Search Results (1,013)

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments. Full article

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

Multi-layer cell constructs produced in vitro are an innovative treatment option to support the growing demand for therapy in regenerative medicine. Our research introduces a novel construct integrating organ-derived decellularised extracellular matrix (dECM) hydrogels and 3D-printed biodegradable polymer meshes composed of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P34HB) to support and maintain multiple layers of different cell types. We achieved that by integrating the mechanical stability of PHBV+P34HB, commonly used in the food storage industry, with a dECM hydrogel, which replicates organ stiffness and supports cellular survival and function. The construct was customised by adjusting the fibre arrangement and pore sizes, making it a suitable candidate for a personalised design. We showed that the polymer is degradable after precoating it with PHB depolymerase (PhaZ), with complete degradation achieved in 3–5 days and delayed by adding the hydrogel to 10 days, enabling tuneable degradation for regenerative medicine applications. Finally, as a proof of concept, we composed a three-layered tissue in vitro; each layer represented a different tissue type: epidermal, vascular, and subcutaneous layers. Possible future applications include wound healing and diabetic ulcer paths, personalised drug delivery systems, and personalised tissue implants. Full article

This study employs molecular dynamics (MD) simulations to investigate the mechanical response and fracture behavior of penta-graphene, a novel two-dimensional carbon allotrope composed entirely of pentagonal rings with mixed sp²–sp³ hybridization and pronounced mechanical anisotropy. Atomistic simulations are carried out to evaluate the impact of structural defects on mechanical performance and to elucidate crack propagation mechanisms. The results reveal that void defects involving sp³-hybridized carbon atoms cause a more significant degradation in mechanical strength compared to those involving sp² atoms. During fracture, local atomic rearrangements and bond reconstructions lead to the formation of energetically favorable ring structures—such as hexagons and octagons—at the crack tip, promoting enhanced energy dissipation and fracture resistance. A central focus of this work is the evaluation of the critical stress intensity factor (SIF) under mixed-mode (I/II) loading conditions. The simulations demonstrate that the critical SIF is influenced by the loading phase angle, with pure mode I exhibiting a higher SIF than pure mode II. Notably, penta-graphene shows a critical SIF significantly higher than that of graphene, indicating exceptional fracture toughness that is rare among ultra-thin two-dimensional materials. This enhanced toughness is primarily attributed to penta-graphene’s capacity for substantial out-of-plane deformation prior to failure, which redistributes stress near the crack tip, delays crack initiation, and increases energy absorption. Additionally, the study examines crack growth paths as a function of loading phase angle, revealing that branching and kinking can occur even under pure mode I loading. Full article

We propose Observer-Linked Branching (OLB), a mathematically rigorous extension of quantum theory in which an observer’s cognitive commitment actively modulates collapse dynamics at macroscopic scales. The OLB framework rests on four axioms, employing a norm-preserving nonlinear Schrödinger evolution and Lüders-type projection triggered by crossing a cognitive commitment threshold. Our expanded formalism provides five main contributions: (1) deriving Lie symmetries of the observer–environment interaction Hamiltonian; (2) embedding OLB into the Consistent Histories and path-integral formalisms; (3) multi-agent network simulations demonstrating intentional synchronisation toward shared macroscopic outcomes; (4) detailed statistical power analyses predicting measurable biases (up to ~5%) in practical experiments involving traffic delays, quantum random number generators, and financial market sentiment; and (5) examining the conceptual, ethical, and neuromorphic implications of intent-driven reality selection. Full reproducibility is ensured via the provided code notebooks and raw data tables in the appendices. While the theoretical predictions are precisely formulated, empirical validation is ongoing, and no definitive field results are claimed at this stage. OLB thus offers a rigorous, norm-preserving and falsifiable framework to empirically test whether cognitive engagement modulates macroscopic quantum outcomes in ways consistent with—but extending—standard quantum predictions. Full article

In the field of modern optical communication, radar signal processing and optical sensors, true time delay technology, as a key means of signal processing, can achieve the accurate control of the time delay of optical signals. This study presents a novel design that integrates a 2 × 2 Multi-Mode Interference (MMI) structure with a Mach–Zehnder modulator on a silicon nitride–lithium niobate (SiN-LiNbO₃) heterogeneous integrated optical platform. This configuration enables the selective interruption of optical wave paths. The upper path passes through an ultralow-loss Archimedes’ spiral waveguide delay line made of silicon nitride, where the five spiral structures provide delays of 10 ps, 20 ps, 40 ps, 80 ps, and 160 ps, respectively. In contrast, the lower path is straight through, without introducing an additional delay. By applying an electrical voltage, the state of the SiN-LiNbO₃ switch can be altered, facilitating the switching and reconfiguration of optical paths and ultimately enabling the combination of various delay values. Simulation results demonstrate that the proposed optical true delay line achieves a discrete, adjustable delay ranging from 10 ps to 310 ps with a step size of 10 ps. The delay loss is less than 0.013 dB/ps, the response speed reaches the order of ns, and the 3 dB-EO bandwidth is broader than 67 GHz. In comparison to other optical switches optical true delay lines in terms of the parameters of delay range, minimum adjustable delay, and delay loss, the proposed optical waveguide digital adjustable true delay line, which is based on an optical switch and an Archimedes’ spiral structure, has outstanding advantages in response speed and delay loss. Full article

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

Some construction projects in Indonesia have been experiencing frequent major delays, and there is an urgent need to perform a comprehensive schedule forecast to anticipate them. This study aims to enhance the forecast of the project-completion schedule of a major railway construction project in Indonesia, with considerations of the identified project risks. Using structured interviews of practitioners and experts, this study has identified key risks in the Phase II Construction project. The @RISK V8.5.1 software was used to run Monte Carlo Simulation to calculate the scheduling forecast more accurately with the PERT method. The results of the scheduling simulation on the inherent key risks that affect key activities in the critical path show that there are additional days due to pre-mitigation during the construction of the project, ranging from 130.81% to 136.25% of the initial contract completion duration to approximately 95.82% to 102.23% of the original contract completion duration. This research provides a way not only to identify and prioritize the risks but also to quantify them to estimate the project completion schedule more up to 95% accuracy using Monte Carlo Simulation, where the model can be used to plan and monitor the risks of future projects. Full article

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions. Full article

Ascon is a family of lightweight cryptographic algorithms designed for Authenticated Encryption with Associated Data (AEAD), hashing, and Extendable Output Functions (XOFs) in resource-constrained environments. While the AEAD variants of Ascon provide confidentiality and authenticity, they do not inherently detect replayed messages. This work presents an FPGA implementation of Ascon-128, the primary AEAD variant, on a Xilinx Artix-7 device with integrated replay detection. A 128-bit Linear Feedback Shift Register (LFSR) is used to generate a unique sequential nonce per encryption, enabling high-speed, stateless nonce generation with minimal logic complexity. At the decryption end, replay detection is performed by hashing the received nonce using Ascon-XOF128 and verifying its freshness via a Bloom Filter stored in on-chip Block RAM (BRAM). Leveraging the flexibility of Ascon-XOF128 to generate variable length outputs, our design derives all ten Bloom Filter indices from a single 256-bit XOF output using the same permutation core as the AEAD data path, thereby eliminating the need for additional hashing logic. The Bloom Filter ensures zero false negatives, and our configuration achieves a low False Positive Rate (FPR) of 0.77% theoretically and 0.17% empirically after testing 100,000 nonces, consistent with analytical models. Replay detection is fully overlapped with decryption and introduces no additional delay for messages of 64 bytes or more when using the optimized two Rounds Per Clock Cycle (RPCC) permutation core operating at 100 MHz. This architecture extends Ascon with hardware-based replay protection, offering a lightweight and scalable security solution for practical IoT deployments. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

The computational complexity of convolutional neural networks (CNNs) becomes challenging for resource-constrained hardware devices. The convolution layer is predominant in the overall CNN architecture, performing the expensive multiplication and accumulation operation. Therefore, designing a hardware-efficient convolution layer will effectively improve the overall performance of a CNN. In this research, we propose a dynamic resource optimization (DROP) approach to improve the power and delay of the convolution layer. The proposed approach controls the computational path in accordance to the interrupts which are dependent on a non-zero-bit pattern. With a single interrupt, our solution provides 42.5% power and 36.7% delay efficiency compared to the standard bit-serial-parallel approach. Moreover, the power consumed by eight parallel functioning blocks is 27.7% less than the traditional bit-parallel approach. Full article

This study examined the temporal relationship between fundamental movement skills (FMSs) and sport-specific skills (SSSs) in children aged 7 to 10. Based on the premise that FMSs are the basis for sport skills, we implemented a 10-week intervention program targeting two FMSs (running and stationary dribbling) and one SSS (speed dribbling), followed by immediate and long-term assessments. Using a path-modeling approach, we tested two models: one examining whether FMSs were associated with sport skill performance at the same time point and another exploring whether this influence emerged over time. Results revealed significant FMS and SSS improvements immediately after the intervention program. However, significant associations between the FMSs and SSS emerged only at later time points (8 to 20 months post-intervention), suggesting the delayed influence of the FMSs on the SSS. These findings support that while FMSs are essential for developing more complex skills, their effect may not be immediately observable, emphasizing the importance of long-term follow-up. The results also align with theoretical models contending that proficiency in FMS and sustained practice opportunities are key to integrating fundamental and sport-specific motor skills and may represent an important foundation for public health initiatives advocating early FMS interventions as a strategy to promote lifelong physical activity and sustained engagement in sports. Full article

A conflict is a situation where multiple stakeholders have different evaluations over possible scenarios or states. Conflict analysis is an essential tool for understanding and resolving complex conflicts, especially in scenarios involving multiple stakeholders and uncertainties. Confrontation analysis (ConAna) and graph model for conflict resolution (GMCR) have been integrated for dilemma reasoning and conflict resolution in region crisis analysis. This paper discusses the application of decision-theoretic rough sets (DTRS) to three-way decisions (3WD) in dilemma reasoning and conflict resolution. Three-way decisions are a strategy for making decisions under uncertain conditions, which compensates for the shortcomings of traditional two-way decisions (such as accept or reject) by introducing a “delayed decision” option. In terms of dilemma reasoning, we try to address incomplete or conflicting information and provide a more reasonable decision path for decision-makers through comprehensive evaluation of multi-criteria. In terms of conflict resolution, the DTRS model seeks a compromising solution that is acceptable to all parties by analyzing the game relationship between different stakeholders. The DTRS model combines decision-making theory and rough set theory to determine the balanced decision region by constructing a game between multiple criteria. This dynamic integration is of great significance for the study of complex international conflicts, providing a cross-disciplinary perspective for related research. In this paper, we demonstrate the application of DTRS in 3WD and discuss the relationship between DTRS and probabilistic rough sets. The research shows that the DTRS model has significant advantages in dealing with complex decision problems and can effectively deal with the conflicts and uncertainties in multi-criteria decision-making. Full article

With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have established fixed departure and arrival air routes above vertiports and designed fixed flight air routes between vertiports to guide UAVs to fly along predefined paths. In the complex and constrained low-altitude urban environment, the design of safe and efficient air routes has undoubtedly become a key enabler for successful operations. This research, grounded in both current theoretical research and real-world logistics UAV operations, defines the concept of UAV logistics air routes and presents a comprehensive description of their structure. A parametric model for one-way round-trip logistics air routes is proposed, along with an air route evaluation model and optimization method. Based on this framework, the research identifies four basic configurations that are commonly adopted for one-way round-trip operations. These configurations can be further improved into two optimized configurations with more balanced performance across multiple metrics. Simulation results reveal that Configuration 1 is only suitable for small-scale transport; as the number of delivery tasks increases, delays grow linearly. When the task volume exceeds 100 operations per 30 min, Configurations 2, 3, and 4 reduce average delay by 88.9%, 89.2%, and 93.3%, respectively, compared with Configuration 1. The research also finds that flight speed along segments and the cruise segment capacity have the most significant influence on delays. Properly increasing these two parameters can lead to a 28.4% reduction in the average delay. The two optimized configurations, derived through further refinement, show better trade-offs between average delay and flight time than any of the fundamental configurations. This research not only provides practical guidance for the planning and design of UAV logistics air routes but also lays a methodological foundation for future developments in UAV scheduling and air route network design. Full article