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Permanent magnet synchronous motors (PMSMs) are widely used in agricultural unmanned aerial vehicle (UAV) electromechanical systems for their high efficiency and power density. While sliding mode control (SMC) offers robustness for PMSM drives, conventional designs face challenges like slow convergence, singularity, and chattering. This paper proposes a model-free improved non-singular fast terminal SMC scheme with an improved adaptive super-twisting algorithm and a disturbance observer (MFINFTSMC-IADSTA-IFTSMO) for agricultural UAV applications. The designed sliding surface ensures fixed-time convergence without singularity, the adaptive reaching law reduces chattering, and the observer enables feedforward compensation of disturbances. Closed-loop stability is proven via Lyapunov theory. DSP-based experiments demonstrate that the proposed method outperforms existing SMC variants in dynamic response, steady-state accuracy, chattering suppression, and disturbance rejection. Specifically, the proposed method achieves a start-up convergence time of only 0.35 s, which is 56.25% shorter than that of the classic SMC-STA method, fully verifying its superior fast convergence performance. Full article

Despite the growing adoption of Ti6Al4V-ELI made by Laser Powder Bed Fusion (LPBF) in tribologically demanding applications, the influence of hybrid nanoparticle additives on its lubrication behavior under starved contact conditions remains insufficiently explored. The tribological performance of Ti6Al4V was investigated under starved boundary-to-mixed lubrication conditions using engine oil modified with Ag-doped TiO₂ nanoparticles. Double-scan LPBF-fabricated discs were tested in a ball-on-disc configuration against AISI 52100 bearing steel using a TRB³ tribometer. Nanolubricants were prepared by dispersing TiO₂ and Ag–TiO₂ nanopowders with different Ag⁺/Ti⁴⁺ ratios (0.5%, 1.5%, and 2.5%) in SAE 10W-40 engine oil at a constant nanoparticle concentration of 0.05 wt%. Comprehensive physicochemical characterization of the nanopowders and nanolubricants was performed through structural, chemical, optical, morphological, rheological, and stability analyses. Tribological experiments were conducted following a full-factorial design combining three normal loads (5–15 N), three sliding speeds (0.10–0.20 m·s⁻¹), and four lubricant formulations. The steady-state coefficient of friction ranged between 0.281 and 0.359, while the specific wear rate varied from 2.81 × 10⁻⁴ to 4.83 × 10⁻⁴ mm³·N⁻¹·m⁻¹. The contact temperature rise remained relatively moderate, within the interval of 1.9–9.4 °C. Among the investigated formulations, the lubricant containing 1.5% Ag–TiO₂ exhibited the lowest friction coefficient, whereas the formulation with the highest Ag content showed improved stability of tribological performance across the investigated operating domain. These results indicate that Ag-modified TiO₂ nanoparticles are consistent with the formation of protective tribofilms and contribute to the stabilization of friction, wear, and thermal behavior under starved lubrication conditions. ANOVA confirmed that sliding speed and the load–lubricant interaction are the dominant factors governing friction and wear, while normal load controls the thermal response. These findings support the use of Ag–TiO₂ nanolubricants as a viable strategy for stabilizing interfacial behavior in LPBF-fabricated titanium components operating under starved lubrication conditions. Full article

This study presents a systematic topological progression and multi-objective optimization of a Permanent Magnet-assisted Non-overlapping Winding Line-Start Synchronous Reluctance Motor (PMaNWLS-SynRM) for industrial applications. To explicitly highlight the core contribution, the research establishes a rigorous comparative framework evaluating the transition from a conventional 4-pole/36-slot distributed winding (DW) to a 2 × 12-slot non-overlapping winding (NW) architecture. Baseline results demonstrate that the NW configuration shortens end-turns, successfully reducing total electromagnetic losses from 417 W to 349 W and improving steady-state efficiency from 93.7% to 95.1%. To overcome the inherent starting limitations of pure synchronous reluctance machines, an aluminum squirrel-cage is integrated to enable robust direct-on-line (DOL) synchronization, while NdFeB permanent magnets are embedded within the rotor flux barriers to mitigate asynchronous spatial harmonics and elevate torque density. Finite element analysis (FEA) confirms this magnetic assistance raises the average synchronous torque to 65.8 Nm while suppressing absolute torque ripple to 1.37 Nm. Finally, an evolutionary genetic algorithm is deployed across 440 iterative configurations to resolve geometric multi-physics conflicts. The finalized optimized design achieves a 13.2 kW output power at 1800 rpm, maximizing average torque to 70.12 Nm and strictly dampening absolute torque ripple to an industry-acceptable 1.04 Nm. Operating with an aggregated total loss of 1382 W, the optimized PMaNWLS-SynRM yields a 90.5% operational efficiency, definitively validating its suitability as an ultra-premium IE4/IE5 alternative to conventional induction motors. Full article

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k−ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article

This study investigates steady-state conductive heat transfer and water-vapor diffusion through the external wall of a refrigerated warehouse with a specified load-bearing wall assembly. The formal analogy between heat conduction and mass diffusion is stated and used to establish a practical calculation framework for estimating heat and moisture ingress through multilayer cold-store walls. Calculation routines are presented to determine the temperature field and the corresponding water-vapor saturation and partial-pressure distributions across (and within) the insulation layer, enabling the identification of regions prone to interstitial condensation. The analysis highlights the roles of (i) the vapor diffusion resistance of the vapor barrier layer, (ii) the thermal resistance of the insulation, and (iii) key outdoor boundary conditions in governing condensation risk. Increasing insulation thermal resistance reduces external heat gains; however, it may also increase the likelihood of condensation in layers close to the cold side by lowering local temperatures and saturation pressures. Among external parameters, outdoor relative humidity exerts the strongest influence on interstitial condensation risk. For the investigated wall assembly, increasing outdoor relative humidity by 50% shifts the condensation onset location within the insulation toward mid-thickness. The effects of vapor barrier diffusion resistance, insulation thermal resistance, and changes in outdoor conditions (relative humidity, temperature, and wind speed) are reported in tabulated form and illustrated through pressure–position and temperature–position profiles. Full article

Background/Objectives: Steady-State Visual Evoked Potential-based Brain–Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise. Full article

Reliable in situ temperature monitoring is essential for the safe operation of liquid-nitrogen-cooled superconducting cables, yet conventional electrical sensors are often difficult to scale to multi-point deployment in cryogenic, high-current environments. This work presents a fiber Bragg grating (FBG) sensing solution for in situ temperature monitoring of superconducting cables in liquid nitrogen. An FBG array packaged with a polyimide-coated fiber inside a 3 mm stainless-steel tube is deployed along the cable centerline to provide multi-point temperature measurements of the cable core. The system is validated under liquid-nitrogen immersion with a 2000 A current turn-on/turn-off test, with a 1 Hz update rate and a steady-state temperature fluctuation within ±0.1 °C. Experimental results show a continuous temperature decrease during liquid-nitrogen cooling, followed by a cryogenic plateau, during which a spatially consistent 0.6–0.7 °C current-induced temperature rise is observed across multiple sensing points in the present 2000 A turn-on/turn-off test, followed by recovery after current shutoff. Small-amplitude fluctuations during the plateau are attributed to packaging-dependent thermal coupling between the centerline-deployed sensor and the cable core. These results indicate that the proposed FBG-based approach enables reliable cryogenic thermometry for superconducting cables in liquid nitrogen and provides a practical tool for in situ operational condition assessment. Full article

This paper presents an iterative graph-analytical procedure for determining the roll center height, one of the most critical design parameters influencing vehicle dynamic behavior during cornering. The conventional approaches generally determine roll center locations from suspension kinematics and then evaluate vehicle behavior using multibody or numerical vehicle dynamics models. By contrast, the proposed method is intended for the preliminary design stage and provides a direct correlation between front and rear target roll center heights using tire test data, load transfer and axle-level equilibrium conditions. The main advantage of the method is that it helps define a feasible design space before detailed geometry optimization or MBD validation is performed. The objective is to achieve stable and neutral handling (avoiding intrinsic understeer or oversteer tendencies) during steady-state cornering at a predefined target lateral acceleration. The methodology integrates (i) lateral force equilibrium at the axle level, (ii) a dynamic load transfer model based on axle roll stiffness and roll center heights, and (iii) experimental tire grip characteristics (lateral force–slip angle curves under varying vertical loads), processed through numerical interpolation. The procedure is demonstrated using a vehicle model with specific geometric and mass parameters. The results indicate that the methodology does not yield a single unique solution, but rather a set of correlated roll center heights, allowing the designer to select the most feasible geometric configuration while maintaining neutral handling. As an example, the paper presents a convergent solution for the front and rear roll center heights that satisfy neutrality conditions at a slip angle of approximately 4°. This study provides a fundamental framework for the geometric design of suspension systems and serves as a basis for subsequent numerical and experimental validation. Full article

Hydrogen embrittlement (HE) represents a critical degradation mechanism in carbon steel components operating in hydrogen-rich environments, such as those encountered in clean energy and petrochemical applications. This study evaluates the hydrogen permeation barrier performance of titanium nitride (TiN) nanostructured thin films deposited by High-Power Impulse Magnetron Sputtering (HiPIMS) on SAE 1020 carbon steel substrates. Electrochemical permeation measurements were performed using the Devanathan–Stachurski dual-cell methodology in accordance with ASTM G148 and ISO 17081 standards. Key hydrogen transport parameters quantified include the effective diffusion coefficient (D_eff), lag time (t_lag), and steady-state hydrogen oxidation current density. The TiN/carbon steel composite system exhibited t_lag = 570 s, D_eff = (2.68 ± 0.09) × 10⁻¹⁰ m² s⁻¹ and a steady-state hydrogen oxidation current density of 21.5 µA cm⁻², corresponding to a permeation reduction factor (PRF) of 2.32 and a barrier efficiency of η = 56.9%. The superior barrier performance is attributed to the dense, low-defect microstructure characteristic of HiPIMS deposition. These results validate HiPIMS-deposited TiN as a robust hydrogen diffusion barrier, with the established performance metrics providing quantitative benchmarks for the design of hydrogen-resistant coatings in energy applications. Full article

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment. Full article

Enhanced biological phosphorus removal (EBPR) technologies, which are widely applied in wastewater treatment, are based on the activity of polyphosphate-accumulating organisms (PAOs). However, the transition from conventional systems to EBPR remains poorly understood. In this study, phosphorus removal performance and the succession of an activated sludge microbial community were investigated in a sequencing batch reactor during transition from the anoxic/aerobic process to the anaerobic/aerobic EBPR configuration. Reactor performance data combined with molecular analyses revealed that community development proceeded through three distinct stages. During the first 15–20 days, a community with a pronounced PAO phenotype was formed, in which representatives of the genus Azonexus predominated (up to 23.8%). From 15–20 days to 2 months, a pseudo-steady state was achieved, with stable phosphorus removal (36–51%) and sustained dominance of Azonexus (up to 23–18%). Then a shift in community structure occurred, marked by a decline of Azonexus (<1%) and an increase in Accumulibacter (up to 8.3%) and other potential PAOs (Comamonadaceae and Thiotrichaceae); however, phosphorus removal efficiency decreased to 27–31%. These results highlight the importance of considering microbial succession at the EBPR start-up to develop operational strategies that ensure sustainable phosphorus removal. Full article

Achieving high selectivity in the hydrogenation of CO₂ to light olefins remains challenging because of the complex reaction network and the difficulty of regulating key intermediates. Motivated by green-hydrogen-enabled power-to-chemicals pathways, we combine density functional theory (DFT) with first-principles microkinetic simulation (FPMS) to construct a quantitatively predictive reaction-energy landscape and elucidate structure–selectivity relationships. A comprehensive reaction network is established through energy-surface fitting, and steady-state rate constants are solved to capture the microkinetic competition between elementary steps. By introducing electronic density-of-states (DOS) modulation as a design variable, we directly correlate surface structural parameters with rate-controlling steps, thereby enabling targeted regulation of C–C coupling and hydrogen transfer processes. The calculated barrier for CO₂ adsorption to COOH* is 1.35 eV, while the transition state barrier for C–C coupling is 1.50 eV, corresponding to a reaction rate of 9.7 × 10³ s⁻¹; the olefin desorption rate reaches 1.7 × 10⁷ s⁻¹. Crucially, shifting the d-band center from −2.35 eV to −1.60 eV increases the C₂–C₄ olefin selectivity from 42.6% to 68.3%, establishing an actionable electronic structure lever for catalyst optimization. These results reveal the intrinsic mechanism by which surface electronic and geometric regulation governs intermediate stabilization and rate control, providing a verifiable, mechanism-based design principle for efficient CO₂-to-olefin catalysts aligned with green hydrogen deployment. Full article

A new control method for efficiency optimization in systems composed of parallel converters is presented in this paper. The proposed methodology considers the individual efficiency surfaces for given ratings of power and voltage and determines the optimum operating point for each converter such that the global system efficiency is maximized throughout the entire operating spectrum. Furthermore, a supervisory control strategy is proposed to manage the power-sharing of the converters according to the optimal surfaces provided by the methodology, enabling a performance enhancement for the system by improving its efficiency. Different approaches can be used to implement the active current sharing (ACS) scheme, and in-depth discussions are provided to guide the designer through the tradeoffs to achieve the desired transient and steady-state behavior for the system. Experimental results show that under light load operation, an improvement of 8.5% is achieved in comparison with a conventional technique of equal power-sharing. This points out that the proposed strategy is especially applicable and can significantly improve the performance of systems powered by batteries or renewable sources. Full article

The dynamic properties of spread and adsorbed layers of amyloid-like silk fibroin fibrils (ALF) differ significantly from the properties of native protein layers (RSF). In the former case, the dynamic dilational surface elasticity and the steady-state adsorbed amount are considerably lower than in the latter case. This high dynamic elasticity of RSF layers is close to that of the layers of solid nanoparticles and is provided by the spontaneous formation of various interconnected supramolecular structures at the interface. The ALF produced at elevated temperatures is also intertwined at the interface but does not form a continuous network. In this case, the layer properties are close to those of the layers of amyloid fibrils of globular proteins. If the ALF dispersion is purified from admixtures of unreacted protein molecules, the dynamic surface elasticity reaches about 140 mN/m, similar to the results for dispersions of amyloid fibrils of globular proteins. The admixtures of unreacted protein molecules of high surface activity significantly influence the dynamic surface properties participating in the self-assembly, thereby leading to a slight increase in the surface elasticity. At the same time, the ALF acts as an effective inhibitor of the formation of supramolecular structures in the surface layer for mixed systems. Under the influence of amyloid fibrils, neither the impurities nor the addition of native RSF lead to mechanical surface properties close to those of native fibroin systems. Full article

This paper presents a novel voltage differencing transconductance amplifier (VDTA)-based mixed-mode inverse filter capable of operating in voltage mode, transadmittance mode, transimpedance mode, and current mode using a single topology. The proposed configuration employs only three VDTAs with two resistors and three capacitors, offering low component count, high input/output impedance flexibility, and no requirement for component matching. It simultaneously realizes first-order inverse lowpass and highpass, as well as second-order inverse bandpass responses. A comprehensive non-ideal analysis, which includes the effects of VDTA parasitic impedances, determines the practical operating frequency range. The design is validated through PSPICE simulations using 0.18 μm CMOS technology, showing close alignment between theoretical predictions and simulation results, with cutoff frequencies of approximately 1.60 MHz and low power consumption of 0.972 mW. Further analyses confirm orthogonal tuning capability, acceptable temperature stability, and robustness against component tolerances. In a practical application, the proposed inverse filter is employed to implement a mixed-mode PID controller, which significantly improves transient response characteristics by reducing rise time, settling time, and steady-state error. These findings highlight the effectiveness and versatility of the proposed design for analog signal processing and control system applications. Full article