersonic travel has emerged with advances in aircraft design, there remains a lack of scalable methods for assessing where such operations could be viable. This study evaluates supersonic feasibility at the route level using a data-driven framework that integrates engineering, regulation, and economics. A global dataset comprising 435 city-pair routes was constructed using aircraft performance estimates, great-circle routing, over-water routing fractions, and demand indicators derived from population and gross domestic product data. Routes were labeled as feasible or unfeasible based on domain-informed criteria, and supervised machine-learning models were trained to learn a continuous feasibility score between 0 and 1. A Decision Tree classifier was used to extract interpretable feasibility rules, while an Extreme Gradient Boosting (XGBoost) classifier provided predictive performance. Model behavior was analyzed using SHapley Additive exPlanations (SHAP). The results show that over-water routing fraction is the dominant determinant of feasibility, followed by time savings and great-circle distance, with demand contributing in marginal cases. The framework produces a ranked set of candidate routes as well as a predictive engine for future routes.

Abstract: This study investigated the degradation and contamination behavior of 41 real-world operational Marine Diesel Fuel samples, conforming to ELOT ISO 8217:2024 (DFA category). Samples were sourced directly from land-based supply tanks. To assess fuel degradation, a comprehensive suite of parameters was evaluated, including fuel characteristics such as viscosity and density. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was employed for elemental analysis to determine the content of wear and other metallic contaminants. Elevated concentrations of various metals were detected, suggesting potential leaching from system components within the storage infrastructure. Notable elemental concentrations included Iron (Fe up to 1.38 mg/kg), Copper (Cu up to 0.401 mg/kg), Lead (Pb up to 0.358 mg/kg), Aluminum (Al up to 0.218 mg/kg), Zinc (Zn up to 1.331 mg/kg), Nickel (Ni up to 0.172 mg/kg), Calcium (Ca up to 8.054 mg/kg), Sodium (Na up to 0.332 mg/kg), Phosphorous (P up to 0.602 mg/kg), and Silicon (Si up to 8.249 mg/kg). The presence of these contaminants in marine fuels, if bunkered, poses a significant risk of impaired engine performance, including injector fouling and ash formation. Critically, this study suggests that FAME content is not the primary driver of the observed oxidation and subsequent metallic degradation.

Abstract: Fire resistance is a critical aspect of passive fire protection, particularly in door systems that must maintain integrity under extreme conditions. This paper presents the thermal and structural performance of a single-leaf sandwich fire door, with the goal of improving its fire resistance through numerical optimization. An initial numerical assessment identified the door frame as the thermally weakest component, guiding the subsequent optimization process. Then, a one-way coupled transient thermal–structural Finite Element Method (FEM) analysis was performed using ANSYS Mechanical to evaluate the influence of frame material, frame geometry, and insulation type and placement on the door–frame assembly when exposed to fire. Results show that the frame material plays a decisive role where aluminum alloys performed poorly, whereas wooden frames significantly reduced temperatures in both the door and frame by approximately 55% relative to the original configuration. Additional improvements were achieved by increasing frame thickness and placing rock wool within the thermal break, resulting in temperature reductions of 58.3% in the door and 57.3% in the frame. However, these thermal improvements had limited impact on structural deformation, which remained nearly unchanged.

Abstract:

Accurately estimating actual evapotranspiration (ETa) is essential for sustainable water management, particularly in semi-arid regions. Although the SAFER algorithm provides a practical remote sensing-based approach, its sensitivity to parameter settings and reduced performance during dry periods limit its reliability. This study develops four parametric ETa models—two linear (LM-I, LM-II) and two nonlinear (NLM-I, NLM-II)—and recalibrates SAFER coefficients via a simulation/optimization (S/O) approach. Models were evaluated using Landsat-8 data (LST, NDVI, αₛ) and reference evapotranspiration (ETo), and compared with machine learning methods: Random Forest (RF), Bagged Trees (BT), Support Vector Machines (SVM), and Generalized Additive Models (GAM). Results indicate that nonlinear models better capture the physical behavior of ET processes and outperform linear models across key metrics. In particular, the NLM-II model achieved R² = 0.8295 and RMSE = 0.4913 on the test set, surpassing SAFER (R² = 0.8195, RMSE ≈ 0.5713), LM-II, and the best soft computing model, BT (R² = 0.8137, RMSE = 0.5084). Its physically grounded structure ensures stable, interpretable predictions that accurately reflect water–energy interactions and seasonal dynamics. These findings demonstrate that compact, physically based nonlinear parametric models provide a robust, operationally practical solution for ETa estimation under sparse Landsat-based datasets, outperforming both linear and black-box machine learning approaches.

Abstract: Development of AI-driven automated driving functions requires vast amounts of diverse, high-quality data to ensure road safety and reliability. However, manual collection of real-world data and creation of 3D environments is costly, time-consuming, and hard to scale. Most automatic environment generation methods still rely heavily on manual effort, and only a few are tailored for Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) training and validation. We propose an automated generative framework that learns real-world features to reconstruct realistic 3D environments from a road definition and two simple parameters for country and area type. Environment generation is structured into three modules - map-based data generation, semantic city generation, and final detailing. The overall framework is validated by training a perception network on a mixed set of real and synthetic data, validating it solely on real data, and comparing performance to assess the practical value of the environments we generated. By constructing a Pareto front over combinations of training set sizes and real-to-synthetic data ratios, we show that our synthetic data can replace up to 90% of real data without significant quality degradation. Our results demonstrate how multi-layered environment generation frameworks enable flexible and scalable data generation for perception tasks while incorporating ground-truth 3D environment data. This reduces reliance on costly field data and supports automated rapid scenario exploration for finding safety-critical edge cases.

Abstract: This paper focuses on the design of a lightweight antenna suitable for remote sensing applications aimed at the identification of buried objects from UAVs. The presented antenna structure is simple, lightweight, and allows for achieving a fractional bandwidth of nearly 100% with an excellent stability of the radiation pattern that exhibits minimal modification within the operating band of the antenna. We illustrate numerical simulations and measurements of an antenna prototype that validate the proposed approach.

Abstract: Simulator‑based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology‑independent model‑based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150‑hour undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low‑level reactive behaviors to high‑level deliberation—under a single formal model. A structured programming template ensures traceable, optimization‑free software synthesis, facilitating debugging and enabling self‑diagnosis of design flaws. The framework includes real‑time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and SLAM while avoiding dependency on specific middleware such as ROS~2. Results over three academic years indicate that students gain a deeper understanding of controller internals, and demonstrate improved ability to reason about system errors and their causes. The proposed environment thus offers an effective methodology for teaching end‑to‑end robot controller design through transparent, simulation‑driven digital twins.

Abstract: Hot-fluid injection in thermal enhanced oil recovery (TEOR) imposes temperature-driven volumetric strains that can substantially alter in-situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection with temperature contrasts of ΔT = 0-100 °C and reduced pumping rate. Results show that hydraulic fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75-100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions above and below the fracture and markedly elevated shear stresses that may promote natural-fracture activation. These findings demonstrate that thermo-poroelastic coupling can govern fracture stability, containment, and injectivity during thermal EOR, motivating temperature-aware geomechanical risk assessment and design for long-term hot-fluid injection operations.

Abstract: In this paper, the proposed work provided a comprehensive review of non-isolated, isolated and optimization techniques for Direct Current (DC) to DC converters. This survey work is focused on their application in solar Photovoltaic (PV) based renewable energy systems. An important reason for considering these converters is that, critical components in power electronics are used to efficiently manage energy conversion and distribution. The DC-DC converter is categorized as a non-isolated and isolated DC-DC converter. The non-isolated converters are used where a direct electrical connection between input and output is acceptable, which makes it simpler and more efficient. Also, isolated DC-DC converters provide an electrical partition between input and output in high-voltage applications or where noise isolation is necessary. Also, optimization methods are used to improve the performance and efficiency of DC-DC converters. These techniques maximize energy extraction and minimize the losses especially renewable energy systems to ensure optimal operation. Overall, the review provides the latest advancements in DC-DC converter and optimization strategies. The significant improvement is the efficiency and effectiveness of renewable energy systems.

Abstract: Suspended sediment concentration affects the erosion and deposition of estuaries and coastal zones, and affects channel construction and safety. Sediment settling velocity controls sediment transport and sedimentation processes, and is crucial for assessing sediment distribution, diffusion, and material transport. As an important means for the inversion study of sediment concentration in estuaries and coasts, remote sensing alone cannot establish a model of the nearshore suspended sediment concentra-tion field by inverting surface sediment. Based on the remote sensing inversion of surface sediment, this study, in combination with the vertical distribution calculation method of sediment concentration in estuaries, inversely deduced the sediment concentration patterns in the middle and bottom layers, and proposed a sediment settling velocity calculation formula considering turbulent shear and concentra-tion influence. The results show that the highest concentration of suspended sediment in the study area appears in the east of Guan River Estuary, which is characterized by a high concentration in the east and a low concentration in the west. At a low suspended sediment concentration, the settling velocity is positively correlated with the suspended sediment concentration. At a high suspended sediment con-centration, the two are negatively correlated. The method introduced in this study is simple and feasi-ble, and the results are stable and reliable. It can be effectively used to evaluate the suspended sediment concentration and sediment settling velocity in different research areas.

Abstract: This study investigates and formulates a structured framework to guide deci-sion-making in the remanufacturing of spent electric vehicle (EV) traction motors. With a projected increase in the number of motors reaching end-of-life, the study tack-les key challenges by introducing a data-driven, integrated remanufacturing approach. A predictive algorithm was developed to estimate Remaining Useful Life (RUL) from operational parameters, supported by exploratory data analysis (EDA) that highlight-ed strong inverse relationships between lifespan and stress-related variables such as mechanical load, vibration, and thermal exposure. Various machine learning models including random forest, gradient boosting, and support vector regression yielded moderate predictive performance (mean absolute error ≈ 9.0 km; R² ≈ 0.58), with long short-term memory (LSTM) networks outperforming others (error ≈ 9.1k km; R² ≈ 0.61). These results demonstrate the viability of using predictive analytics to inform reman-ufacturing decisions, contributing to circular economy principles through sustainable EV motor reuse.

Abstract: Hydrogen is a versatile energy carrier essential for decarbonizing hard-to-abate sectors and long-duration storage. This study presents a unified techno-economic comparison of major production pathways—grey/blue steam methane reforming, biomass gasification, thermochemical cycles, biological methods, and solar-powered electrolysis—using 2025 benchmarks. Focus is on a 100 kW off-grid PV-electrolyzer system with realistic assumptions (PV performance ratio 0.85, electrolyzer efficiency 70% LHV). In Iran's high-insolation regions (PSH ≥ 5.15 kWh/kWp/day), annual yields reach 3.2–3.4 tonnes H₂—55–60% higher than northern Europe—with round-trip efficiency of 23.8%. Solar electrolysis offers zero direct emissions and 51–55 kWh/kg H₂ consumption. Scaling to multi-MW coastal hybrids with renewable desalination projects LCOH of 3.0–4.0 USD/kg by 2030, positioning Iran as a competitive exporter. A reproducible model and phased roadmap provide actionable insights.

Abstract: Traditional reliability engineering paradigms, originally designed to prevent physical component failures, are facing a fundamental crisis when applied to today's soft-ware-intensive and autonomous systems. In critical domains like aerospace, the dom-inant risks no longer stem from the aleatory uncertainty of hardware breakdowns, but from the deep epistemic uncertainty inherent in complex systematic interactions and non-deterministic algorithms. This paper reviews the historical evolution of reliability engineering, tracing the progression through the Statistical, Physics-of-Failure, and Prognostics eras. It argues that while these failure-centric frameworks perfected the management of predictable risks, they are structurally inadequate for the "unknown unknowns" of modern complexity. To address this methodological vacuum, this study advocates for an imperative shift towards a fourth paradigm: the Resilience Era. Grounded in the principles of Safety-II, this approach redefines the engineering objec-tive from simply minimizing failure rates to ensuring mission success and functional endurance under uncertainty. The paper introduces Uncertainty Control (UC) as the strategic successor to Uncertainty Quantification (UQ), proposing that safety must be architected through behavioral constraints rather than prediction alone. Finally, the paper proposes a new professional identity for the practitioner: the system resilience architect, tasked with designing adaptive architectures that ensure safety in an era of incomplete knowledge.

Abstract: Mechanical coffee dryers have been widely adopted to reduce weather dependence, improve yield, and stabilize product quality. However, their operation is still energy-intensive and often suboptimal in terms of controlling the temperature, airflow and moisture content of the grains. In parallel, digital twin (DT) technology has emerged to virtually replicate complex processes and enable model-based monitoring, optimization, and control. This article presents a systematic review based on PRISMA on mechanical coffee dryers and their modeling and control strategies and the current and emerging use of digital twins in drying processes, including agricultural and food products with technological analogies to coffee. The results show a large amount of research on mathematical modeling, energy evaluation, and quality evaluation of mechanical coffee drying. Rapidly growing but still predominantly conceptual literature on digital twins for food processing and drying. Finally, only a small convergence between the two fields, with no fully realized digital twin for mechanical coffee dryers having yet been reported. This review found key gaps in the detection, data infrastructure, and development of hybrid physical-informed AI models. Finally, lines of research are proposed for mechanical coffee dryers enabled with digital twins, aimed at energy efficiency, product traceability and quality assurance.

Abstract: Non-line-of-sight (NLOS) propagation poses a significant challenge to achieving high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. Building on this foundation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to compensate for NLOS errors dynamically. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results demonstrate that the proposed classification model and positioning framework significantly outperform state-of-the-art methods, thereby providing a systematic solution for high-precision and robust UWB positioning in complex indoor environments.

Abstract: The growing demand for high-protein dairy products, driven by the expanding markets for infant formula and nutritional supplements, has led to a higher incorporation of milk protein ingredients like milk protein concentrate (MPC) and whey protein isolate (WPI) in dairy formulations. However, the effects of these protein additives on the thermal stability and sensory attributes of dairy products remain insufficiently studied. This research examines the influence of thermal processing (80 °C for 30 min) and protein fortification (MPC, WPI, and their combination) on the denaturation of whey proteins, the formation of volatile compounds, and the sensory characteristics of milk. Specifically, whole milk was fortified with MPC, WPI, and their combination at concentrations of 4% MPC, 4% WPI, and 2% MPC + 2% WPI, respectively, to evaluate the impact of different protein fortifications on these properties. Our findings reveal that heat treatment significantly promoted the denaturation of β-lactoglobulin and α-lactalbumin, with protein fortification playing a role in modulating these changes. Notably, lactoferrin exhibited matrix-dependent antioxidant behavior, meaning its antioxidant activity varied based on the protein composition and structure of the milk matrix, influencing its stability and function under different fortification conditions. Volatile profiling indicated that MPC enhanced the formation of sulfur-containing compounds and aldehydes, whereas WPI favored ketones and Maillard-derived volatiles. Sensory analysis revealed that heated WPI fortified samples exhibited stronger cooked and dairy fat aromas, while unfortified milk retained milky and grassy notes. Correlation analysis highlighted the mechanistic links between protein denaturation and lipid-derived compounds. These results emphasize that protein type and composition play crucial roles in flavor development. The strategic blending of MPC and WPI offers a practical approach to balancing volatile profiles and mitigating off-flavors, providing insights for the formulation of thermally stable, protein-fortified dairy products with optimized sensory quality.