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Visual–Inertial Odometry (VIO) is a foundational technology for autonomous navigation and robotics. However, existing deep learning-based methods face key challenges in temporal modeling and computational efficiency. Conventional approaches, such as Long Short-Term Memory (LSTM) networks and Transformers methods, often struggle to handle dependencies across different temporal scales while causing high computational costs. To address these issues, this work introduces Receptance Weighted Key Value (RWKV)-VIO, a novel framework based on the RWKV architecture. The proposed framework is designed with a lightweight structure and linear computational complexity, which effectively reduces the computational burden in temporal modeling. Furthermore, a newly developed Inertial Measurement Unit (IMU) encoder is included to improve the effectiveness of feature extraction using residual connections and channel alignment, allowing the efficient use of historical inertial data. A parallel encoding strategy uses two independently initialized encoders. Features are extracted from different dimensions by this strategy, strengthening the model’s ability to detect complex patterns. Experimental results for publicly shared datasets show that RWKV-VIO prioritizes computational efficiency and lightweight design. It significantly reduces model size and inference time compared to existing advanced methods while achieving top-ranked positioning accuracy among evaluated approaches.

Objectives To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. Methods We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training ( n  = 450) and a testing ( n  = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. Results The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. Conclusions This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. Key Points • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

The Hemispherical Resonator Gyro (HRG) is a solid-state and widely used vibrating gyroscope, especially in the field of deep space exploration. The flat-electrode HRG is a new promising type of gyroscope with simpler structure that is easier to be fabricated. In this paper, to cover the shortage of a classical generalized Coriolis Vibration Gyroscope model whose parameters are hard to obtain, the model of flat-electrode HRG is established by the equivalent mechanical model, the motion equations of unideal hemispherical shell resonator are deduced, and the calculation results of parameters in the equations are verified to be reliable and believable by comparing with finite element simulation and the reported experimental data. In order to more truthfully reveal the input and output characteristics of HRG, the excitation and detection models with assemble errors and parameters are established based on the model of flat-electrode capacitor, and they convert both the input and output forms of the HRG model to voltage changes across the electrodes rather than changes in force and capacitance. An identification method of assemble errors and parameters is proposed to evaluate and improve the HRG manufacturing technology and adjust the performance of HRG. The average gap could be identified with the average capacitance of all excitation and detection capacitors; fitting the approximate static capacitor model could identify the inclination angle and direction angle. With the obtained model, a firm and tight connection between the real HRG system and theoretical model is established, which makes it possible to build a fully functional simulation model to study the control and detection methods of standing wave on hemispherical shell resonator.

Highlights Host–guest inversion engineering is proposed to create poly(vinylidene fluoride-hexafluoropropylene) (PVH)-in-SiO 2 composite solid electrolytes with an original “polymer guest-in-ceramic host” architecture, exhibiting optimized interfacial contacts and comprehensive properties. The PVH-in-SiO 2 exhibits an overwhelming ionic conductivity of 1.32 × 10 −3  S cm −1 at 25 °C, with an ultralow residual solvent content of 2.9 wt%. In addition, the LiFePO 4 |PVH-in-SiO 2 |Li full cells deliver a significant capacity retention of 92.9% at an ultrahigh rate of 3C after 300 cycles at 25 °C. The host–guest inversion engineering is a versatile strategy, as proved by preparing Na + and K + -based PVH-in-SiO 2 composite solid electrolytes, delivering excellent ionic conductivity of 10 −4  S cm −1 at 25 °C (vs. 10 −6 –10 −5  S cm −1 of previous reports). Composite solid electrolytes (CSEs) are promising for solid-state Li metal batteries but suffer from inferior room-temperature ionic conductivity due to sluggish ion transport and high cost due to expensive active ceramic fillers. Here, a host–guest inversion engineering strategy is proposed to develop superionic CSEs using cost-effective SiO 2 nanoparticles as passive ceramic hosts and poly(vinylidene fluoride-hexafluoropropylene) (PVH) microspheres as polymer guests, forming an unprecedented “polymer guest-in-ceramic host” (i.e., PVH-in-SiO 2 ) architecture differing from the traditional “ceramic guest-in-polymer host”. The PVH-in-SiO 2 exhibits excellent Li-salt dissociation, achieving high-concentration free Li + . Owing to the low diffusion energy barriers and high diffusion coefficient, the free Li + is thermodynamically and kinetically favorable to migrate to and transport at the SiO 2 /PVH interfaces. Consequently, the PVH-in-SiO 2 delivers an exceptional ionic conductivity of 1.32 × 10 −3  S cm −1 at 25 °C (vs . typically 10 −5 –10 −4  S cm −1 using high-cost active ceramics), achieved under an ultralow residual solvent content of 2.9 wt% (vs . 8–15 wt% in other CSEs). Additionally, PVH-in-SiO 2 is electrochemically stable with Li anode and various cathodes. Therefore, the PVH-in-SiO 2 demonstrates excellent high-rate cyclability in LiFePO 4 |Li full cells (92.9% capacity-retention at 3C after 300 cycles under 25 °C) and outstanding stability with high-mass-loading LiFePO 4 (9.2 mg cm −1 ) and high-voltage NCM622 (147.1 mAh g −1 ). Furthermore, we verify the versatility of the host–guest inversion engineering strategy by fabricating Na-ion and K-ion-based PVH-in-SiO 2 CSEs with similarly excellent promotions in ionic conductivity. Our strategy offers a simple, low-cost approach to fabricating superionic CSEs for large-scale application of solid-state Li metal batteries and beyond.

The hemispherical resonator gyroscope (HRG) is a typical capacitive Coriolis vibratory gyroscope whose performance is inevitably influenced by the uneven electrostatic forces caused by the uneven excitation capacitance gap between the resonator and outer base. First, the mechanism of uneven electrostatic forces due to the significantly uneven capacitance gap in that the non-uniformity of the electrostatic forces can cause irregular deformation of the resonator and further affect the performance and precision of the HRG, was analyzed. According to the analyzed influence mechanism, the dynamic output error model of the HRG was established. In this work, the effect of the first four harmonics of the uneven capacitance gap on the HRG was investigated. It turns out that the zero bias and output error, caused by the first harmonic that dominates mainly the amplitude of the uneven capacitance gap, increase approximately linearly with the increase of the amplitude, and periodically vary with the increase of the phase. The effect of the other three harmonics follows the same law, but their amplitudes are one order of magnitude smaller than that of the first one, thus their effects on the HRG can be neglected. The effect of uneven electrostatic forces caused by the first harmonic on the scale factor is that its nonlinearity increases approximately linearly with the increase of the harmonic amplitude, which was analyzed in depth. Considering comprehensively the zero bias, the modification rate of output error, and scale factor nonlinearity, the tolerance towards the uneven excitation capacitance gap was obtained.

Highlights Indium-based metal–organic framework (In-MOF) is proposed as a multifunctional promoter to create poly(vinylidene fluoride–hexafluoropropylene) (PVH)/In-MOF (PVH-IM) composite solid polymer electrolyte, synchronously achieving a high ionic conductivity of 1.23 × 10 −3 S cm −1 and excellent electrochemical stability against Li anodes. In-MOF not only can adsorb and convert free residual solvents into bonded states to prevent their side reactions with Li anodes, but also induce inorganic-rich solid electrolyte interphase layers to prevent PVH from reacting with lithium anodes and promote uniform lithium deposition without dendrite growths. The Li|PVH-IM|Li symmetric cells maintain stable cycling for 5550 h at the current density of 0.2 mA cm −2 . In addition, all-solid-state LFP|PVH-IM|Li full cells deliver a significant capacity retention of 80.0% at a rate of 0.5C after 280 cycles at 25 °C. Fluoropolymers promise all-solid-state lithium metal batteries (ASLMBs) but suffer from two critical challenges. The first is the trade-off between ionic conductivity ( σ ) and lithium anode reactions, closely related to high-content residual solvents. The second, usually consciously overlooked, is the fluoropolymer’s inherent instability against alkaline lithium anodes. Here, we propose indium-based metal–organic frameworks (In-MOFs) as a multifunctional promoter to simultaneously address these two challenges, using poly(vinylidene fluoride–hexafluoropropylene) (PVH) as the typical fluoropolymer. In-MOF plays a trio: (1) adsorbing and converting free residual solvents into bonded states to prevent their side reactions with lithium anodes while retaining their advantages on Li + transport; (2) forming inorganic-rich solid electrolyte interphase layers to prevent PVH from reacting with lithium anodes and promote uniform lithium deposition without dendrite growth; (3) reducing PVH crystallinity and promoting Li-salt dissociation. Therefore, the resulting PVH/In-MOF (PVH-IM) showcases excellent electrochemical stability against lithium anodes, delivering a 5550 h cycling at 0.2 mA cm −2 with a remarkable cumulative lithium deposition capacity of 1110 mAh cm −2 . It also exhibits an ultrahigh σ of 1.23 × 10 −3 S cm −1 at 25 °C. Moreover, all-solid-state LiFePO 4 |PVH-IM|Li full cells show outstanding rate capability and cyclability (80.0% capacity retention after 280 cycles at 0.5C), demonstrating high potential for practical ASLMBs.

Objective This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer. Materials and Methods This retrospective study included women with breast cancer who underwent CEM preoperatively between 2018 and 2021. We included 241 patients, which were randomly assigned to either a training or a test set in a 7:3 ratio. Twenty-one features were visually described, including four clinical features and seventeen radiological features, these radiological features which extracted from the CEM. Three binary classifications of subtypes were performed: Luminal vs. non-Luminal, HER2-enriched vs. non-HER2-enriched, and triple-negative (TNBC) vs. non-triple-negative. A multinomial naive Bayes (MNB) machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method were used to select the most predictive features for the classifiers. The classification performance was evaluated using the area under the receiver operating characteristic curve. We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. Results The model that used a combination of low energy (LE) and dual-energy subtraction (DES) achieved the best performance compared to using either of the two images alone, yielding an area under the receiver operating characteristic curve of 0.798 for Luminal vs. non-Luminal subtypes, 0.695 for TNBC vs. non-TNBC, and 0.773 for HER2-enriched vs. non-HER2-enriched. The SHAP algorithm shows that “LE_mass_margin_spiculated,” “DES_mass_enhanced_margin_spiculated,” and “DES_mass_internal_enhancement_homogeneous” have the most significant impact on the model’s performance in predicting Luminal and non-Luminal breast cancer. “mass_calcification_relationship_no,” “calcification_ type_no,” and “LE_mass_margin_spiculated” have a considerable impact on the model’s performance in predicting HER2 and non-HER2 breast cancer. Conclusions The radiological characteristics of breast tumors extracted from CEM were found to be associated with breast cancer subtypes in our study. Future research is needed to validate these findings.

Objectives To evaluate the effectiveness of a geometric model (GM) as an adjunctive tool for radiologists to match lesions between craniocaudal (CC) and mediolateral (MLO) views. Methods A retrospective study was conducted on 711 patients who underwent mammography from January 2016 to August 2018. Two senior radiologists used bounding boxes to delineate lesions as the reference standard, calculated the absolute error (the shortest distance from the lesion center to the predicted curve) of GM, and compared it with the annular band (AB) and straight strip (SS) methods. Four radiologists of varying seniority levels were tasked with localizing the corresponding lesion in MLO view using a bounding box, based on the given lesion in CC views, and recording reading time per case with or without GM assistance. The Dice coefficient was used to evaluate the overlap between the bounding box and the reference standard. Results Overall, 499 calcification and 212 mass pairs were evaluated. GM outperformed both AB and SS, yielding a median absolute error of 3.03 mm (IQR 1.45–5.55 mm) versus 5.78 mm (IQR 2.44–10.71 mm) for AB and 4.59 mm (IQR 1.91–8.19 mm) for SS ( P  < 0.001). With GM assistance, all four radiologists achieved improved Dice coefficients and reduced reading times (all P  < 0.001). Stratified analysis by lesion conspicuity demonstrated that GM assistance significantly enhanced Dice coefficients for all radiologists in the low-conspicuity group and improved matching consistency for junior radiologists. Conclusion The geometric model holds substantial promise as a valuable tool to assist radiologists in more effectively localizing lesions in ipsilateral mammograms, thereby potentially enhancing diagnostic accuracy and efficiency.

Knowledge about soil nitrogen (N) and phosphorus (P) concentrations, stocks, and stoichiometric ratios is crucial for understanding the biogeochemical cycles and ecosystem function in arid mountainous forests. However, the corresponding information is scarce, particularly in arid mountainous forests. To fill this gap, we investigated the depth and elevational patterns of the soil N and P concentrations and the N: P ratios in a Picea schrenkiana forest using data from soil profiles collected during 2012-2017. Our results showed that the soil N and P concentrations and the N: P ratios varied from 0.15 g kg-1 to 0.56 g kg-1 (average of 0.31 g kg-1), from 0.09 g kg-1 to 0.16 g kg-1 (average of 0.12 g kg-1), and from 2.42 g kg-1 to 4.36 g kg-1 (average of 3.42 g kg-1), respectively; additionally, values significantly and linearly decreased with soil depth. We did not observe a significant variation in the soil N and P concentrations and the N: P ratios with the elevational gradient. In contrast, our results revealed that the mean annual temperature and mean annual precipitation exhibited a more significant influence on the soil N and P concentrations and the N: P ratios than did elevation. This finding indicated that climatic variables might have a more direct impact on soil nutrient status than elevation. The observed relationship among the soil N and P concentrations and the N: P ratios demonstrated that the soil N was closely coupled with the soil P in the P. schrenkiana forest.

Objective: To evaluate the mammographic features, clinicopathological characteristics, treatments, and prognosis of pure and mixed tubular carcinomas of the breast. Materials and methods: Twenty-five tubular carcinomas were pathologically confirmed at our hospital from January 2011 to May 2019. Twenty-one patients underwent preoperative mammography. A retrospective analysis of mammographic features, clinicopathological characteristics, treatment, and outcomes was performed. Results: Altogether, 95% of the pure tubular carcinomas (PTCs) and mixed tubular carcinomas (MTCs) showed the presence of a mass or structural distortions on mammography and the difference was not statistically significant (P = .373). MTCs exhibited a larger tumor size than PTCs (P = .033). Lymph node metastasis was more common (P = .005) in MTCs. Patients in our study showed high estrogen receptor and progesterone receptor positivity rates, but low human epidermal growth factor receptor 2 positivity rate. The overall survival rate was 100% in both PTC and MTC groups and the 5-year disease-free survival rates were 100% and 75%, respectively with no significant difference between the groups (P = .264). Conclusion: Tubular carcinoma of the breast is potentially malignant and has a favorable prognosis. Digital breast tomosynthesis may improve its detection. For patients with PTC, breast-conserving surgery and sentinel lymph node biopsy are recommended based on the low rate of lymph node metastasis and good prognosis. MTC has a relatively high rate of lymph node metastasis and a particular risk of metastasis. Axillary lymph node dissection should be performed for MTC even if the tumor is smaller than 2 cm.