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The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in orbit, and a significant communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. Traditional navigation schemes have been unable to provide high-accuracy autonomous intelligent navigation for the probe independent of the ground. Therefore, high-accuracy intelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE probe. The probe of the SSBE is disturbed by multiple sources of solar light pressure and a complex, unknown environment during its long cruise operation while in orbit. In order to ensure the high-accuracy position state and velocity state error estimation for the probe in the cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar/solar and target planetary Doppler velocity measurements is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of state and observation noise error covariance parameters. The federated extended Kalman filter (FEKF) based on the Q-learning (QLFEKF) navigation algorithm is proposed to achieve high-accuracy state estimations of the autonomous intelligence navigation system for the SSBE probe cruise phase. The main advantage of the QLFEKF is that Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time and obtain high position and velocity state estimation (PVSE) accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSE navigation accuracy of the federated filter integrated based the Q-learning navigation algorithm is improved by 55.84% and 37.04%, respectively, demonstrating the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher navigation accuracy and is able to satisfy the demands of autonomous high accuracy for the SSBE cruise phase. [ABSTRACT FROM AUTHOR]

The first-line treatment for severe Pneumocystis carinii pneumonia (PCP) is trimethoprim-sulfamethoxazole (TMP/SMZ). Here, we report a case involving 6-month-old child with a PCP infection, highlighting the role of clinical pharmacists in providing individualized pharmaceutical care and guidance through the process of therapeutic drug monitoring (TDM). Methods: The clinical pharmacist monitored the concentration of TMP/SMZ in the serum, urine and sputum of a 6-month-old child with PCP infection. To improve the serum levels of TMP/SMZ, the dose of TMP/SMZ was increased, while infusions of other medications were reduced to decrease the rate of drug excretion. Additionally, the patient received other supportive medications to enhance clinical therapeutic efficacy. Results: Clinical pharmacists observed that, despite administration of a sufficient dose of TMP/SMZ, plasma concentration of TMP/SMZ remained below the therapeutic window, while urine concentrations were extremely high. This phenomenon was attributed to Augmented Renal Clearance (ARC), often seen in critically ill patients and associated with increased renal clearance. Throughout treatment, the concentrations of SMZ remained below the minimum effective concentration, while the concentrations of TMP fell within the effective target range. However, sufficient therapeutic effects were ultimately achieved and observed in the patient, likely due to improved drug distribution in lung tissue (sputum) and the patient's recovering immune functions. Finally, thanks to individualized pharmaceutical care from clinical pharmacists and the combined efforts of clinicians, the patient was discharged after 58 days of hospitalization. Conclusion: Throughout treatment, the clinical pharmacist played a vital role in optimizing the treatment plan based on the serum, urine and sputum concentrations of TMP/SMZ and providing pharmaceutical care to ensure a safe, rational and effective medications in children. Individualized dose adjustments, particularly high-dose TMP/SMZ guided by TDM, can significantly enhance the management of PCP in pediatric patients and support clinical pharmacists in delivering individualized pharmaceutical care. [ABSTRACT FROM AUTHOR]

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target's Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. [ABSTRACT FROM AUTHOR]

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method's capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system. [ABSTRACT FROM AUTHOR]

Changes in physiological factors may result in large pharmacokinetic variability of vancomycin in pediatric patients, thereby leading to either supratherapeutic or subtherapeutic exposure and potentially affecting clinical outcomes. This study set out to characterize the disposition of vancomycin, quantify the exposure target and establish an optimal dosage regimen among the Southern Chinese pediatric population. Routine therapeutic drug monitoring data of 453 patients were available. We performed a retrospective population pharmacokinetic analysis of hospitalized children prescribed intravenous vancomycin using NONMEM® software. A one‐compartment PPK model of vancomycin with body weight and renal functions as covariates based on a cutoff of 2 years old children was proposed in this study. Both internal and external validation showing acceptable and robust predictive performance of the model to estimate PK parameters. The value of area under the curve over 24 h to minimum inhibitory concentration ratio (AUC0‐24/MIC) ≥ 260 was a significant predictor for therapeutic efficacy. Monte Carlo simulations served as a model‐informed precision dosing approach and suggested that different optimal dose regimens in various scenarios should be considered rather than flat dosing. The evaluation of vancomycin exposure‐efficacy relationship indicated that lower target level of AUC0‐24/MIC may be needed to achieve clinical effectiveness in children, which was used to derive the recommended dosing regimen. Further prospective studies will be needed to corroborate and elucidate these results. [ABSTRACT FROM AUTHOR]

In the context of the escalating challenge of climate change, optimizing buildings' energy performance has become a critical research area, yet studies specifically targeting library buildings are scarce. This study addresses this gap by investigating the impact of multi-objective optimization on energy efficiency and occupant comfort in educational library buildings under future climate scenarios. Utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), this research optimizes a range of building parameters, including the cooling and heating setpoints, air change rates, shading device depths, window visible transmittance, and window gas types. The optimization aims to balance energy consumption and comfort, using simulations based on future weather data for the years 2020, 2050, and 2080. The results indicate that the optimized solutions can significantly reduce the heating energy by up to 95.34% and the cooling energy by up to 63.74% compared to the baseline models, while maintaining or improving the occupant comfort levels. This study highlights the necessity for dynamic, responsive architectural designs that can adapt to changing environmental conditions, ensuring both sustainability and occupant well-being. Furthermore, integrating these building-level optimizations into a City Information Model (CIM) framework can enhance urban planning and development, contributing to more resilient and energy-efficient cities. These findings underscore the importance of sustainable design practices in the context of climate change and the critical role of advanced optimization techniques in achieving energy-efficient, comfortable educational spaces. [ABSTRACT FROM AUTHOR]

The problem of adaptive output feedback control for a class of nonlinear Cyber-Physical Systems(CPSs) with unknown control direction under sensor and actuator attacks is studied. By implementing a suitable linear state transformation, the original system is transformed into a new system for facilitating the design of output feedback controller. Nussbaum gain function method is adopted to overcome the design difficulty resulted from unknown control coefficients and network attacks. Based on the state estimate and the attacked measurement output signal, a new event-triggered adaptive output-feedback control strategy is proposed by means of neural network approximation method. The proposed controller can not only achieve the semi-globally uniformly ultimately boundedness of the closed-loop system, but also greatly reduce the number of transmitted data. In addition, the "complexity explosion" problem in backstepping design can be overcome by means of the dynamic surface control (DSC) method based on a nonlinear filter. Finally, a robotic arm system is utilized to verify the effectiveness and practicality of the proposed control method. [ABSTRACT FROM AUTHOR]