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Long-term spaceflight is known to induce disruptions in circadian rhythms, which are driven by a central pacemaker located in the suprachiasmatic nucleus (SCN) of the hypothalamus, but the underlying molecular mechanisms remain unclear. Here, we developed a rat model that simulated microgravity and isolation environments through tail suspension and isolation (TSI). We found that the TSI environment imposed circadian disruptions to the core body temperature, heart rate, and locomotor-activity rhythms of rats, especially in the amplitude of these rhythms. In TSI model rats' SCNs, the core circadian gene NR1D1 showed higher protein but not mRNA levels along with decreased BMAL1 levels, which indicated that NR1D1 could be regulated through post-translational regulation. The autophagosome marker LC3 could directly bind to NR1D1 via the LC3-interacting region (LIR) motifs and induce the degradation of NR1D1 in a mitophagy-dependent manner. Defects in mitophagy led to the reversal of NR1D1 degradation, thereby suppressing the expression of BMAL1. Mitophagy deficiency and subsequent mitochondrial dysfunction were observed in the SCN of TSI models. Urolithin A (UA), a mitophagy activator, demonstrated an ability to enhance the amplitude of core body temperature, heart rate, and locomotor-activity rhythms by prompting mitophagy induction to degrade NR1D1. Cumulatively, our results demonstrate that mitophagy exerts circadian control by regulating NR1D1 degradation, revealing mitophagy as a potential target for long-term spaceflight as well as diseases with SCN circadian disruption. [ABSTRACT FROM AUTHOR]

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is a respiratory failure disease associated with high mortality rates in patients. The primary pathological damage is attributed to the excessive release of pro-inflammatory mediators in pulmonary tissue. However, specific therapy for ALI has not been developed. In this study, a series of novel ferulic acid-parthenolide (FA-PTL) and ferulic acid-micheliolide (FA-MCL) hybrid derivatives were designed, synthesized, and evaluated for their anti-inflammatory activities in vitro. Compounds 2, 4, and 6 showed pronounced anti-inflammatory activity against LPS-induced expression of pro-inflammatory cytokines in vitro. Importantly, compound 6 displayed good water solubility, and treatment of mice with compound 6 (10 mg/kg) significantly prevented weight loss and ameliorated inflammatory cell infiltration and edema in lung tissue, as well as improving the alveolar structure. These results suggest that compound 6 (((1aR,7aS,8R,10aS,10bS,E)-8-((dimethylamino)methyl)-1a-methyl-9-oxo-1a,2,3,6,7,7a,8,9,10a,10b-decahydrooxireno[2′,3′:9,10]cyclodeca[1,2-b]furan-5-yl)methyl (E)-3-(4-hydroxy-3-methoxyphenyl)acrylate 2-hydroxypropane-1,2,3-tricarboxylate) might be considered as a lead compound for further evaluation as a potential anti-ALI agent. [ABSTRACT FROM AUTHOR]

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system. [ABSTRACT FROM AUTHOR]

Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found on fault diagnosis under dynamic conditions, whereas the dynamic operation of a motor is a very common scenario, e.g., electric vehicles. The voltage and current signal-based traditional fault detection method is not only affected by the structure of the motor, but it also becomes complicated to extract signals involving fault characteristics. Hence, this paper proposes a search coil-based online method for detecting demagnetization faults in PMSMs under dynamic conditions, which are not affected by the motor structure. To gather the flux of the stator tooth, flexible Printed circuit board (FPCB) search coils are positioned at the stator slot. The search coil is made up of two branches that are one pole apart and arranged in reverse sequence. In this installation option, the output signal in the fault state cannot be eliminated, and the output signal in the health state is zero. This paper defines only that characteristic value related to the position angle of the rotor. Further, the aim was to simultaneously eliminate the influence of elements like the search coil installation error and the inherent dispersion of the permanent magnet on the detection results. To characterize the fault degree, the measurement differential between the health state and the fault state is further integrated according to a predetermined angle range. Last but not least, speed-independent detection of individual permanent magnet demagnetization faults is possible using rotor position and stator tooth flux. A six-phase PMSM was used in experiments to show the efficiency of the suggested approach. The findings of the experiment demonstrate that the suggested strategy may precisely ascertain when a defect will occur. [ABSTRACT FROM AUTHOR]

Interturn short circuit (ITSC) fault is a common fault in electric machines, which may severely damage the machines if no protective measure is taken in time. There are numerous fault diagnosis methods under a steady-state condition. However, there is relatively limited research on fault diagnosis under dynamic conditions. The dynamic operation of motors, such as in electric cars, is a very common scenario. Hence, this paper proposes a search-coil based online method for detecting ITSC fault in permanent magnet synchronous machine (PMSM) under a dynamic condition. The search coils are placed on the stator circumference at equal intervals. Each search coil reflects the information about the magnetic field in its vicinity and also contains the fault information. In this paper, the voltage induced by the odd sideband harmonics around the even carrier (2ωc±ω0) is selected as the fault characteristic to be used in effectively improving the detected signal-to-noise ratio by excluding the interference of the counter-potential of the permanent magnet. Since two adjacent search coils are placed one pole apart, a set of quadrature signals can be acquired. The Digital Lock-In Amplifier (DLIA) technology is applied to extract the amplitude of the characteristic voltage, which overcomes the shortcomings of the traditional spectrum analysis in applying to non-stationary conditions. The amplitudes of the voltage at different search coils can be compared to further determine the occurrence of a fault and also its rough location if occurred. Experiments were conducted with a six-phase PMSM for demonstrating the effectiveness of the proposed method. The obtained results show that the proposed method can accurately determine the occurrence of a fault. [ABSTRACT FROM AUTHOR]

Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer. [ABSTRACT FROM AUTHOR]

To understand the change in overburden structure after coal seam group mining, we investigated the overburden characteristics and bearing capacity of abandoned coal mines in a coal seam group. We provide a theoretical basis for the construction and utilization of a coal mining subsidence area under a complex geological environment. This paper takes the construction project of Zhongtie Huizhi Square in Zhangqiu District, Jinan City, Shandong Province as the engineering background. According to the occurrence conditions of the study area, theoretical analysis, similar simulation, numerical simulation, and engineering practice verification are used. The overburden structure characteristics of abandoned mines in a shallow-buried coal seam group were studied. The results show that the development height of the water-carrying fractured zone after the mining of the 3#, 4#, and 9# coal seams is 17 m, 19.5 m, and 27.1 m, respectively, which shows that the height of water flow in the fractured zone is proportional to the buried depth of the coal seam after coal seam mining. After the model is set aside for three months, the degree of development of the residual fracture in the goaf is analyzed, and the distribution law of residual porosity in the longwall old goaf of a shallow-buried multiple coal seam is obtained. The development rate of residual fissures on both sides of the goaf is between 20.31% and 42.31%. The residual fracture development rate in the middle is relatively small, being between 8.21% and 18.53%. We comprehensively analyzed the characteristics of overlying strata in the abandoned mine under actual stratum conditions, and compared the empirical calculation results, theoretical research, similar simulation, and numerical simulation results in the specification with the engineering practice to prove the reliability of the research. [ABSTRACT FROM AUTHOR]

To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve as direct substitutes for the majority of modules within YOLOv8. Subsequently, we develop an innovative attention mechanism, denoted as ECSA (Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for the pruning process, culminating in the creation of LP-YOLO(s). Through a comprehensive series of evaluations on the IP102 dataset, the efficacy of LP-YOLO as a lightweight object detection model is validated. By incorporating fine-tuning techniques during training, LP-YOLO(s)n demonstrates a marginal mAP decrease of only 0.8% compared to YOLOv8n. However, it achieves a significant reduction in parameter count by 70.2% and a remarkable 40.7% increase in FPS, underscoring its efficiency and performance. [ABSTRACT FROM AUTHOR]

Graphene with atomic thickness possesses excellent mechanical and electrical properties, which hold great potential for high performance pressure sensing. The exposed electron of graphene is always cross-sensitive to any pollution absorbed or desorbed on the surface, from which the long-term stability of the graphene pressure sensor suffers a lot. This is one of the main obstacles towards graphene commercial applications. In this paper, we utilized polymethylmethacrylate (PMMA)/graphene heterostructure to isolate graphene from the ambient environment and enhance its strength simultaneously. PMMA/graphene pressure sensors, with the finite-depth cavities and the through-hole cavities separately, were made for comparative study. The through-hole device obtained a comparable sensitivity per unit area to the state of the art of the bare graphene pressure sensor, since there were no leaking cracks or defects. Both the sensitivity and stability of the through-hole sensor are better than those of the sensor with 285-nm-deep cavities, which is due to the sealed gas effect in the pressure cavity. A modified piezoresistive model was derived by considering the pressure change of the sealed gas in the pressure cavity. The calculated result of the new model is consistent with the experimental results. Our findings point out a promising route for performance optimization of graphene pressure sensors. [ABSTRACT FROM AUTHOR]

Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction. [ABSTRACT FROM AUTHOR]