Engineering

Abstract: Halo-gravity traction (HGT) is widely used as a preoperative treatment for severe pediatric spinal deformities by gradually applying traction forces through a cranial fixation system. While the clinical effectiveness of HGT has been extensively documented, the uncertainty associated with the structural performance and operational reliability of mobile HGT systems has received comparatively little attention. This study presents a coupled clinical–structural uncertainty quantification framework for the evaluation of a mobile halo-gravity traction wheelchair. Reduced-order structural and clinical response models are combined with Monte Carlo simulation, Latin Hypercube Sampling, bounded beta-distributed input variables, Polynomial Chaos Expansion, and Sobol sensitivity analysis to propagate uncertainty from patient, operational, and design parameters to structural response metrics. The framework evaluates structural integrity, stability, halo-pin loading, traction delivery, factors of safety, and representative clinical response measures. The probabilistic analysis demonstrates that the proposed design maintains substantial structural safety margins throughout the investigated uncertainty space, with no sampled realization producing a governing factor of safety below unity. The proposed framework provides an efficient methodology for uncertainty-informed design and evaluation of pediatric halo-gravity traction systems.

Abstract: This study numerically investigates the analogy between laser shock (LS) and projec-tile-based hypervelocity impact (HVI) for composite and hybrid spacecraft shielding materials and proposes a methodology for determining the equivalent LS parameters corresponding to a given HVI event. Numerical HVI models were developed in LS-DYNA and validated against published experimental data for two shielding con-figurations: (a) a CFRP bumper and (b) an Al/CFRP/Al/CFRP/Al hybrid shield, with emphasis on crater formation and damage morphology. An LS model was subse-quently calibrated through iterative adjustment of the pressure amplitude and pulse duration to reproduce the damage induced by HVI. The ablation-pressure scaling laws of Grün, Dautray, Pirri, and Phipps were then inverted to estimate the laser intensity and energy required for experimental implementation. Excellent agreement between the HVI and LS responses was achieved in terms of crater morphology, hole size, de-lamination, and damaged area. The predicted laser intensities (498–1424 GW/cm²) and energies (42–1049 J) fall within the validated range of the Grün and Phipps scaling laws and the capabilities of existing laser facilities, demonstrating that the proposed HVI–LS analogy is both physically consistent and experimentally feasible.

Abstract: The memristor, theorized as the fourth fundamental passive circuit element, has attracted intense interest for next-generation memory and neuromorphic computing. Gel-based memristors exploit ionic migration within hydrated polymer matrices to produce history-dependent conductance, offering mechanical flexibility, biocompatibility, and simple solution processing. Although magnetically assisted switching via the Lorentz force remains conceptually appealing, prior demonstrations required lithographically patterned electrodes and purified ionic liquids, which precluded low-cost, informal experimentation. The ORLOK (Organic Response-Linked Output Key) project addressed this limitation by realizing a volatile gel-electrolyte memristor fabricated entirely from commercial-grade materials. The active layer utilized a sodium-alginate gel doped with dissolved iron ions and sodium chloride, housed within a polymeric straw bounded by aluminum foil and carbon-felt electrodes. Hand-wound solenoid coils were used to generate perpendicular magnetic fields of approximately 5.3 mT (copper, 20 turns) and 1.7 mT (steel, 10 turns) to deflect mobile ionic trajectories. Current-voltage characterization using Arduino Due, the baseline device exhibited a quantifiable hysteresis loop with an integrated area of 0.000158 mA·V and a resistance coefficient of variation of 0.125, confirming genuine memristive behavior. Volatile memory testing achieved 99.07 +/- 0.2% read/write accuracy at 1.82 kHz, demonstrating memristive effects. Although the Lorentz force exerted on individual ions was negligible, collective perturbation from thermal and electrochemical factors within the mobile ionic population were sufficient to modulate conductivity. These results establish a proof-of-concept for neuromorphic, low-cost logic elements fabricated from accessible precursors.

Abstract: Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.

Abstract: A bidirectional DC-DC converter is the part that lets an electric vehicle both draw a fast charge and push power back during vehicle-to-grid (V2G) operation, so its control decides how well the DC link holds up when the load jumps or the power reverses. This paper sets out a full switching-level model of a synchronous half-bridge converter that sits between a 400 V battery pack and a 750 V DC link at 50 kW, and it controls that converter with a two-loop sliding-mode scheme: an inner sliding-mode current loop with a boundary layer to limit chatter, and an outer loop that holds the link voltage. The controller is written out term by term and compared against a conventional PI cascade on the same plant. Under a 40 to 100 percent load step the sliding-mode controller settles the link in 0.72 ms with a 2.66 percent dip, against 1.75 ms and 4.20 percent for the PI cascade, and its response barely changes when the inductor is 40 percent larger and its resistance 50 percent higher than the controller assumes, which is the invariance property sliding-mode control is meant to give. Power reverses from full discharge to full charge in 0.255 ms. A converter loss model puts peak efficiency at 98.44 percent near 19 kW and 97.79 percent at the rated 50 kW. The converter is then driven by a real-world charging-demand profile taken from 41,213 charging sessions recorded at the Newcastle Helix site, and it holds the DC link within 9.53 V across the day. Every figure comes from the accompanying code and can be regenerated.

Abstract: We propose a unified physics informed neural modelling framework for the simultaneous emulation of hydraulic states and water quality in water distribution systems (WDS). Building on recent advances in physics informed graph neural networks for hydraulics and spatio temporal PINN-GNN hybrids for chlorine transport, our approach (PINNWDSFQ) couples a physics aware Graph Neural Network (GNN) emulator of hydraulic variables (flows, heads, pressures) with a spatio temporal graph PINN for reactive advective transport. The hydraulic GNN employs message passing based on conservation laws to recover nodal/edge flow fields with a small number of layers and low computational cost, matching high fidelity simulator outputs while enabling fast inference across network sizes. The quality module discretizes pipes with virtual nodes and imposes PDE constraints (advection-reaction) using physics informed loss terms in a GNN encoder-processor-decoder, thereby preserving network topology and chemical reaction kinetics. Both modules are jointly trained in a multitask loss that includes supervised data terms (sparse sensor readings or EPANET surrogate labels) as well as physics constraints and consistency coupling (mass/flux conservation at nodes and flow-transport coupling). We show that the hybrid model obtains emulator level accuracy for hydraulics and state-of-the-art water quality RMSE/MAE (order 1e-3-1e-2 mg/L), scales to large networks with thousands of virtual nodes and runs orders of magnitude faster than traditional simulators on multiple benchmark networks. We discuss training strategies, handling transient/pump driven dynamics, sensor placement implications for calibration, and uncertainty quantification for operational use.

Abstract: Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data driven machine learning, and scientific machine learning within a target specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics informed neural networks, neural operators, and reduced order models. The synthesis shows that pressure and deformation modeling and seismic signal processing are comparatively mature, whereas prospective fault slip and seismicity forecasting remain limited by uncertain in situ stress, fault connectivity, CO₂ conditioned friction, monitoring detection limits, model discrepancy, and scarce cross site validation. We propose task appropriate metrics, an explicit validation ladder, and a staged, human supervised digital twin roadmap. Machine learning and physics informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty calibrated prospective evidence rather than algorithm novelty.

Abstract: Geological CO₂ storage is essential to pathways to carbon neutrality, but its deployment depends on trustworthy estimates of storage capacity, injectivity, trapping, reactive evolution, and containment. Digital rock physics can provide these estimates from X-ray and electron microscopy images, yet every result depends on image segmentation, which converts grayscale data into pore, mineral, fracture, and fluid phases. This review evaluates classical methods, machine learning, deep learning, transformers, and foundation models according to whether they support reliable storage decisions rather than image overlap scores alone. Evidence is synthesized from imaging of dry rocks, CO₂–brine experiments, multiscale studies of carbonates and shales, and analyses of fractured rocks. We introduce a framework with seven dimensions: class accuracy, boundary fidelity, topology, morphology, calibrated uncertainty, sensitivity of physical properties, and consequences for engineering decisions. The evidence shows that visually similar segmentations can yield substantially different predictions of permeability, connected porosity, residual trapping, reactive surface area, and leakage paths when errors occur at critical pore throats, fluid interfaces, or fractures. We therefore recommend selecting methods according to storage task and lithology, validating them on independent samples, propagating ensembles of plausible segmentations, using metrics that account for topology, and comparing predictions with laboratory measurements. The central message is simple: segmentation should be treated as both a measurement process and a form of risk control. Segmentation with auditable and quantified uncertainty can reduce false site acceptance or rejection, improve injection and monitoring design, and strengthen geological CO₂ storage as a technology for carbon neutrality.

Abstract: Large-area three-dimensional polyjet printing (3DPP) of continuous photopolymer laminates directly onto knitted fabrics provides a potential route toward technical textile applications beyond localized decorative features. This study investigated acrylic photosensitive resin (APR) laminates measuring 330 × 432 mm deposited onto a Nylon 66 interlock knitted fabric. 12- and 20-layer laminates were produced in one-sided and two-sided configurations, and their morphology and tensile behavior were evaluated in the wale and course directions before and after 20 h of accelerated xenon-arc weathering. Scanning electron microscopy showed that the APR formed a continuous external laminate while locally penetrating inter-yarn and inter-filament spaces within the knitted substrate. Before weathering, the 20-layer two-sided (20L-2S) architecture exhibited the highest maximum engineering stress, reaching 13.11 ± 0.37 MPa in the wale direction and 7.44 ± 0.28 MPa in the course direction. In contrast, the 20-layer one-sided (20L-1S) architecture retained substantially greater extensibility, reaching maximum engineering strains of 183.94 ± 2.45% and 217.34 ± 2.88% in the wale and course directions, respectively. Thus, two-sided printing maximized load-bearing capacity, whereas one-sided 20-layer printing provided a better balance between reinforcement and preservation of the large-strain response of the knitted substrate. Accelerated weathering reduced the maximum engineering stress of fabric-supported laminates by approximately 5.4–18.7% and maximum engineering strain by 2.1–21.1%, depending on architecture and loading direction. Unsupported APR laminates exhibited increases of approximately 59.9–77.2% in maximum engineering stress after exposure, without a corresponding increase in strain capacity. This response suggests exposure-induced stiffening or additional curing of the photopolymer. The results demonstrate that continuous 3DPP can produce mechanically integrated textile–photopolymer laminates with tunable strength–extensibility relationships relevant to flexible technical textile structures.

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating advanced data-driven models, reduced-order modeling techniques, and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative industrial casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications.

Abstract: This proof-of-concept work demonstrates a Bayesian Network (BN) augmentation of the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) Specific Operations Risk Assessment (SORA) 2.5. The proposed architecture maintains the deterministic decision structure of SORA while introducing BN only where operational uncertainty exists. Mission characteristics and observable operational conditions are used as evidence, whereas uncertain factors, such as the risk of ground and air disturbances, are represented probabilistically. Consequently, the Bayesian Network enhances the assessment by quantifying uncertainty while maintaining the official SORA outputs, including the Ground Risk Class (GRC), residual Air Risk Class (ARC), and Specific Assurance and Integrity Level (SAIL). An airframe inspection mission at Riga Airport is used to demonstrate the applicability of the proposed framework and to compare its outputs with those of the SORA 2.5. The results indicate that BN provides an enhanced representation of operational uncertainty and causal relationships while preserving the deterministic structure of SORA. The framework is intended as a probabilistic decision-support tool rather than a replacement for regulatory assessment; therefore, this article does not estimate the probability of regulatory approval or claim predictive superiority over SORA. The contribution is a transparent model specification and a proof-of-concept foundation for future validation using empirical operational data and multiple mission scenarios.

Abstract: This study investigates the fabricability, microstructures, mechanical and corrosion behavior of multi-material structure (MMS) composed of niobium alloy (NbZr1) and titanium alloy (Ti64) using a wire-arc directed energy deposition process. The microstructure of NbZr1 alloy primarily consisted of equiaxed grains oriented in the rolling direction, while the deposited Ti64 microstructure exhibited ‘banding’ morphology and a basket weave structure composed of α phase lamellae in a β matrix. The MMS interface revealed good metallurgical bonding and was free from defects such as cracks, pores and intermetallic phases. Niobium diffusion from the NbZr1 into the Ti64 alloy resulted in formation (β-Ti + Nb) solid solution which imparted strength to the MMS. The hardness testing showed that microhardness values follow the trend: NbZr1 substrate > MMS interface > Ti64 deposit. The NbZr1–Ti64 multi-material structure developed in this study exhibited a balanced combination of ductility (22.73% elongation) and moderate tensile strength (254.18 MPa), outperforming most reported NbZr1-Ti64 MMS studies. All tensile specimens failed in ductile manner on NbZr1 side. The MMS demonstrated superior corrosion resistance, exhibiting the lowest corrosion current density and corrosion rate compared to its individual counterparts.

Abstract: Tether-net systems represent a leading technology for the Active Debris Removal (ADR) of non-cooperative space objects; however, a fundamental operational gap persists between the discipline of Space Situational Awareness (SSA) and the mechanics of net-based capture. No existing framework propagates the observational uncertainties inherent to SSA data through net deployment dynamics to yield operationally actionable capture probability estimates. This paper addresses that gap by proposing an end-to-end SSA-to-ADR uncertainty propagation pipeline that treats tether-net debris capture as a probabilistic decision problem under observational uncertainty. Characterization of the full debris-state covariance from TLE-derived orbital uncertainties and photometric light-curve spin-state estimates; linear covariance propagation and Monte Carlo sampling through a lumped-parameter tether-net deployment dynamics model to the ejection epoch; and construction of a capture probability surface over the ejection parameter space that quantifies expected capture success as a function of observable data quality. The methodology is developed and exercised using Telkom-3, a defunct Indonesian satellite with concurrent photometric observations from Zimmerwald Observatory. The role of ground-based geographically distributed SSA networks in reducing state uncertainty and improving capture reliability is explicitly quantified. In the present implementation, the framework’s rotational-phase coupling is exercised in its conservative, uniform-phase limiting case; the architecture is designed to accommodate the spin-period-dependent phase concentration suggested by the photometric dataset as a direct extension. The proposed framework establishes a principled, transferable, and traceable methodology for probabilistic ADR mission planning conditioned on actual observational campaign quality.

Abstract: Bangladesh faces three interlocking water quality crises: naturally occurring arsenic in shallow groundwater, salinity intrusion across the coastal southwest, and severe industrial and domestic pollution of the rivers around Dhaka. Conventional monitoring is too sparse, slow, and costly to manage these problems at national scale. This paper critically reviews how artificial intelligence (AI), and machine learning (ML) in particular, is being used to assess and improve water quality in the specific hydrological, climatic, and institutional context of Bangladesh, and asks what is still missing. We synthesise recent studies on arsenic risk mapping, coastal salinity forecasting, river water-quality-index (WQI) prediction, satellite and Internet-of-Things monitoring, and treatment optimisation, and summarise the governing equations of the dominant methods: tree ensembles, recurrent and physics-informed neural networks, and explainable AI. Reported performance commonly exceeds 0.9 (R²) for WQI regression and 0.9 accuracy for arsenic classification, yet most studies remain single-problem, single-region, and weakly validated across the monsoon cycle. We argue that the decisive gap is integration, and propose a national framework linking Bangladesh's monitoring agencies, remote sensing, and low-cost sensors to interpretable models and concrete management actions. We critically examine the principal barriers (data scarcity, seasonality, transferability, interpretability, and deployment capacity) and offer a prioritised research and policy roadmap aligned with Sustainable Development Goal 6. The contribution is a Bangladesh-specific synthesis and an actionable, interpretability-centred roadmap.

Abstract: Public aircraft-engine certification data provide a reproducible basis for emissions modeling when proprietary combustor geometry, engine-cycle data, and detailed operating histories are unavailable. This study develops a physically constrained framework based on the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank for modeling gaseous emissions from civil aircraft gas-turbine engines over the landing-and-take-off (LTO) cycle. The analysis uses variables available directly from the ICAO Aircraft Engine Emissions Databank, together with derived LTO quantities, to construct combustor-aware reduced-order correlations for LTO-averaged emission indices of nitrogen oxides (NO_x), carbon monoxide (CO), and hydrocarbons (HC), denoted by EINO_xLTO, EICO_LTO, and EIHC_LTO, respectively. High-bypass-ratio (HBPR) turbofan engines are grouped by representative combustor technology, including conventional/single-annular combustor (Conventional/SAC), double-annular combustor (DAC), twin-annular premixing swirler (TAPS), Rolls–Royce TALON lean-burn combustor, low-emissions combustor (LEC), and Unknown categories. For each pollutant and combustor group, two-predictor quadratic response surfaces and power-law correlations are fitted using public engine-level predictors such as overall pressure ratio, bypass ratio, rated thrust, and total LTO fuel consumption. The results show that combustor-aware grouping substantially improves interpretability and that no single global predictor pair represents all pollutants or combustor technologies. For EINO_xLTO, overall pressure ratio appears in most selected predictor pairs, consistent with the pressure- and temperature-sensitive nature of NO_x formation. For EICO_LTO, robust group-specific correlations are obtained for several combustor classes, whereas EIHC_LTO is more sensitive to zero and near-zero values, making percentage-based errors and log-space power-law fits less reliable. The quadratic models generally provide stronger within-dataset descriptive fits, while the power-law models provide compact non-negative scaling relations when their errors are acceptable.The proposed framework is suitable for emissions-trend analysis, preliminary comparative assessment, and interpretation of public certification data, but it should not be used as a substitute for certification testing, detailed combustor simulation, or off-design mission-level prediction without additional validation.

Abstract: This study evaluates the integrated sustainability potential of WtE technology scenarios across eight South African metropolitan municipalities, which collectively represent 40.14% of the national population. The assessment considers energy generation, greenhouse gas (GHG) emission reduction, landfill space savings, waste diversion, financial feasibility, and technology readiness, in alignment with the Waste-to-Energy Roadmap, National Waste Management Strategy, and Integrated Resource Plan. Seven scenarios were evaluated for the period 2028–2050, incorporating recovery rates that increase at 7–8-year intervals to ensure sustainable feedstock supply. These include S1 (Business-as-Usual/Landfilling), S2 (Landfilling with upgraded landfill gas recovery, LFG+), S3 (LFG+ with anaerobic digestion (AD)), S4 (LFG+ with AD and incineration), S5 (LFG+ with AD and pyrolysis), S6 (LFG+ with AD and gasification), and S7 (LFG+ with AD and plasma gasification). An Integrated Sustainability Performance Index (ISPI), combining 12 normalized energy, environmental, financial, and technological indicators, identified S4 as the most sustainable scenario, with an average score of 0.688 ± 0.044 and first-place ranking in all municipalities. S3 ranked second at 0.520 ± 0.085, while S7 ranked last. S2–S3 remained financially attractive, whereas advanced thermal technologies benefited municipalities with larger waste volumes. Integrated WtE systems support South Africa’s energy transition and sustainable waste management.

Abstract: Dual-parameter photonic sensors often report a single detection limit without specifying its statistical definition or calibration transferability. This fully computational study evaluates these issues in a coupled interface-mode multilayer for simultaneous refractive-index (RI) and temperature sensing. The two resonances occur at 1517 and 1651 nm, with loaded Q factors of 232 and 208 and RI sensitivities of 90.71 and 329.41 nm/RIU. Modal-overlap analysis links the sensitivity matrix to distinct thermal-to-index response ratios. Bounded nonlinear calibration yields held-out errors of 2.43 × 10⁻⁵ RIU and 0.155°C. At 1% false alarm and 95% detection, device-specific probability-of-detection limits are 4.36 × 10⁻⁵ RIU and 0.123°C; transferring one nominal calibration across devices worsens them by factors of 81 and 203 under the linewidth-limited repeatability model. A sensitivity-matched trivial control gives statistically equivalent yield within the declared margin, bounding the topological claim to the studied operating point. Structural audits further show that the sensing modes are cavity-selected, the nanolaminate provides no net measured performance benefit, and hyperbolicity is unobservable at normal incidence. The results support bias-aware detection and calibration-transfer analysis as more informative benchmarks than nominal sensitivity alone.

Abstract: Inverse airfoil design, recovering a geometry that produces a prescribed surface pressureor edge-velocity distribution, is recast here as a single determined nonlinear root-find ratherthan an objective-function search. Parameterising the surface with Class-Shape-Transformation(CST) coefficients that enter the geometry linearly makes the geometric sensitivity of the surfaceexact, constant and design-independent. It also makes geometric design constraints, among themleading-edge radius, trailing-edge thickness and inscribed area, linear algebraic rows rather thannonlinear predicates. Appending the CST coefficients as unknowns to a coupled viscous/inviscidNewton solver (mfoil) therefore converts constrained shape optimisation into constrained root-finding: one square system, no outer loop, no surrogate. The architecture is validated with afalsifiable self-consistency test in which a known CST coefficient vector is recovered from itsown self-generated target to ∥A−A∗∥_∞= 2.75 ×10⁻¹¹ in six Newton iterations. The recoveredgeometry reproduces the reference section’s fully released (natural-transition) aerodynamiccoefficients to ∆c_l = 3.4 ×10⁻¹². An ablation matrix identifies the primary uniqueness guard forthe resulting square system: sensitivity-optimal (QR-pivoted) selection of target stations, notinitial-guess quality, separates recovery of the true design from clean convergence to a spuriousbut residual-zeroing root. Measured against a competently-tuned nested Levenberg–Marquardtbaseline under two independent fair-paired controls, the monolithic architecture requires 3.1–8.1×fewer counted flow solves and 3.4–3.5×less wall-clock time. This is a real but modest reduction,not the two-to-three-orders-of-magnitude headline hypothesised a priori, and it comes withdeterminism, an exact analytic Jacobian for the constraint rows, and per-iteration failure-modediagnostics for which the nested baseline has no analogue. Generalisation is then evaluatedon two pre-registered panels. A 20-section NACA panel recovers all 18 generable sections to∥A−A∗∥_∞ ≤1.51 ×10⁻¹⁰; a 117-section panel drawn from the UIUC coordinate databaserecovers every one of its 83 converged sections to better than 10⁻⁴, while missing the pre-registered composite criterion on iteration count rather than on accuracy. Both panel outcomesare reported alongside the exclusions they rest on and the geometric bias those exclusions carry.The architecture is finally placed on a comparison table against MISES’s own modal inverse mode,the nearest prior CST-based inverse method, and the current generative and learned-surrogateinverse-design literature, on formulation class, cost, determinism and constraint-handling groundsrather than a single flow-solve number, since the methods are not commensurable on that axisalone.

Abstract: Electric vehicles (EVs) are increasingly recognised as a key component of sustainable mobility transitions. While technological advancements and supportive policies have significantly expanded EV adoption, the relatively high purchase cost of conventional EVs remains a barrier for many consumers. In response, a new generation of frugal EVs has emerged, emphasizing affordability, resource efficiency, and functional adequacy while maintaining essential mobility requirements. Despite growing commercial interest, the factors influencing the acceptance and affordability of frugal EVs remain underexplored in the academic literature. This paper addresses this gap through a structured literature review using the SALSA (Search, Appraisal, Synthesis, and Analysis) framework. A total of 116 studies were selected and analysed to identify the key factors influencing user acceptance and affordability in the context of frugal EVs. The reviewed evidence was synthesised into four principal dimensions: demographic, situational, contextual, and psychological factors. The findings indicate that affordability, low operating costs, compact urban mobility, modular vehicle design, and battery-swapping technologies are among the most important drivers of frugal EV adoption. In contrast, safety concerns, limited driving range, charging infrastructure constraints, weak resale value, regulatory uncertainty, and perceptions of lower quality remain significant barriers to market acceptance. The review further suggests that the adoption dynamics of frugal EVs differ from those of conventional EVs, particularly due to their emphasis on cost-performance trade-offs and urban mobility applications. From a smart-city perspective, frugal EVs have the potential to support more inclusive and sustainable urban transportation systems by expanding access to low-carbon mobility for cost-sensitive users and underserved market segments. The study contributes to the emerging literature on affordable electric mobility by providing a dedicated framework for understanding frugal EV acceptance and affordability. It concludes with recommendations for policymakers, manufacturers, infrastructure providers, and service operators aimed at supporting the wider adoption and distribution of frugal EVs.

Abstract: This research focuses on the development of a robotic cell test bench for manufacturing variable-geometry tubular parts with minimal human intervention. The system integrates dual arm manipulation with cutting, welding, and coating stations, and was validated in collaboration with an industrial partner with strict requirements on weld strength, surface quality, and air leak rate. Stable and repeatable operation of the robotic cell was achieved through the characterization and optimization of key welding process parameters, particularly the robot end-effector travel speed (mm/s) and laser power (W). In addition, preliminary sealing evaluations demonstrated that the welded components could withstand internal pressures. These results highlight the influence of mechanical components, geometries, and material behavior, as well as the need for advanced digital tools. A preliminary automated path generation methodology is introduced to support adaptable robot trajectories. This research aims to advance composite manufacturing using industrial robotics and dedicated tooling for complex processes, while inspiring engineers and researchers to further automate repetitive and labour-intensive tasks that remain challenging to robotize.