Search Results (186,182)

Accurate trajectory tracking is a key requirement in unmanned ground vehicles (UGVs) operating in autonomous driving, mobile robotics, and industrial automation. In wireless Time-Sensitive Networking (WTSN) scenarios, trajectory accuracy strongly depends on deterministic packet delivery, precise traffic scheduling, and time synchronization among distributed devices. This paper quantifies the impact of IEEE 802.1Qbv time-aware traffic scheduling on trajectory tracking accuracy in UGVs operating over Wi-Fi-enabled TSN networks. The analysis focuses on how misconfigured real-time (RT) and best-effort (BE) transmission windows, as well as clock misalignment between devices, affect packet reception and control performance. A mathematical framework is introduced to predict the number of correctly received RT packets based on cycle time, packet periodicity, scheduling window lengths, and synchronization offsets, enabling the a priori dimensioning of RT and BE windows. The proposed model is validated through extensive simulations conducted in an ROS–Gazebo environment, utilising Linux-based traffic shaping and scheduling tools. Results show that improper traffic scheduling and synchronization offsets can significantly degrade trajectory tracking accuracy, while correctly dimensioned scheduling windows ensure reliable packet delivery and stable control, even under imperfect synchronization. The proposed approach provides practical design guidelines for configuring wireless TSN networks supporting real-time trajectory tracking in mobile robotic systems. Full article

With the rapid development of the shipping industry, the collision risk among ships in open waters has been steadily increasing, making effective multi-ship collision avoidance decision-making a critical issue for ensuring navigational safety. This paper proposes a multi-ship collision avoidance decision-making method based on the COLREGs. First, a fuzzy comprehensive evaluation method is used to construct a collision risk index model. Then, considering navigational safety, COLREG compliance, turning amplitude, and path economy, an objective function for ship collision avoidance is formulated. Next, the AVOA is improved by incorporating SA to simulate the foraging and navigation behavior of vultures. The Metropolis acceptance criterion is applied to help the algorithm escape local optima and enhance global search capabilities. Experiments conducted in the VSC simulation environment show that the proposed method significantly improves decision-making performance in multi-ship encounter scenarios compared to the standard AVOA. Full article

Conventional gelatin capsules deliver a rapid drug release in the stomach, which is suboptimal for therapies requiring controlled and delayed release, emphasizing the need for customizable drug delivery systems for precision medicine. This study’s objective was to optimize 3D-printed capsule shells formulated with pH-responsive polymer blends—hydroxypropyl methylcellulose acetate succinate (HPMC-AS), PEG-4000, and PVA—to achieve controlled and sustained drug release, comparing profiles against a commercial enteric capsule. Capsule shells were produced via fused filament fabrication (FFF) at two ratios (80:15:5 and 70:20:10), filled with acetaminophen (250 mg), and tested using a two-stage dissolution method (simulated gastric fluid (SGF) for 2 h followed by simulated intestinal fluid (SIF) for 4–5 h). Results showed negligible drug release in SGF (≤5%) for both printed and commercial capsules. However, in SIF, the commercial capsule released its payload rapidly (>80% within 15 min), while the 3D-printed capsules achieved a prolonged, gradual release. The higher HPMC-AS content significantly extended the release duration. All capsules met the pharmacopeial weight uniformity criteria. In conclusion, the 3D-printed shells provided a controllable, sustained drug release profile, underscoring 3D printing’s potential to create tunable, patient-specific dosage forms. Full article

Compared to Arikan’s $G_2$ kernel, large-kernel polar codes exhibit higher polarization rates and superior error correction performance. The critical steps of exact successive cancellation (SC) decoding for such codes can be implemented via trellis-based computations to reduce complexity. However, the complexity remains high for large kernels. This paper proposes a permutation-based trellis optimization scheme. The approach builds on the Massey minimal trellis and reorders its time axis to find a permutation that minimizes the number of trellis edges, thereby further reducing the exact SC decoding complexity. For smaller kernels ($G_3$–$G_{12}$), an exhaustive search is conducted to identify the optimal trellis. For larger kernels ($G_{13}$–$G_{16}$), where an exhaustive search becomes infeasible due to the factorial growth of the permutation space, an ant colony optimization (ACO)-based method is employed to find a near-optimal permutation. Simulation results show that the permutation-optimized trellis lowers the direct SC decoding complexity drastically. Furthermore, compared to the $l$-expression, the $W$-formula and original Massey trellis methods, it achieves multiplication operation reductions of up to 99.2%, 58.1%, and 56.5%, respectively. The improvement is particularly beneficial for large kernels, where traditional decoding methods become computationally prohibitive. Full article

Aiming at the problems of large cutting force, easy honeycomb tearing, and deformation during the traditional cutting process of aramid honeycomb materials and an increase in cutting temperature during continuous processing, which may lead to vibration stoppage, the ultrasonic cutting process characteristics of aramid honeycomb materials were studied. Firstly, torsional vibration was added on the basis of one-dimensional longitudinal ultrasonic vibration cutting (LUC), and the motion characteristics of longitudinal–torsional ultrasonic vibration cutting (LTUC) were analyzed. Secondly, a cutting simulation model was established using finite element simulation software. Under the same cutting parameters, the simulation results for the cutting force and cutting temperature of longitudinal ultrasonic vibration cutting and longitudinal–torsional compound ultrasonic vibration cutting were compared. Then, cutting experiments were conducted to verify the simulation results for cutting force, and single-factor experiments were used to analyze the cutting quality of aramid honeycomb under different processing methods. The results show that the three-directional cutting forces in longitudinal–torsional ultrasonic vibration processing are significantly lower than those in longitudinal ultrasonic vibration processing. The feed force decreased by an average of 28.2%, the tangential force decreased by an average of 45.8%, the axial force decreased by an average of 31.2%, and the tool temperature decreased by 21%. The processing quality of aramid honeycomb using longitudinal–torsional ultrasonic vibration cutting is better than when using longitudinal ultrasonic vibration cutting, which can more effectively reduce cutting stress, cutting force, and tool cutting temperature and show better process characteristics. Full article

Distribution shifts commonly arise in real-world machine learning scenarios in which the fundamental assumption that training and test data are drawn from independent and identically distributed samples is violated. In the case of medical data, such distribution shifts often occur during data acquisition and pose a significant challenge to the robustness and reliability of artificial intelligence systems in clinical practice. Additionally, quantifying these shifts without training a model remains a key open problem. This paper proposes a comprehensive methodological framework for evaluating the impact of such shifts on medical image datasets under artificial transformations that simulate acquisition variations, leveraging the Cumulative Spectral Gradient (CSG) score as a measure of multiclass classification complexity induced by distributional changes. Building on prior work, the proposed approach is meaningfully extended to twelve 2D medical imaging benchmarks from the MedMNIST collection, covering both binary and multiclass tasks, as well as grayscale and RGB modalities. We evaluate the metric analyzing its robustness to clinically inspired distribution shifts that are systematically simulated through motion blur, additive noise, brightness and contrast variation, and sharpness variation, each applied at three severity levels. This results in a large-scale benchmark that enables a detailed analysis of how dataset characteristics, transformation types, and distortion severity influence distribution shifts. Thus, the findings show that while the metric remains generally stable under noise and focus distortions, it is highly sensitive to variations in brightness and contrast. On the other hand, the proposed methodology is compared against Cleanlab’s widely used Non-IID score on the RetinaMNIST dataset using a pre-trained ResNet-50 model, including both class-wise analysis and correlation assessment between metrics. Finally, interpretability is incorporated through class activation map analysis on BloodMNIST and its corrupted variants to support and contextualize the quantitative findings. Full article

Desert–Gobi–wasteland regions possess abundant wind resources and are strategic areas for future renewable energy development and meteorological monitoring. However, existing studies have limited capability in addressing the highly complex and dynamic environmental characteristics of these regions. In particular, few modeling approaches can jointly represent terrain variability, solar radiation distribution, and wind-field characteristics within a unified framework. Moreover, conventional deep reinforcement learning methods often suffer from learning instability and coordination difficulties when applied to multi-agent layout optimization tasks. To address these challenges, this study constructs a multidimensional environmental simulation model that integrates terrain, solar radiation, and wind speed, enabling a quantitative and controllable representation of the meteorological monitoring network layout problem. Based on this environment, an Environment-Aware Proximal Policy Optimization (EA-PPO) algorithm is proposed. EA-PPO adopts a compact environment-related state representation and a utility-guided reward mechanism to improve learning stability under decentralized decision-making. Furthermore, a Global Layout Optimization Algorithm based on EA-PPO (GLOAE) is developed to enable coordinated optimization among multiple monitoring nodes through shared utility feedback. Simulation results demonstrate that the proposed methods achieve superior layout quality and convergence performance compared with conventional approaches, while exhibiting enhanced robustness under dynamic environmental conditions. These results indicate that the proposed framework provides a practical and effective solution for intelligent layout optimization of meteorological monitoring networks in desert–Gobi–wasteland regions. Full article

This paper presents a novel broadband circularly polarized optically transparent monopole antenna using indium tin oxide (ITO) and PMMA. The proposed design successfully integrates ultra-wideband circular polarization characteristics with exceptional optical transparency. The antenna, constructed with a three-layer configuration utilizing ITO films as both the radiating patch and ground plane, along with transparent PMMA serving as the substrate, features compact dimensions of 40 × 40 × 1 mm³. By leveraging a co-optimized design incorporating a slotted hexagonal-ring radiating patch, triangular perturbation ground plane, and stepped-impedance feeding structure, the antenna achieves a circularly polarized operating bandwidth of 2.8–6.6 GHz (fractional bandwidth of 77.9%), with an axial ratio < 3 dB and return loss < −15 dB. The experimental findings exhibit strong consistency with the simulations, illustrating a high level of visible-light transmittance and radiation patterns characterized by right-hand circular polarization in the positive z-axis direction (+z) and left-hand circular polarization in the negative z-axis direction (−z). This innovative antenna shows great potential for applications in smart windows, display integration, and 5G communication systems. Full article

Declining recovery factors from mature oil fields, coupled with the technical challenges of recovering residual oil under harsh reservoir conditions, necessitate the development of advanced enhanced oil recovery (EOR) techniques. While promising, chemical EOR often faces economic and technical hurdles in high-salinity, high-temperature environments where conventional polymers like hydrolyzed polyacrylamide (HPAM) degrade and fail. This study presents a comprehensive numerical investigation that addresses this critical industry challenge by applying a rigorously calibrated simulation framework to evaluate a novel hybrid EOR process that synergistically combines an ionic liquid (IL) with HPAM polymer. Utilizing core-flooding data from a prior study that employed the same Berea sandstone core plug and Saudi medium crude oil, supplemented by independently measured interfacial tension and contact angle data for the same chemical system, we built a core-scale model that was history-matched with RMSE < 2% OOIP. The calibrated polymer transport parameters—including a low adsorption capacity (~0.012 kg/kg-rock) and a high viscosity multiplier (4.5–5.0 at the injected concentration)—confirm favorable polymer propagation and effective in -situ mobility control. Using this validated model, we performed a systematic optimization of key process parameters, including IL slug size, HPAM concentration, salinity, temperature, and injection rate. Simulation results identify an optimal design: a 0.4 pore volume (PV) slug of IL (Ammoeng 102) reduces interfacial tension and shifts wettability toward water-wet, effectively mobilizing residual oil. This is followed by a tailored HPAM buffer in diluted formation brine (20% salinity, 500 ppm), which enhances recovery by up to 15% of the original oil in place (OOIP) over IL flooding alone by improving mobility control and enabling in-depth sweep. This excellent history match confirms the dual-displacement mechanism: microscopic oil mobilization by the IL, followed by macroscopic conformance improvement via HPAM-induced flow diversion. This integrated simulation-based approach not only validates the technical viability of the hybrid IL–HPAM flood but also delivers a predictive, field-scale-ready framework for heterogeneous reservoir systems. The work provides a robust strategy to unlock residual oil in such challenging reservoirs. Full article

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO₂ emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments. Full article

This study provides a detailed technical and sustainability comparison of the conventional CNC machining and additive manufacturing routes for an aerospace bearing bracket. The work integrates material selection, process parameterization, build simulation, and environmental–economic assessment within a single framework. For the CNC route, machining of Al 7175-T7351 is characterized through process sequencing, tooling requirements, and waste generation. For the Laser Powder Bed Fusion (LPBF) route, two build strategies, single-part distortion-minimized and multi-part volume-optimized, are developed using Siemens NX for orientation optimization and Atlas3D for thermal and recoater collision simulations. The mechanical properties of Al 7175-T7351 and Scalmalloy^® are compared to justify material selection for aerospace applications. Both the experimental and simulation-derived process metrics are reported, including the build time, support mass, energy consumption, distortion tolerances, and buy-to-fly (B2F) ratio. CNC machining exhibited a B2F ratio of 1:7, with cradle-to-gate CO₂ emissions of ~11,000 g and an energy consumption exceeding 100 kWh per component. In contrast, both LPBF strategies achieved a B2F ratio of 1:1.2, reducing CO₂ emissions by over 90% and energy consumption by up to 63%. Build volume optimization further reduced the LPBF unit cost by over 50% relative to the CNC machining. Use-phase analysis in an aviation context indicated estimated lifetime fuel savings of 776,640 L and the avoidance of 2328 tons of CO₂ emissions. The study demonstrates how simulation-guided build preparation enables informed sustainability-driven decision-making for manufacturing route selection in aerospace applications. Full article

This paper presents a simulation framework for analysing race car overtaking manoeuvres under aerodynamic wake effects using optimal control theory. The proposed formulation integrates wake-dependent aerodynamic disturbances into a spatial-domain optimal control problem, enabling simultaneous optimisation of racing line and control inputs. A planar vehicle model representative of a modern FIA Formula 3 car is employed and calibrated using real telemetry data obtained from Campos Racing. Wake effects are modelled as distance- and offset-dependent aerodynamic loss factors that influence drag, downforce, and aerodynamic balance of the following vehicle. The framework is implemented using the Dymos optimal control library and applied to single-car and two-car overtaking scenarios on a closed circuit. Simulation results demonstrate that wake effects significantly modify optimal braking points, corner entry trajectories, and corner-exit strategies. Moreover, we show that optimal overtaking requires deliberate lateral deviations from the wake core to recover downforce and traction. The study highlights the importance of incorporating aerodynamic interaction effects into trajectory optimisation when analysing performance-critical motorsport manoeuvres. Full article

This study aims to optimize the voltage profile of the grid by obtaining an optimum level of reactive power support from photovoltaic (PV) plants, thereby enhancing the efficiency of PV systems in power distribution networks and ensuring grid stability. Initially, voltage profiles in the sector, together with the structure and operating principles of PV plants, were considered in detail. Subsequently, the limits of reactive power support that can be provided by PV plants were determined. Then, the optimum levels of reactive power from the plants were determined using particle swarm optimization, genetic algorithm, Jaya algorithm, and firefly algorithm separately. The algorithms were tested through simulations conducted on a power distribution system operator in Türkiye. Additionally, a Modbus-based communication application was developed and tested, as a feasibility demonstration, to verify PV inverter accessibility and the capability of remotely writing reactive power reference setpoints. The quantitative optimization results reported in this manuscript are obtained from DIgSILENT PowerFactory simulations using the actual feeder model and time-series profiles. The results have revealed that PV plants can be effectively utilized as reactive power compensators to contribute to the operation of the grid under more ideal voltage profile conditions. In Türkiye, there is no regulatory or market mechanism to support reactive power provision from PV plants. Therefore, this study is novel in the Turkish market. The experimental results confirm that power generation from renewable energy can provide reactive support effectively when needed, which reveals that this approach is both technically feasible and practically relevant. Full article

The increasing penetration of renewable energy leads to a continuous reduction in system inertia, for which conventional synchronization criteria based solely on frequency consistency can no longer accurately capture the coupled dynamics of frequency and voltage during transients. To address this issue, this paper employs the concept of complex frequency and develops an analysis framework that integrates theory, indices, and simulation for assessing synchronization stability in low-inertia power systems. Firstly, the basic concepts and mathematical formulation of complex frequency and complex frequency synchronization are introduced. Then, dynamic criteria for local and global complex synchronization are established, upon which a complex inertia index is proposed. This index unifies the supporting role of traditional frequency inertia and the voltage support capability associated with voltage inertia, enabling the quantitative evaluation of the strength of coordinated frequency–voltage support and disturbance rejection within a region. Finally, transient simulations on a modified WSCC nine-bus system are carried out to validate the proposed method. The results show that the method can clearly reveal the synchronization relationships between subnetworks and the overall system, providing a useful theoretical reference for stability analysis and control strategy design in low-inertia power systems. Full article

Fibre reinforced cementitious matrix (FRCM) systems are composite materials that are increasingly used for retrofitting masonry and reinforced concrete structures. Their behaviour does not depend only on the mechanical properties of the fibres and the matrix. Therefore, it is essential to perform tensile tests on FRCM coupons, as well as additional tests to investigate whether the interaction between the FRCM system and the substrate can be considered a perfect bond. The aim of this paper is to numerically simulate the behaviour of concrete beams retrofitted with two FRCM composite systems assuming perfect bond. The results of the numerical simulations were compared with experimental data, and it was observed that the adopted models successfully capture the cracking behaviour of both the concrete and the FRCM, as well as overall structural response of the specimens. The main finding was that the behaviour of concrete beams retrofitted with FRCM can be effectively estimated using a macro-modelling approach in numerical simulations. The ultimate load obtained experimentally is between 2% and 20% higher than the numerical value. This is safe and accurate enough for engineering purposes. Full article