Search Results (5,085)

For multiple unmanned underwater vehicles (UUVs) systems, obstacle avoidance during cooperative operation in complex marine environments remains a challenging issue. Recent studies demonstrate the effectiveness of deep reinforcement learning (DRL) for obstacle avoidance in unknown marine environments. However, existing methods struggle in marine environments with complex non-convex obstacles, especially during multi-UUV cooperative operation, as they typically simplify environmental obstacles to convex shapes with sparse distributions and ignore the dynamic coupling between cooperative operation and collision avoidance. To address these limitations, we propose a centralized training with decentralized execution framework with a novel multi-agent dynamic encoder based on an efficient self-attention mechanism. The framework, to our knowledge, is the first to dynamically process observations from an arbitrary number of neighbors that effectively addresses multi-UUV collision avoidance in marine environments with complex non-convex obstacles while satisfying additional constraints derived from cooperative operation. Experimental results show that the proposed method effectively avoids obstacles and satisfies cooperative constraints in both simulated and real-world scenarios with complex non-convex obstacles. Our method outperforms typical collision avoidance baselines and enables policy transfer from simulation to real-world scenarios without additional training, demonstrating practical application potential. Full article

Satellite-based target positioning is vital for applications like disaster relief and precision mapping. Practically, satellite errors, e.g., thermal deformation and attitude errors, lead to a mix of fixed and random errors in the measured line-of-sight angles, resulting in a decline in target-positioning accuracy. Motivated by this concern, this study introduces a systematic error self-correction target-positioning method under continuous observations using a single video satellite. After analyzing error sources and establishing an error-inclusive positioning model, we formulate a dimension-extended equation estimating both target position and fixed biases. Based on the equation, a projection transformation method is proposed to obtain the linearized estimation of unknown parameters first, and an iterative optimization method is then utilized to further refine the estimate. Compared with state-of-the-art algorithms, the proposed method can improve positioning accuracy by 98.70% in simulation scenarios with large fixed errors. Thus, the simulation and actual data calculation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm effectively improves the target-positioning accuracy under non-ideal error conditions. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article

Water supply systems (WSSs) are energy-intensive infrastructure that present significant opportunities for decarbonization through the integration of renewable energy (RE). This study evaluated the RE generation potential within the WSSs of Incheon Metropolitan City (IMC), Republic of Korea, using a site-specific, data-driven approach. Three RE technologies were considered: solar photovoltaic (PV) systems installed in water-treatment plants (WTPs), micro-hydropower (MHP) utilizing the residual head at the inlet chamber of a WTP, and in-pipe MHP recovery using the discharge from water supply tanks in water distribution networks. Actual facility data, hydraulic simulations, and spatial analyses were used to estimate an annual RE generation potential of 32,811 MWh in the WSSs of IMC, including 18,830 MWh from solar PV in WTPs, 4938 MWh from MHP in WTPs, and 9043 MWh from in-pipe MHP. This corresponds to an energy self-sufficiency rate of approximately 22.3%, relative to the IMC WSS total annual electricity consumption of 147,293 MWh in 2022. The results demonstrated that decentralized RE deployment within existing WSSs can significantly reduce grid dependency and carbon emissions. This study provides a rare empirical benchmark for RE integration in large-scale WSSs and offers practical insights for municipalities seeking energy-resilient and climate-aligned infrastructure transitions. Full article

Next-generation communication systems demand extreme spectral efficiency to handle ever-increasing data traffic. The combination of faster-than-Nyquist (FTN) signaling and reconfigurable intelligent surfaces (RISs) presents a promising solution to meet this demand. However, the aggressive time compression inherent to FTN signaling introduces severe and highly non-linear inter-symbol interference (ISI). This complex distortion is challenging for conventional linear equalizers and even for recurrent neural network (RNN)-based detectors, which can struggle to model long-range dependencies within the signal sequence. To overcome this limitation, this paper proposes a novel signal detection framework based on the transformer model. By leveraging its core multi-head self-attention mechanism, the transformer globally analyzes the entire received signal sequence at once. This enables it to effectively model and reverse complex ISI patterns by identifying the most significant interfering symbols, regardless of their position, leading to superior signal recovery. The simulation results validate the outstanding performance of the proposed approach. To achieve a target bit error rate (BER) of ${10}^{-4}$, the transformer-based detector shows a significant signal-to-noise ratio (SNR) gain of approximately 1.5 dB over a Bi-LSTM detector over 4 dB compared to the conventional FTN-RIS system, while maintaining a high spectral efficiency of nearly 2 bps/s/Hz. Full article

The inversion factor is an important physical parameter for assessing and revealing the performance of semiconductor lasers, providing insights into the carrier-injected band-filling effect and radiation characteristics. In this paper, the carrier inversion factor (P_f) is measured to elucidate the luminescence mechanism of an InGaAs-based well-island composite (WIC) structure, formed by the self-assembly migration of indium atoms and exhibiting excellent spectral properties. P_f is obtained by collecting the amplified photoluminescence (PL) spectra from dual facets of the device, with carrier concentrations ranging from 9.0 × 10¹⁷ to 9.4 × 10¹⁷ cm⁻³. Compared with classical InGaAs/GaAs quantum well structures under the same operating conditions, the inversion level in the WIC structure can be as high as 2.2. Simulation results reveal enhanced quasi-Fermi-level separation and broadened spectral bandwidth. The research is of great significance in the development of new types of quantum-confined lasers with wide spectral output. Full article

The deposition of large-sized cuttings (or blocky cuttings) is a critical risk factor for stuck pipe incidents during the drilling of deep and extended-reach wells. This risk is particularly pronounced in well sections with long borehole trajectories and low drilling fluid return velocities, where it poses a substantial threat to wellbore cleanliness and the safe operation of the drill string. This study utilizes a self-developed visual experimental platform to simulate the deposition evolution of large-sized cuttings (20–40 mm in diameter) in the annulus under various wellbore inclinations and drilling fluid parameters. The stable height, lateral distribution characteristics, and response patterns of the resulting cuttings bed under different conditions were quantitatively characterized. Building upon this, a theoretical contact friction model between the drill string and the cuttings bed was employed to investigate how the bed height influences hook load during tripping and rotary torque during top drive operation. A mapping relationship was established between cuttings bed structural parameters and the resulting additional loads and torques. Results reveal significant interactive effects among drilling fluid velocity, fluid density, drill pipe rotation speed, and wellbore inclination on both cuttings bed development and associated drill string loads. Strong correlations were identified among these parameters. Based on these findings, a stuck pipe early-warning indicator system is proposed using frictional load thresholds, with clearly defined safety limits for cuttings bed height. Recommendations for optimizing cuttings transport parameters through coordinated control of fluid velocity, density, and rotary speed are also provided, offering theoretical support and engineering guidance for borehole cleaning strategies and stuck pipe risk prediction in large cuttings scenarios. Full article

Background: Simulation-based education (SBE) is a key component of nursing training. It enables students to apply theoretical knowledge in practice, expand their clinical understanding, develop critical thinking, improve communication skills, and build self-confidence. Increasing the number of simulation hours in nursing curricula may enhance students’ ability to manage stress in clinical settings. Methods: This was a cross-sectional comparative study involving 113 nursing students during a practical exam. Participants graduated in three consecutive years: 2020, 2021, and 2022. SBE was introduced into the curriculum in 2020, resulting in varying levels of simulation experience. Stress and anxiety markers were measured during the exam and compared across groups. Results: Greater simulation experience was associated with lower heart rate (p = 0.007), lower diastolic blood pressure (p < 0.001), and lower cortisol levels (p < 0.001). Students with two years of simulation training reported the lowest perceived stress (p = 0.031). However, anxiety levels remained high across all groups and did not differ significantly. Conclusions: The study showed that OSCEs are associated with elevated stress in nursing students. Students with greater exposure to simulation-based education had significantly lower stress and anxiety indicators. SBE appears to reduce stress and improve students’ preparedness for clinical assessments. Full article

Aerospace engines and hypersonic vehicles, among other high-temperature components, often operate in environments characterized by temperatures exceeding 1000 °C and high-speed airflow impacts, resulting in severe thermal erosion conditions. Coaxial thermocouples (CTs), with their unique self-eroding characteristic, are particularly well suited for use in such extreme environments. However, fabricating high-temperature electrical insulation layers for coaxial thermocouples remains challenging. Inspired by the self-healing mechanism of pine trees, we designed a composite electrical insulation layer with a similar self-healing function. This composite layer exhibits excellent high-temperature insulation properties (insulation resistance of 14.5 kΩ at 1200 °C). Applied as the insulation layer in K-type coaxial thermocouples via dip-coating, the thermocouples were tested for temperature and heat flux. Temperature tests showed an accuracy of 1.72% in the range of 200–1200 °C, a drift rate better than 0.474%/h at 1200 °C, and hysteresis better than 0.246%. The temperature response time was 1.08 ms. Heat flux tests demonstrated a measurable range of 0–41.32 MW/m² with an accuracy better than 6.511% and a heat flux response time of 7.6 ms. In simulated extreme environments, the K-type coaxial thermocouple withstood 70 s of 900 °C flame impact and 50 cycles of high-power laser thermal shock. Full article

In the age of labor shortage, increasing the throughput of warehouses is a good issue. In the recent two decades, automatic warehouses designed to reduce human labor have therefore become a very hot research topic. Tricycle forklifts being able to carry heavy goods can play important roles in automatic warehouses. Meanwhile, Robot Operating System (ROS) is a very famous and popular platform for developing the software of robotics. Its powerful communication function makes lots of warehouse information exchange easy. Therefore, ROS installed as the communication backbone of warehouse is very popular. However, the software modules of ROS do not offer tricycle forklifts. Therefore, in this research, the model of a tricycle forklift developed for ROS systems in warehouse applications is constructed. In spite of the developed model, the existing software modules must be modified for compatible connection such that the tricycle forklift can be navigated and controlled by constructed ROS. For the function of Simultaneous Localization And Mapping (SLAM) and the control of self-guided navigation, the constructed system is verified by Gazebo simulation. In addition, the experiments of a real tricycle forklift to demonstrate the developed ROS for enough accuracy of warehouse application are also included. Full article

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

Airy beams exhibit enormous application potential in the field of optics and microwave owing to their unique self-bending, self-accelerating, and non-diffracting characteristics. In this paper, the Airy beams are dynamically generated and manipulated in both reflection and transmission spaces utilizing a full space programmable metasurface, which can achieve an approximately 360° phase coverage in the reflection space and a nearly 180° phase coverage in the transmission space in the operating frequency band from 6 GHz to 7 GHz. The direct current (DC) bias voltage is applied to the varactor diodes integrated on the metasurface by precise control of the external feeding system, allowing dynamic generation and regulation of Airy beams. Numerical simulations and experimental measurements are performed at 6.5 GHz. The Airy beams with parameters a = 56 and 61 are generated in the transmission space, while the Airy beams with parameters a = 71 and 81 are achieved in the reflection space. The parabolic propagation trajectory of the main beams and acceleration in the transverse planes can be observed. The good agreement between the simulated and measured results demonstrates that the metasurface can dynamically generate and manipulate the Airy beams in full space. The suggested Airy beam manipulation system has a wide range of applications, including optical particle manipulation, imaging, and difficult terrain exploration. Full article

Background: With increasing involvement of pharmacists in clinical situations and expanding scope of practice, the expectation and management of uncertainty is a desirable component of pharmacy education, leading to improved uncertainty tolerance (UT) as pharmacists. Methods: The purpose of this pilot study was to determine whether an inpatient rounding simulation (IRS) that exposes student pharmacists to a level of uncertainty leads to changes in tolerance of ambiguity scale (TAS) scores and self-reflection comments. A pre-/post-observational, pilot survey study was conducted, including student pharmacists in their second professional year at the United States School of Pharmacy who were enrolled in an associated lab-based course in Spring 2024. Student teams completed a mock IRS and responded to clinical questions in a timed environment. Students completed pre-/post-simulation TAS and self-reflection on uncertainty/UT within the simulation. Pre-/post-simulation TAS scores were analyzed with a paired t-test. Qualitative analysis was used to identify themes in self-reflection. Results: 59 students responded (48% response rate). TAS was not different pre-/post-IRS (63.2 vs. 62.6, p = 0.63). When individual subscales were compared, a statistically significant difference was found in the insolubility subscale (10.5 vs. 9.5; p = 0.02). Themes of uncertainty that emerged during qualitative analysis of pre-simulation included the clinical question posed to the team. Student pharmacists overcame uncertainty pre-simulation by working with their team and relying on the availability of resources. Themes of uncertainty emerging during qualitative analysis of post-simulation included discerning the best answer. Student pharmacists overcame uncertainty post-simulation by working with their team and cited working with their team as a positive aspect of the IRS experience. Conclusions: In this pilot study, student pharmacists had high TAS scores both pre- and post-simulation. Students utilized their teams and cited this as a positive in an uncertain environment. Full article

This study focuses on exploring the differences in driving abilities in emergency traffic situations between older drivers (aged 60–70) and young drivers (aged 20–35) in a simple traffic environment. Two typical emergency scenarios were designed in the experiment: Scenario A (intrusion of electric bicycles) and Scenario B (pedestrians crossing the road). The experiment employed a driving simulation system to synchronously collect data on eye movement characteristics, driving behavior, and physiological metrics from 30 drivers. Two-factor covariance analysis, correlation analysis, and regression analysis were conducted on the experimental data. The comprehensive study results indicated that the older group exhibited better driving performance in emergency scenarios compared to the younger group. Specifically, in Scenario A, the older group had a faster first fixation time on the AOI compared to the younger group, a faster braking reaction time, a higher maximum brake pedal depth, and a higher skin conductance level. In Scenario B, the older group’s driving performance was similar to that in Scenario A, with better performance than the younger group. The study reveals that in some simple driving tasks, young-old drivers (60–70 years) can compensate for their physiological decline through self-regulation and self-restraint, thereby exhibiting safer driving behaviors. Full article

GNSS-Inertial integration brings great potential to detect and mitigate the effect of erroneous (spoofed) GNSS data. When a trajectory of an airplane diverges from (or is inconsistent with) inertial data, the integrated system may detect this erroneous GNSS trajectory and may be able to maintain navigation integrity by rejecting this data. A GNSS-Aided Inertial System can provide both self-contained detection of a GNSS spoofing event as well as mitigation, where mitigation is hard to achieve globally with other commercial aviation systems relying on good ground system coverage. This paper provides an overview of the newly developed Inertial Spoofing Monitor for aviation grade navigation systems, which was designed to detect multiple simultaneous erroneous (spoofed) satellite measurements. The Inertial Spoofing Monitor was then thoroughly tested, and simulations were performed to evaluate and demonstrate the detection, mitigation, and recovery capability of the spoofing monitor. The performance validation followed the process prescribed by Appendix Q of the RTCA DO-384 MOPS (Minimum Operation Performance Standard). The results show great detection, mitigation, and recovery performance of the developed Inertial Spoofing Monitor, but also indicate constraints regarding the assumed sensor error model. Full article