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Keywords = unknown time delays

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18 pages, 2531 KiB  
Article
Inhibitory Effect of Allyl Isothiocyanate on Cariogenicity of Streptococcus mutans
by Tatsuya Akitomo, Ami Kaneki, Masashi Ogawa, Yuya Ito, Shuma Hamaguchi, Shunya Ikeda, Mariko Kametani, Momoko Usuda, Satoru Kusaka, Masakazu Hamada, Chieko Mitsuhata, Katsuyuki Kozai and Ryota Nomura
Int. J. Mol. Sci. 2025, 26(15), 7443; https://doi.org/10.3390/ijms26157443 (registering DOI) - 1 Aug 2025
Abstract
Allyl isothiocyanate (AITC) is a naturally occurring, pungent compound abundant in cruciferous vegetables and functions as a repellent for various organisms. The antibacterial effect of AITC against various bacteria has been reported, but there are no reports on the effect on Streptococcus mutans [...] Read more.
Allyl isothiocyanate (AITC) is a naturally occurring, pungent compound abundant in cruciferous vegetables and functions as a repellent for various organisms. The antibacterial effect of AITC against various bacteria has been reported, but there are no reports on the effect on Streptococcus mutans, a major bacterium contributing to dental caries. In this study, we investigated the inhibitory effect and mechanism of AITC on the survival and growth of S. mutans. AITC showed an antibacterial effect in a time- and concentration-dependent manner. In addition, bacterial growth was delayed in the presence of AITC, and there were almost no bacteria in the presence of 0.1% AITC. In a biofilm assay, the amount of biofilm formation with 0.1% AITC was significantly decreased compared to the control. RNA sequencing analysis showed that the expression of 39 genes (27 up-regulation and 12 down-regulation) and 38 genes (24 up-regulation and 14 down-regulation) of S. mutans was changed during the survival and the growth, respectively, in the presence of AITC compared with the absence of AITC. Protein–protein interaction analysis revealed that AITC mainly interacted with genes of unknown function in S. mutans. These results suggest that AITC may inhibit cariogenicity of S. mutans through a novel mechanism. Full article
(This article belongs to the Special Issue Microbial Infections and Novel Biological Molecules for Treatment)
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25 pages, 674 KiB  
Article
Sensor Fault Detection and Reliable Control of Singular Stochastic Systems with Time-Varying Delays
by Yunling Shi, Haosen Yang, Gang Liu, Xiaolin He and Jajun Wang
Sensors 2025, 25(15), 4667; https://doi.org/10.3390/s25154667 - 28 Jul 2025
Viewed by 150
Abstract
In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump [...] Read more.
In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and H performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the H performance index γ were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article
(This article belongs to the Special Issue Smart Sensing and Control for Autonomous Intelligent Unmanned Systems)
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19 pages, 3636 KiB  
Article
Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
by Hanlin Liu, Yule Hu and Zhenkun Wu
Appl. Sci. 2025, 15(15), 8317; https://doi.org/10.3390/app15158317 - 26 Jul 2025
Viewed by 191
Abstract
Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. [...] Read more.
Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06° and 0.01°, respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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15 pages, 5202 KiB  
Article
Power-Independent Microwave Photonic Instantaneous Frequency Measurement System
by Ruiqiong Wang and Yongjun Li
Sensors 2025, 25(14), 4382; https://doi.org/10.3390/s25144382 - 13 Jul 2025
Viewed by 339
Abstract
The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) [...] Read more.
The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) curve, achieved by introducing a frequency-dependent time delay via an optical tunable delay line (OTDL) for the signal under test (SUT). System simulation demonstrates the measurement capability across a wide bandwidth of 0.1–40 GHz with high precision, exhibiting frequency errors ranging from −0.03 to 0.04 GHz. The scheme also maintains consistent performance under varying input power levels. Key implementation aspects, including single-sideband modulation selection and system extension methods, are analyzed in detail to optimize measurement accuracy. Notably, the proposed architecture features a simple and compact design with excellent integration potential. These characteristics, combined with its wide operational bandwidth and high measurement precision, make this approach particularly suitable for demanding applications in electronic reconnaissance and communication. Full article
(This article belongs to the Special Issue Advanced Microwave Sensors and Their Applications in Measurement)
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28 pages, 11429 KiB  
Article
Trajectory Tracking of Unmanned Surface Vessels Based on Robust Neural Networks and Adaptive Control
by Ziming Wang, Chunliang Qiu, Zaopeng Dong, Shaobo Cheng, Long Zheng and Shunhuai Chen
J. Mar. Sci. Eng. 2025, 13(7), 1341; https://doi.org/10.3390/jmse13071341 - 13 Jul 2025
Viewed by 249
Abstract
In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a [...] Read more.
In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article
(This article belongs to the Section Ocean Engineering)
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15 pages, 1239 KiB  
Article
Extremum Seeking for the First Derivative of Nonlinear Maps with Constant Delays via a Time-Delay Approach
by Jianzhong Li, Hongye Su and Yang Zhu
Mathematics 2025, 13(13), 2196; https://doi.org/10.3390/math13132196 - 4 Jul 2025
Viewed by 178
Abstract
This paper introduces an extremum seeking (ES) scheme for the unknown map’s first derivative by tailoring a demodulation signal in which the closed-loop system is subject to constant transmission delays. Unlike most publications that manage delays using predictor-based methods, we are concerned with [...] Read more.
This paper introduces an extremum seeking (ES) scheme for the unknown map’s first derivative by tailoring a demodulation signal in which the closed-loop system is subject to constant transmission delays. Unlike most publications that manage delays using predictor-based methods, we are concerned with the delay-robustness of the introduced ES system via the newly developed time-delay approach. The original ES system is transformed to a nonlinear retarded-type plant with disturbances and the stability condition in the form of linear matrix inequalities is achieved. When the related bounds of the nonlinear map are not known, a rigorous practical stability proof is provided. Second, and more importantly, under the availability of prior knowledge about the nonlinear map, we are able to provide a quantitative calculation on the maximum allowable delay, the upper bound of the dither period, and the ultimate seeking error. Numerical examples are offered to exemplify the effectiveness of the proposed method. Full article
(This article belongs to the Section E2: Control Theory and Mechanics)
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12 pages, 488 KiB  
Article
Association Between Endogenous Equol Production and the Onset of Overactive Bladder in Postmenopausal Women
by Hiroyuki Honda, Tomohiro Matsuo, Hidenori Ito, Shota Kakita, Shintaro Mori, Kyohei Araki, Kensuke Mitsunari, Kojiro Ohba, Yasushi Mochizuki and Ryoichi Imamura
J. Clin. Med. 2025, 14(12), 4183; https://doi.org/10.3390/jcm14124183 - 12 Jun 2025
Viewed by 687
Abstract
Objectives: Equol, a gut-derived metabolite of soy isoflavones with estrogenic activity, may influence bladder aging. However, the association between overactive bladder (OAB), which is closely linked to bladder aging, and the estrogenic effects of equol remains unknown. Therefore, this study investigated the [...] Read more.
Objectives: Equol, a gut-derived metabolite of soy isoflavones with estrogenic activity, may influence bladder aging. However, the association between overactive bladder (OAB), which is closely linked to bladder aging, and the estrogenic effects of equol remains unknown. Therefore, this study investigated the association between endogenous equol production and onset and severity of OAB in postmenopausal women. Methods: The study included 128 postmenopausal women, newly diagnosed with OAB, who were categorized into equol- and non-equol-producing groups based on urinary equol levels as measured by enzyme-linked immunosorbent assay. Patient clinical characteristics, OAB Symptom Score (OABSS), and urodynamic parameters were assessed. Propensity score matching was performed to minimize confounding factors related to the timing of lower urinary tract symptom (LUTS) onset. Results: Equol producers exhibited a significantly later onset of LUTS than non-producers (68.7 ± 10.9 vs. 62.7 ± 10.7 years, p = 0.002). Equol producers were more prevalent in the late-onset group (58.6% vs. 31.0%, p = 0.002), which had significantly higher urinary equol concentrations than the early-onset group (p = 0.014). No significant differences were observed in total OABSS or subscale scores between the groups, suggesting that equol did not affect symptom severity. Propensity score-matched analysis (n = 104) confirmed that equol non-production was an independent risk factor for early-onset LUTS (OR, 1.930; 95% CI, 1.248–4.049; p = 0.014). Conclusions: Endogenous equol production was significantly associated with the delayed onset of OAB in postmenopausal women. Thus, equol may serve as a protective factor and non-invasive biomarker to guide individualized prevention and early intervention strategies in urological care for women. Full article
(This article belongs to the Topic Gynecological Endocrinology Updates)
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24 pages, 8162 KiB  
Article
Fixed-Time Event-Triggered Control for High-Order Nonlinear Multi-Agent Systems Under Unknown Stochastic Time Delays
by Junyi Liu, Hongbo Han, Yuncong Ma and Maode Yan
Mathematics 2025, 13(10), 1639; https://doi.org/10.3390/math13101639 - 16 May 2025
Viewed by 407
Abstract
In this paper, the fixed-time control for high-order nonlinear multi-agent systems under unknown stochastic time delay is investigated via an event-triggered approach. First of all, RBF neural networks are utilized to approximate the system’s uncertain nonlinearities. After that, an event-triggered scheme, which is [...] Read more.
In this paper, the fixed-time control for high-order nonlinear multi-agent systems under unknown stochastic time delay is investigated via an event-triggered approach. First of all, RBF neural networks are utilized to approximate the system’s uncertain nonlinearities. After that, an event-triggered scheme, which is designed with a relative threshold for more flexible control, is proposed to alleviate the communication burden. In consideration of the unknown stochastic time delay in the inter-communication among high-order nonlinear multi-agent systems, the Lyapunov–Krasovskii functional (LKF) is used to construct the system’s Lyapunov function, specifically targeting the adverse effects caused by time delay. Further, the fixed-time stability theory is employed to ensure that the convergence time remains independent of the initial values. Finally, the proposed control strategy is validated through numerical simulations. Full article
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10 pages, 1714 KiB  
Proceeding Paper
Efficient Detection of Galileo SAS Sequences Using E6-B Aiding
by Rafael Terris-Gallego, Ignacio Fernandez-Hernandez, José A. López-Salcedo and Gonzalo Seco-Granados
Eng. Proc. 2025, 88(1), 46; https://doi.org/10.3390/engproc2025088046 - 9 May 2025
Viewed by 214
Abstract
Galileo Signal Authentication Service (SAS) is an assisted signal authentication capability under development by Galileo, designed to enhance the robustness of the European Global Navigation Satellite System (GNSS) against malicious attacks like spoofing. It operates by providing information about some fragments of the [...] Read more.
Galileo Signal Authentication Service (SAS) is an assisted signal authentication capability under development by Galileo, designed to enhance the robustness of the European Global Navigation Satellite System (GNSS) against malicious attacks like spoofing. It operates by providing information about some fragments of the unknown spreading codes in the E6-C signal. Unlike other approaches, Galileo SAS uniquely employs Timed Efficient Stream Loss-tolerant Authentication (TESLA) keys provided by Open Service Navigation Message Authentication (OSNMA) in the E1-B signal for decryption, avoiding the need for key storage in potentially compromised receivers. The encrypted fragments are made available to the receivers before the broadcast of the E6-C signal, along with their broadcast time. However, if the receiver lacks an accurate time reference, searching for these fragments—which typically last for milliseconds and have periodicities extending to several seconds—can become impractical. In such cases, the probability of detection is severely diminished due to the excessively large search space that results. To mitigate this, initial estimates for the code phase delay and Doppler frequency can be obtained from the E1-B signal. Nevertheless, the alignment between E1-B and E6-C is not perfect, largely due to the intrinsic inter-frequency biases they exhibit. To mitigate this issue, we can leverage auxiliary signals like E6-B, processed by High Accuracy Service (HAS)-compatible receivers. This is a logical choice as E6-B shares the same carrier frequency as E6-C. This could help in obtaining more precise estimates of the location of the encrypted fragments and improving the probability of detection, resulting in enhanced robustness for the SAS authentication process. This paper presents a comparison of uncertainties associated with the use of the E1-B and E6-B signals, based on real data samples obtained with a custom-built Galileo SAS evaluation platform based on Software Defined Radio (SDR) boards. The results show the benefits of including E6-B in SAS processing, with minimal implementation cost. Full article
(This article belongs to the Proceedings of European Navigation Conference 2024)
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43 pages, 5199 KiB  
Article
An Actor–Critic-Based Hyper-Heuristic Autonomous Task Planning Algorithm for Supporting Spacecraft Adaptive Space Scientific Exploration
by Junwei Zhang and Liangqing Lyu
Aerospace 2025, 12(5), 379; https://doi.org/10.3390/aerospace12050379 - 28 Apr 2025
Viewed by 410
Abstract
Traditional spacecraft task planning has relied on ground control centers issuing commands through ground-to-space communication systems; however, as the number of deep space exploration missions grows, the problem of ground-to-space communication delays has become significant, affecting the effectiveness of real-time command and control [...] Read more.
Traditional spacecraft task planning has relied on ground control centers issuing commands through ground-to-space communication systems; however, as the number of deep space exploration missions grows, the problem of ground-to-space communication delays has become significant, affecting the effectiveness of real-time command and control and increasing the risk of missed opportunities for scientific discovery. Adaptive Space Scientific Exploration requires that spacecraft have the ability to make autonomous decisions to complete known and unknown scientific exploration missions without ground control. Based on this requirement, this paper proposes an actor–critic-based hyper-heuristic autonomous mission planning algorithm, which is used for mission planning and execution at different levels to support spacecraft Adaptive Space Scientific Exploration in deep space environments. At the bottom level of the hyper-heuristic algorithm, this paper uses the particle swarm optimization algorithm, grey wolf optimization algorithm, differential evolution algorithm, and positive cosine optimization algorithm as the basic operators. At the high level, a reinforcement learning strategy based on the actor–critic model is used, combined with the network architecture, to construct a framework for the selection of advanced heuristic algorithms. The related experimental results show that the algorithm can meet the requirements of Adaptive Space Scientific Exploration, and exhibits a quality solution with higher comprehensive evaluation in the test. This study also designs an example application of the algorithm to a space engineering mission based on a collaborative sky and earth control system to demonstrate the usability of the algorithm. This study provides an autonomous mission planning method for spacecraft in the complex and ever-changing deep space environment, which supports the further construction of spacecraft autonomous capabilities and is of great significance for improving the exploration efficiency of deep space exploration missions. Full article
(This article belongs to the Special Issue Intelligent Perception, Decision and Autonomous Control in Aerospace)
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17 pages, 6150 KiB  
Article
Electromagnetic-Based Localization of Moisture Anomalies in Grain Using Delay-Multiply-and-Sum Beamforming Technique
by Xiaoxu Deng, Xin Yan, Jinyi Zhong and Zhongyu Hou
Appl. Sci. 2025, 15(9), 4848; https://doi.org/10.3390/app15094848 - 27 Apr 2025
Viewed by 287
Abstract
Timely detection and treatment of moisture anomalous regions in grain storage facilities is crucial for preventing mold growth, germination, and pest infestation. To locate these regions, this paper presents a novel anomalous moisture region localization algorithm based on the delay-multiply-and-sum (DMAS) beamforming techniques, [...] Read more.
Timely detection and treatment of moisture anomalous regions in grain storage facilities is crucial for preventing mold growth, germination, and pest infestation. To locate these regions, this paper presents a novel anomalous moisture region localization algorithm based on the delay-multiply-and-sum (DMAS) beamforming techniques, including the design of an effective spatial arrangement of electromagnetic wave transmitters and receivers, along with comprehensive testing of detectable regions and experimental validation of anomaly localization across varying moisture levels and positions within grain piles. Following initial localization using the proposed algorithm, the study introduces a reliability assessment method for unknown samples based on the signal-to-mean ratio (SMR) value and compares the region of maximum response intensity with that of maximum connected domain volume. The system demonstrated successful localization of a 7 cm × 7 cm × 7 cm region with 15.4% moisture content within a cubic experimental bin containing 10.5% moisture content long-grained rice, achieving an average recall accuracy exceeding 50%. The proposed method presents rapid detection capabilities and precise localization, showing potential for moisture content evaluation of anomalous regions and practical applications in grain storage monitoring systems. Full article
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18 pages, 2800 KiB  
Article
Microvascular Cortical Dynamics in Minimal Invasive Deep-Seated Brain Tumour Surgery
by José Pedro Lavrador, Oliver Wroe-Wright, Francesco Marchi, Ali Elhag, Andrew O’Keeffe, Pablo De La Fuente, Christos Soumpasis, Andrea Cardia, Ana Mirallave-Pescador, Alba Díaz-Baamonde, Jose Sadio Mosquera, Domingos Coiteiro, Sharon Jewell, Anthony Strong, Richard Gullan, Keyoumars Ashkan, Francesco Vergani, Ahilan Kailaya Vasan and Ranjeev Bhangoo
Cancers 2025, 17(9), 1392; https://doi.org/10.3390/cancers17091392 - 22 Apr 2025
Viewed by 651
Abstract
Background: The tubular retractor-assisted minimally invasive parafascicular approach (trMIPS) is a transsulcal approach to deep-seated brain tumours. It is a safe surgical approach but its impact on the microvascular dynamics of the retracted cortex and its clinical implications are unknown. Methods: This was [...] Read more.
Background: The tubular retractor-assisted minimally invasive parafascicular approach (trMIPS) is a transsulcal approach to deep-seated brain tumours. It is a safe surgical approach but its impact on the microvascular dynamics of the retracted cortex and its clinical implications are unknown. Methods: This was a single-centre prospective study including patients with deep-seated brain tumours operated on with a trMIPS (BrainPath Nico System©). All patients underwent pre- and post-cannulation indocyanine green study using a FLOW 800 module in a KINEVO Zeiss© microscope. Speed, delay, time-to-peak (TtP) rise-in-time and cerebral blood flow index (CBFI) metrics were assessed. Results: Thirty-five patients were included, with 144 regions-of-interest (ROIs) selected. The majority of patients were diagnosed with glioblastoma (51.43%), and 37.14% of patients had a preoperative focal neurological deficit (FND) at presentation. A ROI-based analysis concluded that an increase in speed and CBFI was related with a worse neurological outcome when comparing the pre- and post-brain cannulation assessments (speed: deterioration = 43.12 ± 80.60% versus stable = −14.51 ± 57.80% versus improvement = 6.93 ± 31.33%, p < 0.0001; CBFI: deterioration = 50.40 ± 88.17% versus stable = −2.70 ± 67.54% versus improvement = −38.98 ± 26.17%, p = 0.0005). These findings were reproducible in a combined-ROI per patient analysis and confirmed after adjustment for confounding. Conclusion: Microvascular flow dynamics impact trMIPS outcomes as an increase in the speed and CBFI after decannulation was related with worse neurological outcome. Full article
(This article belongs to the Special Issue Emerging Research on Primary Brain Tumors)
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17 pages, 2258 KiB  
Article
Fixed-Time Adaptive Synchronization of Fractional-Order Memristive Fuzzy Neural Networks with Time-Varying Leakage and Transmission Delays
by Yeguo Sun, Yihong Liu and Lei Liu
Fractal Fract. 2025, 9(4), 241; https://doi.org/10.3390/fractalfract9040241 - 11 Apr 2025
Viewed by 410
Abstract
Finite-time synchronization depends on the initial conditions of the system in question. However, the initial conditions of an actual system are often difficult to estimate or even unknown. Therefore, a more valuable and pressing problem is fixed-time synchronization (FTS). This paper addresses the [...] Read more.
Finite-time synchronization depends on the initial conditions of the system in question. However, the initial conditions of an actual system are often difficult to estimate or even unknown. Therefore, a more valuable and pressing problem is fixed-time synchronization (FTS). This paper addresses the issue of FTS for a specific class of fractional-order memristive fuzzy neural networks (FOMFNNs) that include both leakage and transmission delays. We have designed two distinct discontinuous control methodologies that account for these delays: a state feedback control scheme and a fractional-order adaptive control strategy. Leveraging differential inclusion theory and fractional-order differential inequalities, we derive several novel algebraic conditions that are independent of delay. These conditions ensure the FTS of drive–response FOMFNNs in the presence of leakage and transmission delays. Additionally, we provide an estimate for the upper bound of the settling time required to achieve FTS. Finally, to validate the feasibility and applicability of our theoretical findings, we present two numerical examples which are accompanied by simulations. Full article
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14 pages, 613 KiB  
Article
Exploratory Algorithms to Aid in Risk of Malignancy Prediction for Indeterminate Pulmonary Nodules
by Laurel Jackson, Claire Auger, Nicolette Jeanblanc, Christopher Jacobson, Kinnari Pandya, Susan Gawel, Hita Moudgalya, Akanksha Sharma, Christopher W. Seder, Michael J. Liptay, Ramya Gaddikeri, Nicole M. Geissen, Palmi Shah, Jeffrey A. Borgia and Gerard J. Davis
Cancers 2025, 17(7), 1231; https://doi.org/10.3390/cancers17071231 - 5 Apr 2025
Viewed by 687
Abstract
Background/Objectives: Lung cancer screening can reduce patient mortality. Multiple issues persist including timely management of patients with a radiologically defined indeterminate pulmonary nodule (IPN), which carries unknown pathological significance. This pilot study focused on combining demographic, clinical, radiographic, and common circulating biomarkers for [...] Read more.
Background/Objectives: Lung cancer screening can reduce patient mortality. Multiple issues persist including timely management of patients with a radiologically defined indeterminate pulmonary nodule (IPN), which carries unknown pathological significance. This pilot study focused on combining demographic, clinical, radiographic, and common circulating biomarkers for their ability to aid in IPN risk of malignancy prediction. Methods: A case-control cohort consisting of 379 patients with IPNs (251 stage I lung tumors and 128 nonmalignant nodules) was used for this effort, divided into training (70%) and testing (30%) sets. Demographic variables (age, sex, race, ethnicity), radiographic information (nodule size and location), smoking pack-years, and plasma biomarker levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, IL-6, PlGF, sFlt-1, hs-CRP, Ferritin, IgG, IgE, IgM, IgA, and Kappa and Lambda Free Light Chains were assessed for this purpose. Results: Multivariable analyses of biomarker, demographic, and radiographic variables yielded a model consisting of age, lesion size, pack-years, history of extrathoracic cancer, upper lobe location, spiculation, hs-CRP, NSE, Ferritin, and CA-125 (AUC = 0.872 in training, 0.842 in testing) with superior performance over the Mayo Score model, which consists of age, lesion size, history of smoking, history of extrathoracic cancer, upper lobe location, and spiculation (AUC = 0.816 in training, 0.787 in testing). Conclusions: In conclusion, a simple reduced algorithm consisting of biomarkers, clinical information, and demographic variables may have value for malignancy prediction of screen-detected IPNs. Upon further validation, this method stands to reduce the need for serial radiographic studies and the risks of diagnostic delay. Full article
(This article belongs to the Special Issue Predictive Biomarkers for Lung Cancer)
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19 pages, 15931 KiB  
Article
Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
by Yang Lei, Jian Hou, Peixin Ma and Mingze Ma
Appl. Sci. 2025, 15(6), 3313; https://doi.org/10.3390/app15063313 - 18 Mar 2025
Viewed by 883
Abstract
In modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, such as in search and rescue operations, planetary exploration, and environmental monitoring. This paper proposes a novel collaborative exploration strategy for multiple mobile robots, aiming [...] Read more.
In modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, such as in search and rescue operations, planetary exploration, and environmental monitoring. This paper proposes a novel collaborative exploration strategy for multiple mobile robots, aiming to quickly realize the exploration of entire unknown environments. Specifically, we investigate a hierarchical control architecture, comprising an upper decision-making layer and a lower planning and mapping layer. In the upper layer, the next frontier point for each robot is determined using Voronoi partitioning and the Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) deep reinforcement learning algorithm in a centralized training and decentralized execution framework. In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system. In addition, the improved Robot Operating System (ROS) and Gazebo simulation environments speed up simulation times, further alleviating the slow training of high-precision simulation engines. Finally, the simulation results demonstrate the superiority of the proposed strategy, which achieves over 90% exploration coverage in unknown environments with a significantly reduced exploration time. Compared to MATD3, Multi-Agent Proximal Policy Optimization (MAPPO), Rapidly-Exploring Random Tree (RRT), and Cost-based methods, our strategy reduces time consumption by 41.1%, 47.0%, 63.9%, and 74.9%, respectively. Full article
(This article belongs to the Special Issue Advanced Technologies in AI Mobile Robots)
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