Search Results (4,475)

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

As the resolution and conversion speed of time-to-digital conversion (TDC) chips continue to improve, the bit error rate also increases, leading to a decrease in the linearity of TDC and seriously affecting measurement accuracy. This paper presents a high-linearity, low-power-consumption, and wide dynamic range TDC that was achieved based on the SMIC 180 nm BCD process. Compared with previous research methods, the proposed phase arbiter structure can eliminate sampling errors and improve the linearity of TDC. The preprocessing circuit can eliminate fixed errors caused by START and STOP signal transmission delays. Post-simulation results show that the TDC has high linearity, with ranges of DNL and INL being −0.98 LSB < DNL < 0.93 LSB and −0.88 LSB < INL < 0.95 LSB, respectively. The highest resolution is 156 ps, the maximum measurement time range is 1.2 μs, and the power consumption is 1.625 mW. The overall system architecture of TDC is very simple, and it can be applied to dToF LIDAR to measure photon flight time, capable of measuring a range of up to hundreds of meters, with an accuracy of 2.25 cm, high linearity, and without any post-processing or time calibration. Full article

Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article

Organic acids could act as retarders in magnesium oxysulfide (MOS) systems, not only delaying setting and improving fluidity but also enhancing compressive strength and water resistance. These effects are generally attributed to both the presence of H⁺ ions and anion chelation. However, the enhancement efficiency of different organic acids in MOS systems varies significantly due to differences in their molecular structures. To determine the underlying mechanism, this study comparatively investigated the effects of amino trimethylene phosphonic acid (ATMP) and tartaric acid (TA) on the setting time, fluidity, compressive strength, and water resistance of the MOS system, with the two additives incorporated at mole ratios to MgO ranging from 0.002 to 0.006. The mechanism behind it was revealed by discussion on the hydration heat, hydrates, and pH value. Results showed that both ATMP and TA could effectively improve the fluidity, delay the setting process, and enhance the mechanical properties, including strength and water resistance. At a mole ratio of 0.006, the incorporation of ATMP increased the 28 d compressive strength and the softening coefficient by 214.12% and 37.29%, respectively, compared with the blank group. In contrast, under the same dosage, TA led to an increase of 55.13% in the 28 d strength and 22.03% in the softening coefficient. Furthermore, hydration heat, product analysis, and pH measurements indicated that both ATMP and TA inhibited hydration during the initial hours but promoted hydration at later stages. The potential reason could be divided into two aspects: (1) H⁺ ions from ATMP and TA suppressing the formation of Mg(OH)₂; (2) anion chelation with Mg²⁺ in the liquid phase, leading to a supersaturated solution with higher saturation, which further hindered Mg(OH)₂ formation and facilitated the later development of 5Mg(OH)₂·MgSO₄·7H₂O (517 phase). By contrast, under the same mole dosage of H⁺ or anions, the enhancement in compressive strength as well as the water resistance is superior when using ATMP. This was owing to its stronger chelating ability of ATMP, which more effectively inhibited Mg(OH)₂ formation and then promoted the formation of the 517 phase. These findings confirm that the chelating ability of anions exerts an important impact on the retarding effect as well as the enhancement of strength in MOS systems. Full article

Extra-pulmonary tuberculosis (EPTB) can affect any organ of the body, producing a wide variety of clinical manifestations that make the diagnosis and treatment of EPTB challenging. The optimum treatment varies depending on the site of EPTB, its severity, and response to treatment. There is often uncertainty about the best management practices, with a significant departure from national guidelines. This study aims to identify gaps and barriers in adhering to the national guidelines for the diagnosis and treatment of EPTB. We included 433 patients having EPTB and followed up at predefined intervals of 2 months, 6 months, 9 months, and 12 months. Questionnaire-based interviews of the treating physician and the patients in different departments were conducted. For confirmatory diagnosis, heavy dependence on clinical-radiological diagnosis without microbiological support was observed, which is a deviation from National Tuberculosis Elimination Programme (NTEP) guidelines and raises concerns about the potential for misdiagnosis and overtreatment. Apart from patient delays, long health system delays in EPTB were observed. The median patient delay, health system delay, and total treatment delay times were 4.2, 4, and 10.1 weeks, respectively. To enhance EPTB diagnosis and management, there is a pressing need for improved access to microbiological testing, enhanced physician training on adherence to NTEP guidelines, and greater utilisation of imaging and histopathological techniques. Full article

Supervisory control theory (SCT), based on Petri nets, offers a robust framework for modeling and controlling discrete-event systems but faces significant challenges in scalability, expressiveness, and practical implementation. This paper introduces General-purpose Petri Net Simulator and Real-Time Controller (GPenSIM), a MATLAB version 24.1.0.2689473 (R2024a) Update 6-based modular Petri net framework, as a novel solution to these limitations. GPenSIM leverages modular decomposition to mitigate state-space explosion, enabling parallel execution of weakly coupled Petri modules on multi-core systems. Its programmable interfaces (pre-processors and post-processors) extend classical Petri nets’ expressiveness by enforcing nonlinear, temporal, and conditional constraints through custom MATLAB scripts, addressing the rigidity of traditional linear constraints. Furthermore, the integration of GPenSIM with MATLAB facilitates real-time control synthesis, performance optimization, and seamless interaction with external hardware and software, bridging the gap between theoretical models and industrial applications. Empirical studies demonstrate the efficacy of GPenSIM in reconfigurable manufacturing systems, where it reduced downtime by 30%, and in distributed control scenarios, where decentralized modules minimized synchronization delays. Grounded in systems theory principles of interconnectedness, GPenSIM emphasizes dynamic relationships between components, offering a scalable, adaptable, and practical tool for supervisory control. This work highlights the potential of GPenSIM to overcome longstanding limitations in SCT, providing a versatile platform for both academic research and industrial deployment. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

Focusing the over simplification of existing models in simulating river flow evolution process and lack of coordination of hydropower generation and export, this paper proposes a hydropower generation and export coordinated optimal operation model that, at the same time, incorporates dynamic water flow delay by finely modeling the water flow evolution process among cascade hydropower stations within a river basin. Specifically, firstly, a dynamic water flow evolution model is built based on the segmented Muskingum method. By dividing the river into sub-segments and establishing flow evolution equation for individual sub-segments, the model accurately captures the dynamic time delay of water flow. On this basis, integrating cascade hydropower systems and the transmission system, a hydropower generation and export coordinated optimal operation model is proposed. By flexibly adjusting the power export, the model balances local consumption and external transmission of hydropower, enhancing the utilization efficiency of hydropower resources and achieving high economic performance. A case study verified the accuracy of the dynamic water flow evolution model and the effectiveness of the proposed hydropower generation and export coordinated optimal operation model. Full article

The improvement of the partially integrated reinforcement learning-model predictive control (RL-MPC) system is developed in the paper by introducing the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. This framework differs from the traditional ones, which completely substitute the MPC prediction model; instead, an RL agent refines predictions through feedback correction and thus maintains interpretability while improving robustness. Most importantly, the study details two configuration paradigms: decoupled (offline policy application) and coupled (online policy update) and tests them for their effectiveness in trajectory tracking tasks within simulation and real-life experiments. A decoupled framework based on TD3 showed significant improvements in control performance compared to the rest of the implemented paradigms, especially concerning Integral of Time-weighted Absolute Error (ITAE) and mean absolute error (MAE). This work also illustrated the advantages of partial integration in balancing adaptability and stability, thus making it suitable for real-time applications in robotics. Full article

Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article

This study investigates the short- and long-run drivers of natural gas consumption in the European Union using an ARDL bounds testing approach. The analysis incorporates GDP per capita, liquid fuel use, and solid fuel use as explanatory variables. Augmented Dickey–Fuller tests confirm mixed integration orders, allowing valid ARDL estimation. The results reveal a statistically significant long-run relationship (cointegration) between gas consumption and the energy–economic system. In the short run, the use of liquid fuel exerts a strong positive influence on gas demand, while the effects of GDP materialise only after a two-year lag. Solid fuels show a delayed substitutive impact, reflecting the ongoing transition from coal. An error correction model confirms rapid convergence to equilibrium, with 77% of deviations corrected within one period. Recursive residual and CUSUM tests indicate structural stability over time. These findings highlight the responsiveness of EU gas demand to both economic and policy signals, offering valuable insights for energy modelling and strategic planning under the European Green Deal. Full article

This study develops a dynamic IS-LM macroeconomic model that incorporates delayed taxation and a memory-dependent income effect, and calibrates it to quarterly data for Romania (2000–2023). Within this framework, fiscal policy lags are modelled using a “memory” income variable that weights past incomes, an approach grounded in distributed lag theory to capture how historical economic conditions influence current dynamics. The model is analysed both analytically and through numerical simulations. We derive stability conditions and employ bifurcation analysis to explore how the timing of taxation influences macroeconomic equilibrium. The findings reveal that an immediate taxation regime yields a stable adjustment toward a unique equilibrium, consistent with classical IS-LM expectations. In contrast, delayed taxation, where tax revenue depends on past income, can destabilise the system, giving rise to cycles and even chaotic fluctuations for parameter values that would be stable under immediate collection. In particular, delays act as a destabilising force, lowering the threshold of the output-adjustment speed at which oscillations emerge. These results highlight the critical importance of policy timing: prompt fiscal feedback tends to stabilise the economy, whereas lags in fiscal intervention can induce endogenous cycles. The analysis offers policy-relevant insights, suggesting that reducing fiscal response delays or counteracting them with other stabilisation tools is crucial for macroeconomic stability. Full article

Traumatic liver injury remains a significant contributor to trauma-related morbidity and mortality worldwide. In Saudi Arabia, motor vehicle accidents (MVAs) are the predominant mechanism of injury, particularly among young adults. This study aimed to evaluate the clinical characteristics, management strategies, and outcomes of patients with liver trauma over a ten-year period at a tertiary academic level-one trauma center. A retrospective cohort study was conducted from January 2015 to December 2024. All adult patients (aged 18–65 years) who sustained blunt or penetrating liver injuries and underwent a pan-CT trauma survey were included. Demographic data, Injury Severity Scores (ISSs), imaging timelines, management approach, and clinical outcomes were analyzed. Statistical analysis was performed using JASP software with a significance threshold set at p < 0.05. A total of 111 patients were included, with a mean age of 33 ± 12.4 years; 78.1% were male. MVAs were the leading cause of injury (75.7%). Most patients (80.2%) had low-grade liver injuries and received non-operative management (NOM), with a high NOM success rate of 94.5%. The median time to CT was 55 ± 64 min, and the mean time to operative or IR intervention was 159.9 ± 78.8 min. Complications occurred in 32.4% of patients, with ventilator-associated pneumonia (19.8%) being most common. The overall mortality was 6.3%. Multivariate analysis revealed that shorter time to CT significantly reduced mortality risk (OR = 0.5, p < 0.05), while a positive e-FAST result was strongly associated with increased mortality (OR = 3.3, p < 0.05). Higher ISSs correlated with longer monitored unit stays (ρ = 0.3, p = 0.0014). Traumatic liver injuries in this cohort were predominantly low-grade and effectively managed conservatively, with favorable outcomes. However, delays in imaging and operative intervention were observed, underscoring the requirement for streamlined trauma workflows. These findings highlight the requirement for continuous trauma system improvement, including protocol optimization and timely access to imaging and surgical intervention. Full article

In integrated PNT systems, due to defects in satellite signals and long-wave signals, VLF signals can be an essential supplement. However, there is currently a lack of VLF timing systems in the world, and it is impossible to evaluate the impact of the propagation delay of these signals. Based on the theory of very-low-frequency propagation, this paper determines the waveguide mode propagation at ultra-long distances as the main research direction, establishes a signal phase change model, gives a theoretical formula for the phase velocity of VLF signals, and analyzes the main factors affecting the phase velocity of VLF signal propagation. Finally, combined with historical observation data, the phase change is predicted, compared, and analyzed. The results show that the theoretical calculation is consistent with the measured data. The average error of the delay prediction is 0.015 microseconds per 100 km, and the maximum error of the delay prediction is 0.152 microseconds per 100 km. Full article