Engineering

Abstract: In order to enhance the amphibious mobility of robots in water-land environments, this paper proposes a frog-inspired hybrid drive amphibious robot, based on the amphibious locomotion characteristics of frogs. Distinct from existing single-mode frog-inspired jumping or swimming robots, the proposed robot innovatively integrates hybrid propulsion to simultaneously achieve both frog-like swimming and jumping capabilities. On land, the robot utilizes an explosion-driven hind limb actuation mechanism, paired with a linkage-based forelimb posture control system, to achieve high-performance frog-like jumping. In water, a rope-driven hind limb mechanism facilitates extension and retraction movements, while controllable soft-actuated flippers enable swinging and opening/closing motions, thereby achieving efficient frog-like swimming. In addition, an amphibious dynamic model was developed, and the robot's amphibious locomotion capabilities were evaluated and analyzed. Finally, an experimental prototype platform was built to test the amphibious locomotion performance of the designed robot, and a comparative analysis was conducted with the theoretical model. The experimental results not only validated the correctness of the amphibious dynamics and motion theory, but also confirmed the effectiveness of the designed amphibious terrain-crossing mechanism.

Abstract: Modern infocommunication, sensing, and cyber-physical systems increasingly rely on heterogeneous data streams originating from channels of different physical nature, sampling rates, reliability levels, and uncertainty characteristics. Direct fusion of such data in conventional artificial intelligence pipelines often yields decision outputs that are difficult to interpret, calibrate, and trust, especially in safety-related or security-related applications. This work proposes an event-probabilistic approach to the unification of heterogeneous sensor data for decision-support systems. The main idea is to transform heterogeneous sensor observations into a common space of event-oriented probability estimates, which can then be integrated using reliability-aware weighting. In this form, the system can generate not only a final recommendation, but also supporting metrics, including event likelihood, risk level, uncertainty, data quality, and inter-channel conflict. The paper formulates the conceptual and architectural basis of the proposed framework and discusses its compatibility with further Bernoulli encoding and stochastic processing. An illustrative numerical experiment involving four sensor channels and three representative scenarios is used to demonstrate the behavior of the framework. The results show that adaptive reliability-aware weighting improves the stability of the integrated event probability under channel degradation, while explicit conflict assessment prevents unjustified automatic decisions under contradictory sensor evidence. The proposed framework may serve as a basis for future stochastic and photonic-stochastic decision-support systems in access control, industrial monitoring, transport infrastructure, and critical-infrastructure applications.

Abstract: Piles with continuous helix (referred to herein as "screw pile") is a new configuration of helical piles. It features a continuous helix spiraling several pitches around a smooth shaft forming a "threaded shaft". This study investigates the compressive capacity and behavior of helical and screw piles using 3D numerical models calibrated and validated against full-scale field testing. The bearing capacity factor, Nc, for helical piles is back-calculated from the numerical results and compared against standard theoretical assumptions to evaluate their accuracy in predicting ultimate capacity. Parametric studies are conducted considering screw piles configuration, including shaft diameter, pitch size, helix diameter, as well as soil strength. The results reveal that shaft resistance accounts for up to 89% of the total capacity. Analysis of load distribution, shear contours, and displacement contours at failure allowed for the identification of different failure modes of soil adjacent to the pile’s threaded shaft: Individual Bearing Mode (IBM), Cylindrical Shear Mode (CSM), and a combined mode. The study identifies specific parametric thresholds for these modes in both sand and clay layers. Furthermore, varying clay strength is found to alter the development of the shear surface, transitioning from localized bearing to continuous shearing along the threaded shaft. Finally, apparent shaft resistance factors, α and β, are back-calculated to provide ractical parameters for evaluating the resistance of threaded shafts in layered soil.

Abstract: This study presents an approach to quantifying impact-induced damage severity in composites, focusing on synthetic carbon fibre reinforced polymer (CFRP), natural flax fibre reinforced polymer (FFRP) and hybrid fibres reinforced polymer (HFRP) composite of carbon and flax. The investigation aims to quantitatively characterise impact damage under energies ranging from 10 to 70 J through acousto-ultrasonics (AU) testing, proposing an efficient technique for evaluating the integrity of various FRP composites under in-service conditions. AU testing was performed at azimuthal angles of 0°, 30°, 45°, 60° and 90°, utilising acousto-ultrasonic waveform indices (AUWIs), such as wave velocity, peak amplitude, energy content, centroid frequency and skewness factor. Damage severity index is correlated with the damage mode. The findings establish that wave velocity is a reliable parameter for quantifying damage severity across all composite material types considered, with high adjusted R² values of 0.92 for CFRP, 0.89 for FFRP and 0.90 for HFRP. Peak amplitude also shows considerable sensitivity. Finally, this research highlights the limitations of traditional non-destructive evaluation (NDE) techniques and demonstrates the potential of combining multi-damage metrics with advanced imaging methods, such as X-ray micro-computed tomography (X-ray µCT) and scanning electron microscopy (SEM), to provide comprehensive assessment of damage in various composite materials. The proposed methodology offers a promising approach for quantifying the impact damage severity in composite structures, as applicable to wind turbine blades, amongst other structural components.

Abstract: Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because of the difficulty of segmenting individual cardiomyocytes and the reliance of previous methods on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a deep learning framework for classifying CX43-positive regions as either terminal or lateralized directly from fluorescence images, without requiring cardiomyocyte segmentation. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream convolutional classifier based on EfficientNetV2-S was trained to capture both the local and contextual morphology of each region. In addition, an inference module applicable to whole tissue sections was developed to generate spatial lateralization probability maps and global percent lateralization estimates consistent with expert annotation. On the test set, CLARISA achieved a ROC-AUC of 0.905 and a PR-AUC of 0.810. These results support the feasibility of automated assessment of CX43 distribution patterns without explicit cardiomyocyte segmentation. The complete codebase is publicly available, together with access to the pretrained model and the image data used in this study. The Hugging Face model card reports the same held-out test metrics and states that the checkpoint is intended to be used with the main repository.

Abstract: Background: Perception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories. This limitation is particularly relevant in experiential contexts, where subjective judgments often involve transitional evaluations. Methods: This study develops a parameterized fuzzy–entropy exploratory factor analysis (FE-EFA) framework for uncertainty-aware analysis of ordinal perception data. The approach transforms ordinal responses into fuzzy membership distributions, constructs a correlation structure in membership space, and incorporates Shannon entropy and Jensen–Shannon divergence to characterize distributional dispersion and representation differences. The framework is applied to survey data from Chengdu Tramway Line 2 (N = 1242; 32 indicators). Results: Under the Kaiser criterion (eigenvalues > 1), conventional EFA yields a seven-factor structure, whereas FE-EFA identifies an additional eighth factor located near the retention boundary. Under a unified factor specification, both approaches preserve a consistent high-level structure, while FE-EFA shows clearer factor separation, fewer cross-loadings, and more coherent indicator clustering. From an information-theoretic perspective, FE-EFA produces higher entropy (average = 0.8688) and moderate Jensen–Shannon divergence (average = 0.0133), indicating a controlled redistribution of ordinal information rather than structural distortion. Entropy-informed weighting further reveals systematic shifts in indicator importance across key dimensions. Conclusions: The FE-EFA framework extends conventional Likert-scale analysis by introducing an uncertainty-aware representation layer prior to factor extraction. It preserves overall structural stability while improving the resolution of latent constructs and the sensitivity of indicator representation. The proposed approach provides a practical and theoretically grounded basis for perception-based evaluation and decision support in tramway cultural-tourism development and related contexts.

Abstract: The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane's boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane's movements. The Neural Network algorithm optimized the crane's movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane's structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane's fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life.

Abstract: Building upon foundational Item Response Theory (IRT) research conducted at Tecnologico de Monterrey with University of São Paulo (USP), this study presents CONF.i, a framework integrating Canvas LMS with a three-variable IRT model (Grade-Confidence-Performance) and Google's Gemini AI. Using design-based research methodology, an external Google Apps Script application was developed, interfacing with Canvas LTI standards, implementing IRT-based assessment with student confidence ratings and AI-generated personalized feedback and learning resource recommendations. Pilot testing with twenty-three undergraduate students at Tecnologico de Monterrey, Mexico, with theoretical validation from USP collaborators, demonstrated technical feasibility and pedagogical value. Results revealed that 82% of students rated the interface positively, 87% understood the confidence rating mechanism, and 91% would recommend the approach. The three-variable model revealed four learning patterns within the pilot sample that would be invisible to traditional scoring: aligned mastery (34.8%), underconfident competence (21.7%), overconfident struggle (26.1%), and aligned struggle (17.4%). These observed patterns suggest potential for enabling targeted instructional interventions, warranting further investigation with larger samples. This Brazil-Mexico collaboration demonstrates that sophisticated educational technologies can be integrated within existing institutional infrastructure without commercial licensing costs, contributing to Sustainable Development Goal #4 (Quality Education) by making adaptive learning technologies more accessible through mainstream platforms.

Abstract: This work deals with the time-domain analysis of asymmetrical faults in three-phase systems. Conventional three-phase analysis provides steady-state solutions for asymmetrical faults. Transient analysis, however, is usually performed by resorting either to oversimplified approximate circuits, or to numerical methods. In this paper, a rigorous analytical methodology based on the time-domain Clarke transformation is presented for the most common asymmetrical faults in three-phase systems. In particular, it is shown that asymmetrical faults result in circuit coupling in the Clarke equivalent circuits. Circuit representation of coupling is also derived in the paper. Coupled equivalent circuits allow rigorous analytical solution of transients in case of asymmetrical faults. The analytical results derived in the paper are validated through proper numerical simulation of faulted radial systems.

Abstract: The Automobile industry shifts from linear to circular economy for sustainability on a global level with respect to the industrial revolution 5.0, but it faces challenges when establishing circular economy. Circular supply chain implementation is dependent on multiple barriers and enablers, including economic managerial, technological, regulatory and social domains, making it ineffective for single factor solution. The purpose behind this review is to conduct a systematic literature review to develop an understanding how these interconnected barriers and enablers can together shape the circular supply chain implementation and their performance, specifically inside the automotive sector which is still remain a little known. By applying the PRISMA framework on 150 peer reviewed articles, research papers. The research shows that literature focuses on primarily on electric vehicle barriers within developing economies. circular supply chain implementation is governed not only by isolated barriers but by complex systematic interdependencies between enablers as well. This interdependencies are of enablers and barriers can be further classified into economical and financial, managerial and organizational, technological and infrastructure, policy and regularity and market and social. The study shows two systematic patterns, driving the transition technology- policy interdependence and conflicting relationship between large scale production and value extraction. The findings also presented a research agenda focusing on strategic value creation through material streams of automotive electronics, plastics and composites with high potential value and further insights are needed. Circular supply chain as a strategic approach for securing critical material supplies, while policymakers could leverage the use of digital tools as the foundational infrastructure for subsidies allocation and prevent the fraud.

Abstract: Soft robotics is a rapidly evolving field that has attracted significant attention within the scientific community. This review analyzes the main advantages of pneumatic technology in service robots across the different application domains defined by the International Federation of Robotics (IFR). By organizing the literature according to application domains, this work aims to clarify the specific benefits of pneumatic and soft pneumatic solutions in each context. The proposed approach distinguishes between traditional pneumatic solutions and the subsequent emergence of soft robotics, in order to highlight how and to what extent soft technologies have reshaped the design and application scenarios. Particular attention is devoted to the role of materials and recent manufacturing techniques used by researchers to fabricate soft pneumatic robots. Finally, current research trends are discussed, with the goal of identifying key directions for the further development of soft pneumatic service robots.

Abstract: Data science techniques are increasingly employed to enhance process efficiency, reduce energy consumption and operational costs, enable active process control, ensure consistent product quality, and support predictive maintenance in modern manufacturing systems. A central question arising from recent developments is: How can data models fundamentally transform manufacturing processes, and what are the primary barriers to their widespread adoption? Contemporary manufacturing sectors are progressively integrating data models within digital twin and digital shadow frameworks to enable real-time process optimization and data-informed decision-making. However, the inherent complexity of manufacturing processes—combined with the frequent scarcity of high-quality, balanced datasets—often limits the generalizability and interpretability of purely data-driven models. In practice, quality, contextual relevance, representativeness, and richness of data are significantly more critical than its sheer volume when developing robust and reliable models. This paper provides a comprehensive overview of the application of data modeling in dynamic manufacturing environments. It examines key aspects such as data generation, sampling strategies, data preprocessing and handling, and model development methodologies across steady-state, transient, and generative process regimes.

Abstract: The growing adoption of electric vehicles (EVs) and the rapid expansion of public charging infrastructure pose new challenges and opportunities for energy systems, particularly in urban settings. This study presents an optimization-based evaluation of different EV charging strategies including direct charging, average-based methods, smart charging, and vehicle-to-grid (V2G) at public parking lots using real-world charging session data. This data-driven model is set to optimize the public EV charging of vehicles in Gothenburg, without sacrificing on the energy requirement while minimizing charging costs for the operators. Results indicate that direct charging scenarios lead to significantly higher peak loads (up to 1286 kW) and costs (around 370 k€), highlighting their inefficiency under unmanaged operation. In contrast, smart charging reduces peak loads by approximately 47% and overall costs by around 74%, showcasing its potential for cost-effective grid-friendly operation. Two different V2G scenarios were tested based on the impact of discharged power accounted for in peak costs, though it enables energy discharge back to the grid, the benefits remain modest under current assumptions due to tight operational constraints and limited incentives. The study emphasizes the value of smart optimization and appropriate market design in enhancing the flexibility and cost efficiency of public EV charging systems.

Abstract: The Zeta-Minimizer Theorem establishes a variational foundation for the Riemann zeta function by minimizing a phase functional derived from the compressibility factor. Starting from the classical virial expansion, the theorem performs an exact exponential resummation that yields the Euler product form of ζ(s) over a finite helical basis. In a symmetric measure space equipped with non-proper Archimedean conical helices, four geometric constraints—rational signed cosines, positive integer representation dimensions, non-zero integer differences, and prime-modulated exponential decays—force primes to emerge as indivisible cycles in the representation graph, via Hilbert’s irreducibility theorem and Maschke’s theorem. Corollaries include the deductive proof of the Riemann Hypothesis (non-trivial zeros spectrally centered on Re⁡(s)=1/2), stacked phases as stratified orbifolds, emergent layered geometries, bounded prime descent, and dimensional resistance. The three axioms abstract thermodynamic equilibrium conditions purely: strict concavity of entropy on measures, non-vanishing spectral Gibbs minima, and covariance with flux conservation. Number-theoretic structures, complex numbers, polynomials, and quantization itself appear as projected artifacts of the underlying variational optimization. Applications range from atomic stratification (quantized shells arising from phase jumps) and angular-momentum tensors to the fine-structure constant (emergent from cycle sums with β=5 leaps) and covariant mappings to arbitrary conjugate variables via category-theoretic functors and renormalization-group universality. By demoting elementary mathematical constructs to derived descriptions of thermodynamic optimization on the helical manifold, ZMT provides a unified deductive framework for analytic number theory, algebraic geometry, and spectral theory.

Abstract: During the continuous casting of high-titanium steel, traditional fluorine-containing mold fluxes are prone to causing fluoride contamination, equipment corrosion, and intensified slag-metal interface reactions. There is an urgent need to develop highly adaptable fluorine-free mold flux systems. In this study, titanium-containing blast furnace slag was used as the primary base material, while borax, soda ash, and witherite were selected as fluoride-substituting mineral raw materials. The effects of these mineral raw materials on the melting properties, crystallization behavior, crystalline phases, and microstructure of fluorine-free mold fluxes were systematically investigated, and an optimized mold flux design suitable for continuous casting of high-titanium steel was further developed. The results indicate that borax significantly reduces the melting temperature and viscosity and markedly suppresses the growth of crystalline phases such as calcium borosilicate, nepheline, and perovskite by weakening the polymerization degree of the silicate network, thereby substantially decreasing the crystallization ability of the mold flux. Soda ash primarily acts as a strong fluxing and network-depolymerizing agent, promoting the formation of low-polymerized structural units. It also enhances the tendency toward ordered atomic arrangement, thereby markedly increasing nepheline precipitation and the overall crystallization ratio. Witherite exerts a relatively mild effect on slag structure and phase evolution; its moderate addition helps synergistically reduce the melting point, viscosity, and crystallization ratio, thereby supporting performance stability. The optimized fluorine-free mold flux, designed on the basis of these findings, maintains a suitable initial crystallization temperature and critical crystallization cooling rate while exhibiting lower melting temperature, viscosity, and crystallization ratio than conventional fluorine-bearing flux. Moreover, the introduction of TiO2 reduces the chemical potential difference between Ti in the molten steel and the fluorine-free mold flux, thereby slowing down the rate of slag-metal interface reactions and improving compositional stability. The research results provide a theoretical basis for the industrial design of environmentally friendly mold fluxes for high-titanium steel and for improving billet quality.

Abstract: Silver nanoparticles (AgNPs) have attracted significant attention due to their remarkable antimicrobial, antibacterial, and catalytic properties, enabling widespread applications in consumer products, biomedical fields, and environmental systems. Conventional chemical and physical synthesis routes, however, often involve toxic reagents and generate hazardous byproducts, raising environmental and health concerns. In response, green synthesis approaches employing biological entities such as plant extracts, bacteria, and fungi have emerged as sustainable and eco-friendly alternatives. These methods utilize natural reducing and stabilizing agents, minimizing toxicity while enhancing biocompatibility. This review comprehensively examines green-mediated synthesis strategies for AgNP-based composites, highlighting their physicochemical properties and functional performance. Additionally, the potential toxicity and environmental implications of AgNPs are critically discussed. Particular emphasis is placed on their applications in environmental remediation, including water purification, pollutant degradation, and antimicrobial treatments. Overall, green-synthesized AgNP composites offer a promising pathway toward sustainable nanotechnology for environmental pollution control.

Abstract: This paper proposes a novel speed controller design for a brushless DC motor (BLDCM) under field-oriented control (FOC). The designed novel speed controller combines decision tree theory (DTT) and sliding mode theory (SMT). First, the regression algorithm of the classification and regression tree (CART) within decision tree theory is utilized to divide the speed error between the BLDCM's speed command and actual speed into 10 intervals. Based on this, three parameters of the existing exponential reaching law sliding mode controller (ERLSMC)—the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain—are configured. Next, the mean squared error (MSE) of each node after splitting is calculated to identify the root node. According to the selected split variable and splitting point, the data is divided into two subsets, and this process is repeated for each child node. Consequently, during the operation of the BLDCM, appropriate adjustments for the three gains can be provided to the sliding mode speed controller. Subsequently, a new sliding mode dynamic trajectory control gain is recalculated based on the rate of change of the speed error. This allows the overshoot in the system's speed response, caused by adopting the exponential reaching law (ERL), to be improved under different operating conditions through the modulation of its three gains. It also enables the speed response of the BLDCM drive system to rapidly track the speed command under various operating conditions. Therefore, the proposed control law involves no complex computations and does not require a massive amount of training data, making it easy to implement. Finally, Matlab/Simulink simulation software is used to simulate the application of the proposed control law to the BLDCM drive system. Its control performance is compared with sliding mode controllers (SMCs) utilizing three different reaching laws: the constant speed reaching law (CSRL), the ERL, and the extension theory combined with exponential reaching law (ETERL). The simulation results demonstrate that the proposed novel speed controller outperforms the SMCs with the other three reaching laws in terms of both speed command tracking and load regulation response.

Abstract: Tropospheric over-the-horizon (OTH) propagation is a prominent research hotspot in the field of radar countermeasure and reconnaissance. Clarifying the variation law of its impacts on radar emitter signatures is the critical prerequisite for over-the-horizon radar emitter recognition (RER) in complex electromagnetic environments. In recent years, the relevant theories and practical applications of tropospheric OTH propagation mechanism and RER have been continuously improved. However, existing studies have not yet achieved in-depth integration of the two fields, nor systematically sorted out the influence mechanism and variation law of OTH propagation on radar emitter signatures. On this basis, this paper starts from the mechanism and models of tropospheric OTH propagation, conducts the analysis of emitter signal characteristics through the channel characteristics of propagation, and systematically reviews the research achievements in this field. Finally, this paper summarizes the technical bottlenecks and improvement strategies for RER in OTH scenarios, and aims to provide a theoretical and technical reference for promoting the integration of OTH transmission and RER technologies.

Abstract: The paper proposes an implementation of Kolmogorov-Arnold networks (KANs) for the purpose of dynamic proportional-integral-derivative (PID) control tuning in first- and second-order linear systems under noisy and time-varying reference conditions. Toy da-tasets, based on instantaneous system error, output and reference trajectory, are used for training the networks and comparing KANs results over a performance of: i) a PID with fixed coefficients, taken from MATLAB’s Simulink PID Autotune; ii) an MLP based neural network (NN), trained on the same datasets; iii) a traditional adaptive PID scheme with gain scheduling; iv) an LMS-based online tuning approach. Results show that KANs out-perform MLPs and LMS even with less optimized datasets under noisy and quick-changing conditions and perform on par with methods, such as gain scheduling, while allowing for more flexibility and easier setup.

Abstract: Multi-Stage Transmission Network Expansion Planning (MS-TNEP) is critical for adapting power grids to long-term renewable integration. However, simultaneously incorporating N-1 security, active power losses, and spatial generation uncertainties imposes prohibitive computational complexity. This paper proposes a probabilistic MS-TNEP model evaluated over a 20-year horizon. To overcome intractability, a hybrid decomposition framework is employed, delegating discrete combinatorial investment decisions to an upper-level metaheuristic while resolving operational feasibility, power losses via fictitious nodal demand, and N-1 contingencies through lower-level linear programming. Furthermore, a novel Pack-Based Grey Wolf Optimizer (PBGWO) is introduced to enhance convergence in this constrained domain. The approach is validated on the modified Garver and the 46-bus Southern-Brazilian systems under multiple wind and conventional generation scenarios. Comparative analysis against the Genetic Algorithm, standard GWO, and Whale Optimization Algorithm reveals that PBGWO consistently identifies the optimal expansion schedules.