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Geomagnetic navigation has become a hot spot in current research because of its characteristics of passiveness and good concealment. However, the magnetic interference from various ferromagnetic substances, electronic equipment, etc., of the carrier will be superimposed on the geomagnetic field, causing magnetometer measurement errors, thus affecting navigation accuracy. In practice, due to the limited maneuverability of the carrier, sufficient geomagnetic observation data cannot be obtained, resulting in the observation equation used for carrier magnetic interference compensation to be seriously pathological. To achieve the compensation of carrier magnetic interference, this paper proposes the total least squares method based on the ridge regression using the L curve to solve ridge parameters. This method can effectively suppress the measurement noise that exists on both sides of the observation equation, and is suitable for alleviating the pathological effects of carrier magnetic interference compensation. Experimental results show that the compensated magnetometer measurement error is reduced to 3% of the carrier magnetic interference by using the method proposed in this paper, which obtains more stable and accurate parameter estimates. [ABSTRACT FROM AUTHOR]

Water leakage defects often occur in underground structures, leading to accelerated structural aging and threatening structural safety. Leakage identification can detect early diseases of underground structures and provide important guidance for reinforcement and maintenance. Deep learning-based computer vision methods have been rapidly developed and widely used in many fields. However, establishing a deep learning model for underground structure leakage identification usually requires a lot of training data on leakage defects, which is very expensive. To overcome the data shortage, a deep neural network method for leakage identification is developed based on transfer learning in this paper. For comparison, four famous classification models, including VGG16, AlexNet, SqueezeNet, and ResNet18, are constructed. To train the classification models, a transfer learning strategy is developed, and a dataset of underground structure leakage is created. Finally, the classification performance on the leakage dataset of different deep learning models is comparatively studied under different sizes of training data. The results showed that the VGG16, AlexNet, and SqueezeNet models with transfer learning can overall provide higher and more stable classification performance on the leakage dataset than those without transfer learning. The ResNet18 model with transfer learning can overall provide a similar value of classification performance on the leakage dataset than that without transfer learning, but its classification performance is more stable than that without transfer learning. In addition, the SqueezeNet model obtains an overall higher and more stable performance than the comparative models on the leakage dataset for all classification metrics. [ABSTRACT FROM AUTHOR]

Summary: Light can influence many psychophysiological functions beyond vision, including alertness, circadian rhythm, and sleep, namely the non‐image forming (NIF) effects of light. Melanopic equivalent daylight illuminance (mel‐EDI) is currently recommended as the predictor of the NIF effects of light. Although light dose is also critical for entraining and regulating circadian cycle, it is still unknown whether relatively low mel‐EDI light exposure for prolonged duration in the evening would affect pre‐sleep arousal and subsequent sleep. In all, 18 healthy college students (10 females, mean [standard deviation] age 21.67 [2.03] years) underwent 2 experimental nights with a 1 week interval in a simulated bedroom environment. During experimental nights, participants were either exposed to high or low mel‐EDI light (73 versus 38 lx mel‐EDI, 90 versus 87 photopic lx at eye level, 150 photopic lx at table level) for 3.5 h before regular bedtime, and their sleep was monitored by polysomnography. Subjective sleepiness, mood, and resting‐state electroencephalography during light exposure were also investigated. Results showed no significant differences in sleep structure and sleep quality between the two light conditions, whereas 3.5 h of exposure to high versus low mel‐EDI light induced marginally higher physiological arousal in terms of a lower delta but higher beta power density before sleep, as well as a lower delta power density during sleep. Moreover, participants felt happier before sleep under exposure to high versus low mel‐EDI light. These findings together with the current literature suggest that evening prolonged relatively low mel‐EDI light exposure may mildly increase arousal before and during sleep but affected sleep structure less. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to assess the association of neutrophil lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and system inflammation response index (SIRI) with the all-cause mortality and diabetes-cardiovascular mortality in participants with diabetic retinopathy (DR). Methods: A total of 572 participants with DR from NHANES were included, and divided into survival group (n = 440) and all-cause death group (n = 132). NLR = neutrophil count/lymphocyte count, MLR = monocyte count/lymphocyte count, SIRI = (neutrophil count × monocyte count)/lymphocyte count. We utilized the NHANES Public-Use Linked Mortality File through April 26, 2022, to determine mortality status. Diabetes-cardiovascular death was defined as death resulting from heart disease, cerebrovascular disease, or diabetes mellitus. The Spearson Correlation Analysis, Kaplan-Meier curves, Cox proportional hazards regression models, Restricted cubic spline plots and Decision Curve Analysis were used. Results: The all-cause mortality and diabetes-cardiovascular mortality were significantly higher in NLR ≥ 1.516, MLR ≥ 0.309, SIRI ≥ 0.756, and NLR + MLR + SIRI subgroups than NLR < 1.516, MLR < 0.309, SIRI < 0.756 subgroups, and other participants except NLR + MLR + SIRI (all P < 0.05). The HR of NLR, MLR, SIRI, NLR + MLR + SIRI for all-cause mortality were 1.979(1.13–3.468), 1.850(1.279–2.676), 1.821(1.096–3.025), 1.871(1.296–2.703), respectively. The hazard ratio of NLR, MLR, SIRI, NLR + MLR + SIRI for diabetes-cardiovascular mortality were 2.602(1.028–6.591), 2.673(1.483–4.818), 2.001(0.898–4.459), 2.554(1.426–4.575), respectively. In the restricted cubic spline plots, the relationship between NLR, MLR, SIRI and HR of all-cause mortality and diabetes-cardiovascular mortality was overall as "J" shaped. In both age < 60 and age > 60 years participants, the all-cause mortality and diabetes-cardiovascular mortality were significantly higher in NLR ≥ 1.516, MLR ≥ 0.309, SIRI ≥ 0.756, and NLR + MLR + SIRI subgroups than NLR < 1.516, MLR < 0.309, SIRI < 0.756 subgroups, and other participants except NLR + MLR + SIRI (all P < 0.05). Conclusion: NLR, MLR, and SIRI may be three independent prognostic predictors for all-cause mortality and diabetes-cardiovascular mortality among individuals with DR. In practical clinical applications, combining NLR, MLR, and SIRI may enhance the prediction of all-cause mortality and diabetes-cardiovascular mortality in DR. [ABSTRACT FROM AUTHOR]

Forests and grasslands play an important role in carbon cycling. They not only absorb CO2 from the air through vegetation biomass and soil carbon sinks, but also reduce and control the horizontal transport of soil carbon (i.e., reinforcing soil carbon storage via soil conservation), thus avoiding erosion-induced CO2 emissions. In this study, vegetation biomass and soil carbon sinks, soil carbon reinforcement and reduced carbon emissions via soil conservation by forests and grasslands were quantified on the scale of the whole of China. The analysis was based on the distribution of biomass and the soil carbon pool and soil erosion rates derived from national surveys, as well as carbon density values from field surveys and literature. In 2021, forests and grasslands in China generated 394.18 Mt C/year (y) of steady-state carbon sinks through vertical biomass and soil absorption. The biomass carbon sinks of grasslands, and those of leaves, twigs, flowers and fruits of the forests, were not taken into account when quantifying the stable biomass sink, because they can become net producers of CO2 due to seasonal withering and carryover, or they can form soil organic carbon as potential soil carbon sinks. The amount of horizontal soil carbon reinforcement in China's forests and grasslands in 2021 was 20.31 Mt C/y, which was positively correlated with the reduction in the water erosion area; consequently, vertical emissions of approximately 14.89–29.78 Mt of CO2 into the atmosphere were avoided. Overall, in 2021, China's forests and grasslands absorbed atmospheric CO2 and reduced emissions by 1.46–1.47 Gt CO2/y, equivalent to approximately 13% of China's annual fossil CO2 emissions. This study demonstrates the fact that the adoption of forest and grassland measures sequesters carbon in soil and biota and reduces the risks of CO2 emissions by both vertical and horizontal paths, which is important for achieving carbon neutrality and mitigating climate change. [ABSTRACT FROM AUTHOR]