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Abstract Background Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma. Methods We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast‐enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics‐clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots. Results The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95–0.99) on the training set and 0.90 (95% CI: 0.82–0.95) on the validation set. The radiomics‐clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89–0.96) on the training set and 0.91 (95% CI: 0.85–0.97) on the validation set. Conclusions MLP‐based radiomics and radiomics‐clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.

With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model’s inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach.

Abstract Background Neuroblastoma is one common pediatric malignancy notorious for high temporal and spatial heterogeneities. More than half of its patients develop distant metastases involving vascularized organs, especially the bone marrow. It is thus necessary to have an economical, noninvasive method without much radiation for follow‐ups. Radiomics has been used in many cancers to assist accurate diagnosis but not yet in bone marrow metastasis in neuroblastoma. Methods A total of 182 patients with neuroblastoma were retrospectively collected and randomly divided into the training and validation sets. Five‐hundred and seventy‐two radiomics features were extracted from magnetic resonance imaging, among which 41 significant ones were selected via T‐test for model development. We attempted 13 machine‐learning algorithms and eventually chose three best‐performed models. The integrative performance evaluations are based on the area under the curves (AUCs), calibration curves, risk deciles plots, and other indexes. Results Extreme gradient boosting, random forest (RF), and adaptive boosting were the top three to predict bone marrow metastases in neuroblastoma while RF was the most accurate one. Its AUC was 0.90 (0.86–0.93), F1 score was 0.82, sensitivity was 0.76, and negative predictive value was 0.79 in the training set. The values were 0.82 (0.71–0.93), 0.80, 0.75, and 0.92 in the validation set, respectively. Conclusions Radiomics models are likely to contribute more to metastatic diagnoses and the formulation of personalized healthcare strategies in clinics. It has great potential of being a revolutionary method to replace traditional interventions in the future.

The Yellow River Basin (YRB) constitutes an important grain-producing area, as well as an economic core area in China. However, drought and water shortage have always been important factors restricting the agricultural sustainable development of this region. The intensity of agricultural drought in the YRB and the factors affecting it need to be urgently clarified. Based on this, this study attempts to assess the spatio-temporal characteristics of agricultural drought in the YRB using the improved traditional temperature vegetation dryness index (ITVDI) and to identify the type and intensity of constraint effects of human activities (population, GDP, land use type), climate change (temperature, precipitation) and topography (elevation, slope) on drought in the YRB with the constraint line method. Results appeared that from 2001 to 2015, the drought intensity in the YRB exhibited fluctuating changes, while it decreased after 2015. Spatially, the drought characteristics of the YRB existed large heterogeneous, with drought showing a significant increase in the northwestern and downstream areas and a significant decrease in the central part of the basin. There were eight types of constraining effects of human activities, climate change, and topographic factors on drought intensity in the YRB, including U-shaped curve, positive linear, negative linear, logarithmic, negative convex, positive convex, exponential, and hump-shaped. Precipitation and temperature were the most direct factors affecting drought intensity and would exert a binding effect on both the upper and lower boundaries of drought intensity. The constraint effect of precipitation on drought varied in space. In the upstream and midstream, precipitation constrained the upper and lower boundaries of ITVDI, and the constraint effect increased with the increase of precipitation. In the downstream, the constraint effect of precipitation did not change significantly, and the maximum value of ITVDI was always less than 0.8. Temperature had upper and lower boundary constraint effects at three scales. The constraining effects of elevation, slope, GDP, and population were stable across scales. It should be noted that the constraint effect of land use type on drought exhibited U-shaped, and the threshold point of minimum values appeared in the areas covered by grassland and water. The constraint line method can improve the drought monitoring ability of ITVDI, as well as help to clarify the constraint factors and mechanism of drought intensity in the YRB and provide scientific decision-making support to mitigate regional drought and promote regional sustainable development.

Abstract Balanced against this increase in energy demand are energy security and climate warming issues. Restricting the increase in energy demand for peaking carbon emissions will not only directly affect production in sectors but also cause a broader indirect economic impact through intricate industrial linkages. Under the two requirements of adapting to warming and peaking carbon emissions before 2030, we accordingly constructed developmental and restrictive scenarios of China and improved the adaptive Input–Output model from the demand side to evaluate the indirect economic impact. The main findings were as follows: (a) the increase in end‐use demand for conventional energy in 2030 will be 24.43% of energy consumption in 2017 (TEC2017) under the restrictive scenario, and the reduction of this increase will be about 42.98% of TEC2017 in comparison with the developmental scenario. (b) Due to this reduction, the possible indirect damage in 2030 will be 14.96% of the GDP in 2017, and the clean energy level in 2030 will be 20 times the hydropower generation in 2017, which can offset this damage. (c) The industrial sector with high output and high energy will suffer the most indirect damage. The quantitative results demonstrate the great challenges that China will face in clean energy transition.

BackgroundAcute hematologic toxicity (HT) is a common complication during radiotherapy of cervical cancer which may lead to treatment delay or interruption. Despite the use of intensity-modulated radiation therapy (IMRT) with the pelvic bone marrow (PBM) sparing, some patients still suffer from acute HT. We aimed to identify predictors associated with HT and develop a nomogram for predicting grade 2 or higher (G2+) acute HT in cervical cancer following the PBM sparing strategy.MethodsThis study retrospectively analyzed 125 patients with cervical cancer who underwent IMRT with the PBM sparing strategy at our institution. Univariate and multivariate logistic regression, best subset regression, and least absolute shrinkage and selection operator (LASSO) regression, respectively, were used for predictor screening, and Akaike information criterion (AIC) was used to determine the best model for developing the nomogram. Finally, we quantified the risk of G2+ acute HT based on this model to establish a risk stratification.ResultsThe independent predictors used to develop the nomogram were histological grade, pre-radiotherapy chemotherapy, pre-radiotherapy HT, and radiotherapy [IMRT alone vs. concurrent chemoradiotherapy (CCRT)] which were determined by the univariate and multivariate logistic regression with the minimum AIC of 125.49. Meanwhile, the heat map showed that there is no multicollinearity among the predictors. The nomogram was well-calibrated to reality, with a Brier score of 0.15. The AUC value was 0.82, and the median Brier score and AUC in 1000 five-fold cross-validation were 0.16 and 0.80, respectively. The web version developed together was very easy to use. The risk stratification indicated that high-risk patients (risk point > 195.67) were more likely to develop G2+ acute HT [odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.30–3.05].ConclusionThis nomogram well-predicted the risk of G2+ acute HT during IMRT in cervical cancer after the PBM sparing strategy, and the constructed risk stratification could assist physicians in screening high-risk patients and provide a useful reference for future prevention and treatment strategies for acute HT.

The Qinghai–Tibet Plateau is a region sensitive to climate change with significant changes in topography, and the increase in extreme precipitation in the region easily leads to landslides and debris flows. To mitigate the effects of climate change, the Chinese government has pledged to an emission reduction target that achieves a “carbon peak’” in 2030 and being “carbon neutral” in 2060 at a huge cost. In order to explore the quantitative flood risk at the county level in Qinghai province (the core province of the Qinghai–Tibet Plateau) and the contribution of emission reduction efforts to risk mitigation, this article constructs a loss rate curve based on the data of more than 1,000 single flood disasters from 2009 to 2019 through government cooperation and the data of observed cumulative precipitation. Combined with the latest CMIP6 climate model data, the temporal and spatial variation characteristics of the flood loss ratio in Qinghai province from 2020 to 2060 are quantitatively characterized. The results show the following: 1) the curve of the flood loss rate in Qinghai province from 2020 to 2060 may present a trend of “rising in the early stage, stable in the middle stage, and declining in the late stage” under low (SSP126) and medium (SSP245) emission scenarios and presents a trend of rising fluctuation under the high-emission (SSP585) scenario. The flood loss ratio under SSP585 is 10 times higher than that under SSP126; 2) the flood risk of 44 counties from 2020 to 2060 in Qinghai province may be concentrated in the southeast region, with Jiuzhi county, Banma county, Nangqian county, Gande county, and Dari county being the most prominent; (3) compared with SSP585, the Qinghai province in SSP126 may cumulatively avoid about 24 billion CNY in economic losses from 2020 to 2060 and avoid nearly 600 million CNY in losses per year, which is equivalent to 93% of Qinghai province’s special funds for ecological and environmental protection in 2019. The research aims to provide theoretical and data support for flood disaster risk prevention and management in China’s high-altitude areas and to promote the initiative of emission reduction in China and even the world.

Abstract Food security is closely related to individual livelihood and social stability, and assessing the state of food security is essential for making agricultural policies. Nonetheless multi‐scale assessment of national food security state with explicit consideration of availability and access remains to be resolved. In this study, a spatially explicit crop model was used to simulate the production of major crops in China (i.e., wheat, maize, rice, and soybeans). Food production (as simulated by the crop model) and GDP per capita relative to the global average were then used to develop food security indicators to evaluate China's food security levels on grid, provincial, and economic zone scales. The Getis–Ord method was further applied to analyze the spatial aggregation of food security and food insecurity provinces. Results show that food security situation improved in mainland China during 1990–2015, attributed largely to the enhanced purchasing power. On the grid scale, changes in food security state went through two stages: Before 2000, the regions in the eastern part of the Hu Line were food security, while the regions in the western part were food insecurity; after 2000, the state of food security greatly improved from insecurity to security in Northwest China, and the areas of the highest security level considerably increased in eastern China. On the provincial scale, food security state in most provinces improved or almost unchanged over time. Exception was found in coastal provinces, with food security state was greatly affected by urbanization. Food security hot spots were primarily detected in central China, while cold spots were detected in Xinjiang, Qinghai, and Tibet. On the economic zone scale, food security areas in the eastern part decreased during 1990–2010 but increased thereafter, while food security areas in the other three economic zones increased from 1990 to 2015. To ensure food security, China should maintain the arable land areas, improve the quality of farmlands, mobilize farming enthusiasm, and advance agricultural marketization.

In this paper, we propose a misalignment correct method and a particle detection algorithm to improve the accuracy in the quality inspection of the LCD module after the anisotropic conductive film (ACF) bonding. We use only one camera to acquire images of multiple positions in order to establish the transformation from the image space to the world coordinate. Our method can accurately determine the center of rotation of the carrier table and calculate the deviation of position and angle of the tested module. Compared to traditional ways that rely on multiple cameras to align the large-sized product, our method has the advantages of simple structure, low cost, and fast calibration process. The particle detection is performed after positioning all bumps of the bonded module. The gray morphology-based algorithm is developed to detect the extreme point of every particle and refine the particle result through blob analysis. This method reduces the over-checking rate and performs better on the detection precision for dense particles. We verify the effectiveness of our proposed methods in our experiments. The alignment error can be less than 0.05 mm, and the accuracy of the particle detection is 93% while the recall rate is 92.4%.

Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in defect segmentation tasks. To tackle these problems, we proposed a mask-guided background-preserving defect generation method, MDGAN (mask-guided defect generation adversarial networks). First, to preserve the normal background and provide accurate annotations for the generated defect samples, we proposed a background replacement module (BRM), to add real background information to the generator and guide the generator to only focus on the generation of defect content in specified regions. Second, to guarantee the quality of the generated complex texture defects, we proposed a double discrimination module (DDM), to assist the discriminator in measuring the realism of the input image and distinguishing whether or not the defects were distributed at specified locations. The experimental results on metal, fabric, and plastic products showed that MDGAN could generate diversified and high-quality defect samples, demonstrating an improvement in detection over the traditional augmented samples. In addition, MDGAN can transfer defects between datasets with similar defect contents, thus achieving zero-shot defect detection.

Abstract Climate change leads to heat‐related changes in labor productivity, which have additional economic impacts. Based on a framework that considers the impacts evolving from climate change to labor productivity to economic impact, we estimate the changes in labor productivity for indoor and outdoor activities and different work intensities at the grid level in China under a wide range of climatic and socioeconomic conditions and then evaluate the economic impacts in seven regions and eight sectors. The results show that (a) the negative impacts of labor productivity are concentrated in outdoor sectors, and the labor productivity of indoor sectors will decrease slightly or even increase due to high air‐conditioning device penetration rates under relatively optimistic scenarios. (b) The national results show that total economic impacts increase by 0.28%–0.61% of the GDP for each 1°C rise in the temperature, and the total economic impacts of labor productivity reductions in the most pessimistic scenario reach 1.15%–2.67% of the GDP in 2100. (c) The regional results indicate that the regions with lower labor productivity impacts (Northwest and Northeast China) still suffer large economic impacts, highlighting the importance of economic impact assessments across the regions. (b) The sectors in the seven regions of China that are most sensitive to climate change are agriculture and construction. The economic impacts in the manufacturing and service sectors, which contribute 22%–35% and 11%–15% of regional GDP losses, respectively, cannot be ignored, and should receive more attention in climate mitigation policies.

Background: Cryptorchidism is the most common congenital anomaly in pediatric urology. Although early surgery on cryptorchid boys is recommended by pediatric urologists worldwide, the actual age at orchidopexy is often older than the recommended age. Our medical center has started performing ambulatory orchidopexy since March 2016 at the ambulatory surgery center. We aimed to investigate whether ambulatory orchidopexy can improve the timely repair rate.Methods: A retrospective analysis was conducted from 2012 to 2019 at our medical center. Ambulatory orchidopexy was started at our medical center on March 24, 2016. Boys born on or after September 24, 2015 were classified into the “with ambulatory medical resource” group, and boys born before September 24, 2014, were classified into the “without ambulatory medical resource” group. The timely repair rates were calculated and compared.Results: A total of 4,972 cryptorchidism cases were included in the final study. Approximately 33.0% of cryptorchid boys received timely surgery (orchidopexy by the age of 18 months), and only 6.8% of all cryptorchid boys underwent surgery before the age of 1 year. After the performance of ambulatory orchidopexy, the timely repair rate increased from 25.7 to 37.0% (P < 0.001), and the percentage of patients receiving surgery before the age of 1 year increased significantly from 3.5 to 8.6% (P < 0.001). The proportion of timely repair in patients with ambulatory medical resources was significantly higher than that in patients without ambulatory medical resources (15.6% vs. 58.2%, P < 0.001). Significant changes in the rate of surgery before 12 months of age were also found between the two groups (2.4% vs. 14.8%, P < 0.001).Conclusions: After the performance of ambulatory orchidopexy in our medical center, the rates of both timely repair and receiving surgery before the age of 1 year increased significantly. Ambulatory orchidopexy is a potential solution to improve the rate of timely repair in cryptorchid boys, and it is worthy of promotion in developing countries and regions.

Abstract Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE.

Abstract Solomon Hsiang's study, which was recently published in Science, has caused extensive discussion by indicating that future climate change may exert influences on the agricultural yield, labor supply, and energy demand, among others of the United States. Based on the above study, we use an optimized input‐output model to evaluate the economic ripple effect (ERE) of the United States on the world due to climate change under Representative Concentration Pathway 4.5 with different increases in the annual mean temperature (AMT; 1, 1.5, and 2°C) between 2020 and 2100. The results show that if a loss of gross domestic product of 0.88% occurs with a 1°C AMT increase in the United States, the ERE of approximately 0.12% will be generated onto the global gross domestic product; with a 2°C increase, the ERE will triple. The time variation trend of the ERE conforms to the future variations in AMT. The degree of this effect on other regions of the world is closely related to the trade links between the United States and economic aggregates. Among these regions, Canada shows the greatest impact, followed by China. The ERE that China suffers may increase by 4.5 times as the AMT in the United States increases from 1 to 2°C. In cold regions, the benefit from global warming will decrease due to the ERE from other regions, which will inhibit their economic development. This paper aims to provide new perspectives and data‐based support for regions around the world to cope with climate change and to develop policies by studying the ERE behind international trade.

An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.