Abstract: The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty ser-vo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes a dynamics model accuracy enhancement method that integrates multi-objective global sensitivity analysis and ant colony optimization-based calibration. First, a nonlinear dynamics model of the eight-bar mechanism was constructed based on Lagrange's equations, which systematically incorporates generalized external force models con-sistent with actual production, including gravity, friction, balance force, and stamping process load. Subsequently, six key sensitive parameters were identified from 28 sys-tem parameters using Sobol global sensitivity analysis, with response functions defined for torque prediction accuracy, transient overload risk, thermal load, and work done. Based on the sensitivity results, a parameter calibration model was formulated to minimize torque prediction error and transient overload risk, and solved by the ant colony algorithm. Experimental validation shows that, after calibration, the root mean square error between predicted and measured torque decreases significantly from 1366.9 N·m to 277.7 N·m (a reduction of 79.7%), the peak error drops by 72.7%, and the servo motor’s effective torque prediction error was reduced from 7.6% to 1.4%. In an automotive door panel stamping application on a 25,000 kN heavy-duty servo press, the production rate increases from 11.4 to 11.6 strokes per minute, demonstrating en-hanced performance without compromising operational safety. This study provides a theoretical foundation and an effective engineering solution for high-precision model-ing and performance optimization of heavy-duty servo presses.

Abstract: This study provides a comprehensive overview of research and advancements on carbon materials with regard to practical targets for hydrogen storage in terms of gravimetric and volumetric capacities. For the sake of clarity, only the most relevant references on hydrogen storage by adsorption are presented, although the study was conducted in the same exhaustive manner as the one initially carried out by Anne C. Dillon and Michael J. Heben [Appl. Phys. A 2001, 72, 133–142] with a particular emphasis on emerging technologies and potential applications in various sectors, and focusing on the importance of carbon-based materials with high specific surface areas and porous structures optimised to maximise adsorption — including at high pressure —, while primarily limiting references herein to experimentally validated results. It therefore offers insights into the porous materials as well as the methodologies — including a fully comprehensive and so far proven highly transferable intermolecular hydrogen model combining van-der-Waals's and Coulomb's forces — used to improve hydrogen solid storage efficiency.

Abstract:

The article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), Digital Twin (DT) technology, and PLC-based architecture aligned with the requirements of the NIS2 Directive. The proposed concept, denoted as Cyber-Physical Digital Twin with QFT & NIS2 Security (CPDTQN), unifies control, observability, synchronization, and traceability mechanisms within a single cyber-physical structure. The study employs the five-axis industrial manipulator FANUC M-430iA/4FH, modeled as a set of SISO servo-axis channels subject to parametric uncertainty and external disturbances. For each axis, QFT controllers and prefilters are synthesized, and the system performance is evaluated using joint-space and TCP-space metrics, including maximum error, RMS error, and 3D positional deviation. A CPDTQN architecture is proposed in which the QFT controllers are executed in MATLAB, a Siemens PLC (CPU 1215C, FW v4.5) provides deterministic communication via Modbus TCP, OPC UA, and NTP/PTP synchronization, and the digital twin implemented in FANUC ROBOGUIDE reproduces the robot’s kinematics and dynamics in real time. This represents one of the first architectures that simultaneously integrates QFT control, real PLC-in-the-loop execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. Simulation results using nominal and worst-case dynamic models, as well as scenarios with externally applied torque disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. Furthermore, the study analyzes how the proposed CPDTQN architecture supports key NIS2 principles, including command traceability, disturbance resilience, access control, and mechanisms for forensic reconstruction in robotic manufacturing systems.

Abstract: Flexible biosensors offer rapid and low-cost diagnostics but are often limited by the mechanical and electrochemical instability of polymer-based designs in biological media. Here, we introduce a metallic flexible sensing platform that exploits the intrinsic deformability of superelastic nickel–titanium (NiTi) for label-free impedimetric detection. Mechanical bending of NiTi wires spontaneously generates martensitic-phase microcracks whose metal–gap–metal geometry forms the active transduction sites, where functional interfacial layers and captured analytes modulate the local dielectric environment and governing the impedance response. Functionalization with thiolated monolayers and Escherichia coli-specific antibodies enables these microdomains to modulate interfacial charge transfer in response to analyte binding, creating a direct coupling between mechanical deformation and resulting impedance signal. The γ-bent NiTi sensors achieved stable and quantitative detection of E. coli ATCC 25922 in sterile human urine, with a detection limit of 53 CFU mL⁻¹ within 45 minutes, without redox mediators, external labels, or amplification steps. This work establishes the first use of self-healing martensitic microcracks in a superelastic alloy as functional transduction elements, defining a new class of metallic flexible biosensors that integrate mechanical robustness, analytical reliability, and scalability for point-of-care biosensing.

Abstract: Microgrids, as localized and flexible power systems capable of operating in both grid-connected and islanded modes, have introduced significant challenges in traditional power system analysis due to the high penetration of Distributed Energy Resources (DERs). These challenges particularly relate to short-circuit current (SCC) calculation and relay protection (RP) coordination, where conventional methods often fail to account for bidirectional power flows, inverter-based resources, and dynamic topologies. This paper presents a review of existing approaches to short-circuit analysis and relay protection coordination in microgrids. Through a critical examination of recent literature and practical implementations, we identify the current gaps and limitations in prevailing methodologies. Based on this review, the most promising methods for short-circuit calculation and relay protection coordination are selected and subjected to an in-depth analysis. These methods are applied and evaluated on a real-life microgrid system using ETAP (Electrical Transient Analyzer Program). Simulation results and performance assessments are presented, highlighting the strengths and weaknesses of the selected approaches. The findings provide valuable insights into current limitations and offer concrete directions for future research and development in the domain of microgrid protection and reliability.

Abstract: This study examines the impact of increasing photovoltaic (PV) penetration on the transient stability of the IEEE 9-bus power system. Synchronous machines are modeled with standard subtransient dynamics, while PV units are represented as current-limited grid-following inverters. Transient stability is assessed through the Critical Clearing Time (CCT) and the post-fault dynamic behavior, obtained from time-domain simulations carried out in MATLAB/Simulink®. A permanent three-phase fault on line 7–5 is considered as the limiting contingency. The results show an increase in CCT as PV generation progressively replaces the active power supplied by synchronous machines, whose inertia is therefore maintained: from 210 ms (0% PV) to 440 ms (25%) / 1080 ms (40%) at bus 5, 410 ms (25%) / 1130 ms (40%) and 290 ms (25%) / 650 ms (40%) at buses 6 and 8, respectively, demonstrating that the injection site is a key factor for system stability. For distributed injection among the three buses, CCT values of 340 ms (25%) and 1020 ms (40%) highlight the significant influence of PV placement at bus 8. Although an overall increase in CCT was observed, higher PV penetration also led to more pronounced oscillations and operability issues after the fault. These results underscore the need for stability-oriented control strategies, such as grid-forming operation, fast active power support, and dynamic voltage control. They also suggest that planning practices should favor interconnections electrically closer to the slack generator. Overall, a high PV penetration level—modifying only the operating point of synchronous machines—allows longer fault durations to be tolerated; however, appropriate siting of PV units and the adoption of advanced inverter controls could mitigate the observed oscillations and post-fault operability challenges.

Abstract: This study tests TiO₂ and SiO₂ nanolubricants in PAG oil using a Mini Traction Machine and an Ultra Shear Viscometer. The loads were 20 N and 40 N. The entrainment speeds ranged from 2.5 to 500 mm per second. The slide to roll ratio ranged from 25 to 150 percent. The nanoparticle concentrations were 0.01, 0.03, and 0.05 percent. The ball size was 19.05 mm, and the disk was 46 mm. All tests ran at 40°C. Only 0.05% of samples lowered traction compared with PAG at fixed SRR. TiO₂ at 0.05% showed the largest drop, up to 4.89 percent at 20 N and 2.99 percent at 40 N. However, lower concentrations increased traction. All nanolubricants reduced wear. TiO₂ at 0.03 percent gave the lowest wear, with a reduction of about 35 µm at 40 N. Nanolubricant samples stayed between 40.2 and 40.5°C while PAG reached about 41.0°C. TiO₂ produced slightly lower temperatures than SiO₂. Ultra shear tests from 40 to 100°C showed shear thinning. TiO₂ at 0.05% kept the highest viscosity at 40 and 60°C, up to 12 percent above PAG. SiO₂ showed smaller changes. TiO₂ delivered better friction, wear, temperature, and viscosity performance. Overall, both nanolubricants at 0.03% suits refrigerations applications while the 0.05% suits high load or high shear use.

Abstract: Against the backdrop of global energy transition and carbon emission reduction, the scientific siting of electric vehicle (EV) charging stations has become a key issue constraining the sustainable development of the industry. To address the common shortcomings of existing research, such as single-objective bias and the tendency of traditional optimization algorithms to fall into local optima, this paper proposes a multi-objective siting optimization method that couples an improved NSGA-II algorithm with an improved TOPSIS model. First, a charging station location model is established with the dual objectives of minimizing total operator costs and maximizing user satisfaction, where user satisfaction comprehensively incorporates factors such as charging distance and queuing time. Second, at the algorithmic level, chaotic mapping, opposition-based learning, and adaptive crossover–mutation operators are introduced to enhance global search capability and solution diversity. Then, an improved entropy-weighted TOPSIS model is used to select the optimal compromise solution from the Pareto set, achieving objective weight determination and stabilized ranking outcomes. Finally, simulation experiments show that the proposed method outperforms the standard NSGA-II algorithm in both operating cost reduction and user satisfaction improvement, while also exhibiting superior performance in hypervolume (HV), inverted generational distance (IGD), and diversity metrics. The results verify that the integrated improved NSGA-II–TOPSIS framework provides an efficient, scientific, and interpretable decision-support tool for the planning of EV charging infrastructure.

Abstract: Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI long before electronics existed [1–9]. In this perspective, we treat insects as canonical edge-AI systems and translate their neurobiology and physiology into a concrete engineering stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries [1–9]. This architecture is realized through a neuromorphic Reflex Island built from spintronic and neuromorphic primitives, MRAM synapses for non-volatile, innate reflex memory, and spin-torque nano-oscillator (STNO) reservoirs for temporal processing—yielding instant-on, memory-centric reflexes compatible with emerging industrial roadmaps [10–16,56,62–67].We further formalize the thermoregulatory and respiratory strategies that allow insects to maintain nearly constant mechanical efficiency across a wide load range: active thoracic temperature control and Discontinuous Gas Exchange (DGC) [17–33]. These mechanisms motivate firmware-level “thermal-debt” and burst-budget controllers, contrasting sharply with the narrow best-efficiency islands of internal combustion engines and miniturbines [34–43]. We instantiate this integrated bio-inspired model in two concrete edge systems: an insect-like IFEVS thruster with nearly flat-band thermal efficiency over thrust, and a solar-assisted cargo e-bike equipped with an insect-inspired neuromorphic safety shell [6,11,14,28,58–61]. Across these examples we provide efficiency comparisons, latency and energy budgets, and safety-case hooks (fault taxonomies, WCET envelopes) aimed at guiding adoption in safety-critical domains.

Abstract: Additive manufacturing is one of the most efficient approaches for fabricating components with complex geometries. Among the wide variety of additive manufacturing technologies, extrusion-based processes using gel materials are attracting increasing attention from researchers. In this work, we consider the extrusion of gel materials for 3D printing and propose a method for calibrating additive manufacturing equipment based on optimization of the pre-extrusion and retraction parameters. Experimental studies were carried out on the extrusion of materials with different rheological properties. The capabilities of the proposed calibration method to improve printing quality are demonstrated using gel materials based on partially crosslinked sodium alginate with a viscosity of 1053 Pa·s. A multi-material 3D printing process was also implemented, enabling the combination of different materials within a single fabrication process. To realize the proposed 3D printing approach, we present a custom-built setup that allows the use of two extrusion modules for materials with different physicochemical properties. The capabilities of the developed system are demonstrated by fabricating a structure with an internal hollow channel and a structure based on a sodium alginate–chitosan polyelectrolyte complex.

Abstract: The design of aircraft components is a complex process that must simultaneously ac-count for environmental impact, manufacturability, cost and structural performance to meet modern regulatory requirements and sustainability objectives. When these factors are integrated from the early design stages, the approach transcends traditional eco-design and becomes a genuinely sustainability-oriented design methodology. This study proposes a sustainability-driven design framework for aircraft components and demonstrates its application to a fuselage panel consisting of a curved skin, four frames, seven stringers, and twenty-four clips. The design variables investigated in-clude the material selection, joining methods, and subcomponent thicknesses. The de-sign space is constructed through a combinatorial generation process coupled with compatibility and feasibility constraints. Sustainability criteria are evaluated using a combination of parametric Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) regression models, parametric Finite Element Analysis (FEA), and Random Forest surrogate modeling trained on a stratified set of simulation results. Two methodolog-ical pathways are introduced: 1. Cluster-based optimization, involving customized clustering followed by multi-criteria decision-making (MCDM) within each cluster. 2. Global optimization, performed across the full decision matrix using Pareto front analysis and MCDM techniques. A stability analysis of five objective-weighting methods and four normalization techniques is conducted to identify the most robust methodological configuration. The results—based on a full cradle-to-grave assessment that includes the use phase over a 30-year A319 aircraft operational lifetime—show that the thermoplastic CFRP panel joined by welding emerges as the most sustainable design alternative.

Abstract: Understanding CO₂ transport in fractal porous media requires models capable of capturing multi-scale structural variability and temporal correlations inherent to complex geological formations. In this work, we develop a mechanistic stochastic framework based on wavelet-assisted damped fractional Brownian motion (WA-DFBM) to describe CO₂ migration and diffusion across fractal pore structures. The method integrates multi-resolution wavelet decomposition with the long-range dependence and damping characteristics of fractional Brownian motion, enabling simultaneous representation of microscopic heterogeneity, temporal memory, and dissipative effects. The resulting WA-DFBM framework reproduces key transport signatures observed in porous media, including anomalous diffusion, non-stationary fluctuations, and scale-dependent variance evolution. Comparison with conventional Brownian-based models demonstrates that WA-DFBM provides enhanced capability for representing multi-scale pore heterogeneity and dynamic variability. This approach offers improved mechanistic insight into CO₂ transport behavior in fractal porous media and establishes a generalized modeling framework applicable to a wide range of subsurface flow and transport problems.

Abstract: Vehicle Ad-Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANETs framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90-120ms vs 150-300ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000-2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment.

Abstract: Population aging is increasing dementia care demand. We present an audio-driven monitoring pipeline that operates either on mobile phones, microcontroller nodes, or smart television sets. The system combines audio signal processing with AI tools for structured interpretation. Preprocessing includes voice activity detection, speaker diarization, automatic speech recognition for dialogs, and speech-emotion recognition. An audio classifier detects home-care–relevant events (cough, cane taps, thuds, knocks, and speech). A large language model integrates transcripts, acoustic features, and a consented household knowledge base to produce a daily caregiver report covering orientation/disorientation (person, place, and time), delusion themes, agitation events, health proxies, and safety flags (e.g., exit seeking and falling). The pipeline targets real-time monitoring in homes and facilities, and it is an adjunct to caregiving, not a diagnostic device. Evaluation focuses on human-in-the-loop review, various audio/speech modalities, and the ability of AI to integrate information and reason. Intended users are low-income households in remote settings where in-person caregiving cannot be secured, enabling remote monitoring support for older adults with dementia.

Abstract: Motor position sensors are critical parts for traction motors control in electrified automotive powertrain. As motors are getting more compact due to the advance of technology the packaging space for motor position sensors is becoming increasingly restricted. This study presents a two-layer (2L) printed circuit board (PCB) routing strategy for inductive motor position sensors with limited area. A prototype was fabricated and tested on a test bench using a comprehensive design of experiments that contains 625 combinations of X- and Y-offsets, tilt angle, and airgap at various levels (±0.5 mm in X/Y, ±0.5° tilt, 1.9–3.1 mm airgap). Across the tolerance box, the accuracy under all test cases remained within ±1 electrical degree. The accuracy analysis through Fourier series on circle shows that the DC offset and magnitudes mismatch of the 3 Rx signals are the dominant error contributors due to the routing modification. An Extreme Gradient Boosting (XGBoost) model was trained and validated with R² = 0.9951 on the sensor data. The SHapley Additive exPlanations (SHAP) analysis identified tilt and Y-offset as dominant contributors to accuracy degradation. The model revealed a mild Y-axis asymmetry introduced by routing modifications. The SHAP results show that machine learning workflow provides a general, quantitative framework for analyzing inductive sensor layouts and installation tolerances.

Abstract: The development of autonomous cargo ships necessitates reliable anchoring operations, a critical challenge due to low-speed maneuverability issues and anchorage disturbances. This paper addresses the challenges of reduced maneuverability at low speeds and susceptibility to anchorage disturbances in autonomous cargo ships, conducting research on anchoring decision-making methods. The process was systematically analyzed. Safety anchorage areas were quantified using ship parameters and environmental data. An available anchor position identification method based on grid theory, combined with an anchorage allocation mechanism to derive optimal anchorage selection. The development of a multi-level guided anchoring trajectory planning algorithm was informed by practical anchoring. This algorithm was designed to facilitate the scientific calculation of turning and stopping guidance points, with the objective of guiding cargo ship to navigate towards the anchorage in a predetermined attitude. An integrated autonomous anchoring system was constructed, encompassing perception, decision-making, planning, and control. Digital simulations verified the system's effectiveness and robustness under complex sea conditions. This study provides a theoretical foundation and feasible technical approach for enhancing the autonomous decision-making and safety control capabilities of intelligent ships during anchoring operations.

Abstract: In this study, the dyeing kinetics of polyamide fabrics with acid dyes, Telon Blue M2R, under both conventional and microwave-assisted heating conditions were comprehensively investigated. While the conventional dyeing reaction was completed in 30 minutes, microwave-assisted dyeing was performed in the microwave device for 10 minutes. Dyeing kinetics were investigated as a function of reaction time, reaction concentration and dyeing temperatures. The K/S values (color depth) of the dyed fabrics were correlated with the concentration. A significant reduction in the dyeing process time for polyamide fabric was observed with microwave heating compared to the conventional method. Kinetic analysis revealed that the PSO kinetic model provides a better fit to the experimental data on the diffusion process of acid dye in polyamide fabrics, as evidenced by higher correlation coefficients (R²) compared to the PFO model. The activation energy of the reaction in dyeing was found to be 63.27 kJ/mol, and the Arrhenius constant was determined as 7,20 x 10¹⁰ L/g.min in conventional media and 18,70 x 10¹⁰ L/g.min in microwave media. The Arrhenius factor in the microwave medium was more than two times higher than in the conventional one.

Abstract: This paper proposes an analog retuning strategy that strengthens the functional longevity of photovoltaic (PV) systems operating within circular-economy environments. Although PV modules can be relocated from large generation sites to low-demand rural or remote settings, their electrical behavior offers no adjustable quantities capable of extending service duration. In many cases, even after formal disposal or decommissioning, these solar panels still retain a considerable portion of their energy-generation capability and can operate for many additional years before their output becomes negligible, making second-life deployment both technically viable and economically attractive. In contrast, the associated power-electronic converters contain modifiable gate-driver parameters that can be reconfigured to moderate transient phenomena and lessen device stress. The method introduced here adjusts the external gate resistance in conjunction with coordinated switching-frequency adaptation, reducing overshoot, ringing, and steep dv/dt slopes while preserving the original switching-loss budget. A unified analytical framework connects stress mitigation, ripple evolution, and projected lifetime enhancement, demonstrating that deliberate analog tuning can substantially increase the endurance of aged semiconductor hardware without compromising suitability for second-life PV applications. Experimental results validated the study, confirming the effectiveness of the proposed approach for long-term deployment.

Abstract: As an emerging technology, three-dimensional (3D) printing is gaining publicity among companies, manufacturers, and individuals to fabricate prototypes, parts, and samples. 3D printing can be used in various fields such as engineering, healthcare, education, etc. This study investigates the influence of FDM 3D printing parameters such as infill patterns (line, triangle, cubic, and gyroid), infill densities (20%, 60%, and 100%), layer thicknesses (0.1, 0.2, and 0.3 mm), and temperature according to the minimum and maximum manufacturer recommendations. This study investigated the most common filament used in FDM 3D printing, which is polylactic acid (PLA). Mechanical tests were performed on the 3D printed parts, which are uniaxial tensile test and 3-point bending test according to ASTM D638 Type-I and ISO 178 standards, respectively. Modulus of elasticity (E), fracture point (σ_F), maximum stress (σ_Max), and yield strength (σ_Y) were obtained from the uniaxial tensile test. And for the 3-point bending test, strain at maximum load (ɛ), modulus of elasticity (E_bending), and flexural strength (σ_fMax) were obtained. This study showed that infill density and pattern are dominant factors in mechanical performance, while layer thickness and printing temperature provide fine-tuning effects.

Abstract: We study minimum-time heliocentric transfers for a spacecraft propelled by an electric thruster that draws constant electrical power P while continuously varying its exhaust speed (variable Isp). The vehicle is assumed to depart from and arrive on circular heliocentric orbits (i.e., initial and final velocities match the local circular velocity at the respective radii). First, we derive an analytic solution of the one-dimensional, gravity-free brachistochrone and discuss how a finite exhaust-speed ceiling modifies the solution, producing a boost–coast–brake structure. Next, we formulate the full planar Sun-field optimal-control problem, derive two closed-form first integrals, and show that the indirect formulation reduces to a seven-dimensional boundary-value problem. Finally, we present a practical numerical continuation strategy that obtains a coarse feasible endpoint via global optimization and then refines it by homotopy and Powell’s local solver. Numerical examples for a 1GW engine with an initial/dry mass of 3000 t→1000 t demonstrate Earth–Jupiter-class transfers in roughly 200–220 days that commonly exploit a solar Oberth pass. Reproducible code and data are available at the project repository.