In recent years, various algorithms using random finite sets (RFS) to solve the issue of simultaneous localization and mapping (SLAM) have been proposed. Compared with the traditional method, the ...advantage of the RFS method is that it can avoid data association, landmark appearance and disappearance, missed detections, and false alarms in Bayesian recursion. There are many problems in the existing robot SLAM methods, such as low estimation accuracy, poor back-end optimization, etc. On the basis of previous studies, this paper presents a labeled random finite set (L-RFS) SLAM method. We describe a scene where the sensor moves along a given path and avoids obstacles based on the L-RFS framework. Then, we use the labeled multi-Bernoulli filter (LMB) to estimate the state of the sensor and feature points. At the same time, the B-spline curve is used to smooth the obstacle avoidance path of the sensor. The effectiveness of the algorithm is verified in the final simulation.
Aerobic exercise is widely recognized for improving mental health and reducing negative emotions, including anxiety. However, research on its role in preventing and treating postpartum depression ...(PPD) has yielded inconsistent results. Some studies show positive effects on PPD symptoms, while others find limited impact, suggesting various factors at play, such as exercise type, intensity, and individual differences. To address this gap, our study aims to comprehensively gather evidence on the preventive and therapeutic effects of aerobic exercise for PPD. We'll focus on differences in exercise program design and implementation, exploring how these factors impact intervention outcomes. By identifying effective exercise approaches, we aim to provide more comprehensive exercise prescription recommendations for this vulnerable population. We conducted a quantitative systematic review of the study in 5 representative databases for the effect of aerobic exercise on PPD. Meta-analysis and network meta-analysis were performed with Review-Manager.5.4 and Stata.16.0 software, respectively. This study has been registered on the official Prospero website, and the registration code is CRD42023398221. Twenty-six studies with 2,867 participants were eventually included and the efficacy of aerobic exercise in preventing and treating postpartum depression is significant compared to standard care. (MD = -1.90; 95%CL: -2.58 to -1.21; I.sup.2 = 86%). Subgroup analysis suggests that the intervention objective (prevention vs. treatment) of exercise could potentially be a source of heterogeneity in this study, as the "Test for subgroup difference" revealed the presence of significant distinctions (p = 0.02 0.05) and the individual vs. team subgroup comparison (p = 0.78 > 0.05). Nonetheless, when assessing their effect sizes Subtotal (95%CL), the supervised exercise group -1.66 (-2.48, -0.85) exhibited a slightly better performance than the unsupervised exercise group -1.37 (-1.86, -0.88), while the team exercise group -1.43 (-1.94, -0.93) slightly outperformed the individual exercise group -1.28 (-2.23, -0.33). Network meta-analysis indicated that moderate intensity (35~45 min) group demonstrated a more pronounced intervention effect compared to low intensity (50~60 min) group -2.63 (-4.05, -1.21) and high intensity (20~30 min) group -2.96 (-4.51, -1.41), while the 3~4 times/week group had a more significant intervention effect compared to 1~2 times/week groups -2.91 (-3.99, -1.83) and 5~6 times/week groups -3.28 (-4.75, -1.81). No significant differences were observed in pairwise comparisons of intervention effects among the five common types of aerobic exercises. (95%CL including 0). The Surface Under the Cumulative Ranking curve (SUCRA) results align with the findings mentioned above and will not be reiterated here. The efficacy of aerobic exercise in preventing and treating postpartum depression is significant compared to standard care, with a greater emphasis on prevention. The optimal prescribed exercise volume for intervention comprises a frequency of 3~4 exercise sessions per week, moderate intensity (35~45 minutes). Currently, several uncharted internal factors influence the optimal intervention effect of aerobic exercise, such as the potential enhancement brought by team-based and supervised exercise. Given the absence of significant differences in certain results and the limitations of the study, it is essential to exercise caution when interpreting the outcomes. Further research is needed in the future to provide a more comprehensive understanding.
To exploit the quantum coherence of electron spins in solids in future technologies such as quantum computing, it is first vital to overcome the problem of spin decoherence due to their coupling to ...the noisy environment. Dynamical decoupling, which uses stroboscopic spin flips to give an average coupling to the environment that is effectively zero, is a particularly promising strategy for combating decoherence because it can be naturally integrated with other desired functionalities, such as quantum gates. Errors are inevitably introduced in each spin flip, so it is desirable to minimize the number of control pulses used to realize dynamical decoupling having a given level of precision. Such optimal dynamical decoupling sequences have recently been explored. The experimental realization of optimal dynamical decoupling in solid-state systems, however, remains elusive. Here we use pulsed electron paramagnetic resonance to demonstrate experimentally optimal dynamical decoupling for preserving electron spin coherence in irradiated malonic acid crystals at temperatures from 50 K to room temperature. Using a seven-pulse optimal dynamical decoupling sequence, we prolonged the spin coherence time to about 30 s; it would otherwise be about 0.04 s without control or 6.2 s under one-pulse control. By comparing experiments with microscopic theories, we have identified the relevant electron spin decoherence mechanisms in the solid. Optimal dynamical decoupling may be applied to other solid-state systems, such as diamonds with nitrogen-vacancy centres, and so lay the foundation for quantum coherence control of spins in solids at room temperature.
•Freeze-thaw induced landslides (FTILs) on grasslands were systematically examined.•Soil characteristics and topography were intrinsic factors controlling FTILs.•Increased rainfall and thickening ...active layer were direct drivers of FTILs.•Combining multiple monitoring methods is the trend for early warning of FTILs.
Landslides induced by freeze–thaw processes on grasslands are one of the major geohazards, and their scale and frequency are increasing as the global warms. Freeze-thaw induced landslides degrade surface vegetation and soil properties, reduce biodiversity, intensify landscape fragmentation, and lead to losses in economy, human and animal lives. Despite substantial progress in research on landslides, there has been little study focused on how ground freeze–thaw events affect landslides. By critically analyzing previous studies, this paper proposes a conceptual framework for the forms and types, development, dominant factors, monitoring techniques, and impact mechanisms of freeze–thaw induced landslides. Landslides are controlled by soil characteristics and topographic slope, which are major intrinsic determinants. Increased rainfall, rising temperatures, and thickening active layer due to climate change are all direct drivers of freeze–thaw induced landslides. Vegetation conditions, animal behavior interference, and wind erosion all affect the occurrence and development process of landslides by modifying vegetation cover, soil physical and chemical properties, and structure. Currently, landslide monitoring techniques have evolved rapidly with improved efficiency and accuracy, but with only few applications for freeze–thaw induced landslides. There are a variety of prediction models for landslides, but few consider freeze–thaw effects and lack field validation. The new perspective on the occurring types and dominant factors enhances theoretical understanding of the formation mechanisms, which helps further monitor and analysis of freeze–thaw induced landslides. Future studies should concentrate on the coupling mechanism of multiple factors and the development of an accurate prediction system, which will greatly benefit the understanding and early detection of freeze–thaw induced landslides.
Brain injury, a common complication in preterm infants, includes the destruction of the key structural and functional connections of the brain and causes neurodevelopmental disorders; it has high ...morbidity and mortality rates. The exact mechanism underlying brain injury in preterm infants is unclear. Intestinal flora plays a vital role in brain development and the maturation of the immune system in infants; however, detailed understanding of the gut microbiota-metabolite-brain axis in preterm infants is lacking. In this review, we summarise the key mechanisms by which the intestinal microbiota contribute to neurodevelopment and brain injury in preterm infants, with special emphasis on the influence of microorganisms and their metabolites on the regulation of neurocognitive development and neurodevelopmental risks related to preterm birth, infection and neonatal necrotising enterocolitis (NEC). This review provides support for the development and application of novel therapeutic strategies, including probiotics, prebiotics, synbiotics, and faecal bacteria transplantation targeting at brain injury in preterm infants.
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•The gut-microbiota-brain axis regulates vagus nerve, endocrine and immune pathways.•Preterm birth, dysbiosis and infection cause brain injury by acting on gut microbiota.•The activation of γδ T cells by the microbiome contributes to preterm brain injury.•Inflow of inflammatory cytokines across the blood-brain barrier leads to brain injury.•Metabolites of the gut microbiota may also serve as neuro-signal transmitters.
To examine the level of number line estimation (NLE) in Chinese children with respect to representations of both numerical (Arabic numerals) and non-numerical symbols (dots), a total of 192 Chinese ...preschoolers aged between 4 and 5 years participated in four different NLE tasks. These tasks were paired to evaluate the accuracy and patterns of children’s estimations in both numerical and non-numerical symbol contexts. Our findings indicate that, for Chinese preschoolers, relatively precise numerical symbol representations begin to emerge as early as 4 years of age. The accuracy of number line estimates for both 4- and 5-year-old children gradually increases in tasks involving both numerical and non-numerical symbols. Additionally, the development and patterns observed in the number line estimates of 4- and 5-year-old Chinese preschoolers are similar in both numerical symbol and non-numerical symbol tasks. These results indicate that the initiation of relatively precise numerical symbol representation and the turning point in the developmental trajectory, where the relatively precise representation for numerical symbols surpasses that of non-numerical ones, occur earlier in Chinese children than in their Western counterparts.
Emerging evidences exposed that long noncoding RNAs (lncRNAs) play important roles in various tumor progression including breast cancer (BC). However, the role of IncRNA ADP-dependent glucokinase ...antisense RNA 1 (ADPGK-AS1) in BC progression remains undiscovered. Hence, this study aimed to investigate the role of ADPGK-AS1 in BC. qRT-PCR was performed to investigate ADPGK-AS1 expression level in BC tissues and cell lines. The effect of ADPGK-AS1 knockdown on BC cellular process was assessed by loss-of-function assay. Luciferase reporter and RIP assay were performed to investigate the combination between ADPGK-AS1 and miR-3196. The combination between miR-3196 and orthodenticle homeobox 1 (OTX1) was verified by luciferase reporter assay. Finally, rescue assays were performed to confirm the effects of ADPGK-AS1/miR-3196/OTX1 axis on BC development. ADPGK-AS1 expression level was upregulated in BC tissues and cell lines. High expression of ADPGK-AS1 predicted poor prognosis for BC patients. Functionally, ADPGK-AS1 promoted cell proliferation, migration, induced epithelial-mesenchymal transition (EMT) process, and suppressed cell apoptosis. Mechanistically, ADPGK-AS1 acted as a miR-3196 sponge to release OTX1 in BC cells. Currently, ADPGK-AS1 acted as a competing endogenous RNA (ceRNA) via modulating miR-3196/OTX1 axis in BC.
As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In ...this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.
Due to the unique features including well-defined void space and high surface-to-volume ratio, non-noble metal catalysts with hollow structures have received intensive researches in dye-sensitized ...solar cells (DSSCs) and electrochemical water splitting. Herein, we prepare a series of cobalt-incorporated molybdenum sulfide hollow nanoboxes (denoted as Co-MoSx NBs) via a one-step fleet template conversion from cube-shaped Co-based zeolitic imidazolate framework-67 which precipitated without prolonged aged process. During above transformation, it is notable that the surface morphologies of nanobox can be effectively tailored by the proportion of MoS42−. The as-obtained Co-MoSx NBs with well-defined boxed structure and the appropriate doped ratio are developed as bifunctional catalysts for accelerating both reduction of I3− in DSSCs and hydrogen evolution reaction (HER). As expected, a high power conversion efficiency (9.64%) is achieved by Co-MoSx-1/3 NBs based DSSC under AM 1.5G irradiation, which is much preceded to that of Pt (8.39%). Meanwhile, the practical utilization of Co-MoSx-1/3 NBs for HER yields a low onset overpotential (35 mV) and a small Tafel slope (61.4 mV decade−1) in alkaline medium.
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•Co-MoSx hollow nanoboxes have been synthesized from a cubic ZIF-67 template.•Their surface morphologies can be modulated by changing the reactant ratio.•The PCE of Co-MoSx-2 (9.64%) in DSSCs is much higher than that of Pt (8.39%).•Co-MoSx-2 also performs excellent catalytic activity for HER in alkaline medium.
Most failures in steel materials are due to fatigue damage, so it is of great significance to analyze the key features of fatigue strength (FS) in order to improve fatigue performance. This study ...collected data on the fatigue strength of steel materials and established a predictive model for FS based on machine learning (ML). Three feature-construction strategies were proposed based on the dataset, and compared on four typical ML algorithms. The combination of Strategy Ⅲ (composition, heat-treatment, and atomic features) and the GBT algorithm showed the best performance. Subsequently, input features were selected step by step using methods such as the analysis of variance (ANOVA), embedded method, recursive method, and exhaustive method. The key features affecting FS were found to be TT, mE, APID, and Mo. Based on these key features and Bayesian optimization, an ML model was established, which showed a good performance. Finally, Shapley additive explanations (SHAP) and symbolic regression (SR) are introduced to improve the interpretability of the prediction model. It had been discovered through SHAP analysis that TT and Mo had the most significant impact on FS. Specifically, it was observed that 160 < TT < 500 and Mo > 0.15 was beneficial for increasing the value of FS. SR was used to establish a significant mathematical relationship between these key features and FS.
