Towards High-Order Complementary Recommendation via Logical Reasoning Network

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.Comment: 6 pages, 3 figure

How Are Green Spaces Distributed among Different Social Groups in Urban China? A National Level Study

The study analyzes the distributional equity of urban green space (UGS) among different social groups across all urban areas in China. Urban green space is measured in two ways: Park area per capita and vegetation coverage ratio within 1.6 km and 3.2 km featuring different ecosystem services they provide. Multiple regression analyses are conducted to assess relationships between different groups (children, the elderly, and migrant populations) and distributed UGS. Largely consistent to other national level studies, the nationwide analytical results indicate emerging social inequality of UGS during the urbanization of China, with a few nuances. A bi-fold pattern is observed in our case: Whilst areas with higher portions of children and senior people have less parks and high vegetation coverage, a marginalized group—internal migrant people, have more parks and low vegetation coverage in their vicinities. The results of regression analyses in different regions further shed light on revealing disparities of UGS in areas with varying socioeconomic development levels, geographical features, and urbanization paces. The implication of the study informs the decision makers to incorporate spatial patterns of social groups into green space guidance and evaluation for the purpose of promoting more equal development of UGS

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale

Water Deficit Caused by Land Use Changes and Its Implications on the Ecological Protection of the Endorheic Dalinor Lake Watershed in Inner Mongolia, China

Dalinor Lake, the second-largest endorheic salt lake in Inner Mongolia, has shown a shrinking trend given the lack of a significant decrease in precipitation (PRE). Based on high-spatial-resolution datasets, we employed a linear regression model, Theil–Sen median trend analysis, the Mann–Kendall test, and a land use transfer matrix to identify the spatio-temporal distribution and trends of PRE and actual evapotranspiration (AET) at the watershed scale during 2001–2019; then, the water deficit (WD) caused by land use changes in different surface lithology zones was analyzed. The results showed that the annual PRE and WD of the Dalinor Lake watershed showed insignificant upward trends, while the annual AET showed a significant upward trend. Spatially, about 89% of the watershed showed a significant upward trend for AET, while 12% showed a weak significant upward trend for PRE. The WDs of the aeolian sand zone and the sand, gravel, and silt accumulation zone were most heavily affected by the new increased land use from 2001 to 2019, accounting for 43.14% and 25.56% of the total WD of the watershed, respectively. Specifically, the WD of the aeolian sand zone caused by the new increased grassland and farmland in 2019 accounted for 41.92% and 18.52% of the total WD of the zone, respectively. The WD of the sand, gravel, and silt accumulation zone caused by the new increased grassland and farmland in 2019 accounted for 37.07% and 35.59% of the total WD of the zone, respectively. The WD caused by the new increased land use was increased by 7.78 million m3 in 2019 compared with the corresponding land use type in 2001, which would decrease the water yield. It is necessary to strengthen the protection of regional forest ecosystems in the granite and terrigenous clastic rock zone; standardize pasture management and reduce farmland reclamation in the sand, gravel, and silt accumulation zone, the aeolian sand zone, and the basalt platform zone; and reduce unnecessary impervious land construction in the aeolian sand zone

Constitutive Expresser of Pathogenesis Related Genes 1 Is Required for Pavement Cell Morphogenesis in Arabidopsis.

For over 50 years, researchers have focused on the mechanisms underlying the important roles of the cytoskeleton in controlling the cell growth direction and cell expansion. In our study, we performed ethyl methane sulfonate mutagenesis on Col-0 background and identified two new CONSTITUTIVE EXPRESSER OF PATHOGENESIS RELATED GENES 1 (CPR1) alleles with pavement cell (PC) morphogenetic defects. Morphological characterizations showed that polar growth initiation and expansion of PCs are seriously suppressed in cpr1. Closer cytoskeleton investigation showed that the directional arrangement of microtubules (MTs) during PC development is defective and the cortical fine actin filaments cannot be aggregated effectively to form actin cable networks in cpr1 mutants. These results suggest that the abnormal PC morphogenesis in cpr1 is accompanying with the aberrant arrangement of cytoskeleton. Site-directed mutagenesis and knockout within the F-box-associated (FBA) domain, which is reported to be a motif for recognizing particular substrates of CPR1, proved that the FBA domain is indispensable for normal CPR1 regulation of the PC morphogenesis. Further genetic analysis indicated that the defects on PC morphogenesis of cpr1 depend on two lipase-like proteins, ENHANCED DISEASE SUSCEPTIBILITY 1 and PHYTOALEXIN DEFICIENT 4. Our results provide further insights into the relationship between the cytoskeleton and PC morphogenesis, and suggest that the cytoskeleton-mediated PC morphogenesis control might be tightly linked to plant defense responses

Preparation of Aluminosilicate Ferrierite Zeolite Nanosheets with Controllable Thickness in the Presence of a Sole Organic Structure Directing Agent

Preparation of aluminosilicate ferrierite (FER) zeolite nanosheets with controllable thickness in the presence of a sole organic ammonium is attractive, but still challenging. In this report, with the employment of N,N-diethyl-cis-2,6-dimethylpiperidinium (DMP) as both a structure directing agent and crystal growth inhibitor, aluminosilicate FER zeolite nanosheets, with a variety of crystal thicknesses, ranging from 6 to 200 nm, are successfully synthesized under hydrothermal conditions. Very interestingly, the amount of DMP in the starting gel is the key factor for crystal thickness control of aluminosilicate FER zeolite nanosheets. The obtained FER products, with different thicknesses, are well characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), N2 sorption, thermogravimetric analysis (TG), inductively coupled plasma (ICP), and magic angle spinning nuclear magnetic resonance (MAS NMR) techniques. This simple strategy might provide a novel avenue for the synthesis of other zeolite nanosheets with controllable thickness

Predicting the effect of street environment on residents' mood states in large urban areas using machine learning and street view images

Background: Researchers have demonstrated that the built environment is associated with mental health outcomes. However, evidence concerning the effects of street environments on mood in fast-growing Asian cities is scarce. Traditional questionnaires and interview methods are labor intensive and time consuming and pose challenges for accurately and efficiently evaluating the impact of urban-scale street environments on mood. Objective: This study aims to use street view images and machine learning methods to model the impact of street environments on mood states in a large urban area in Guangzhou, China, and to assess the effect of different street view elements on mood. Methods: A total of 199,754 street view images of Guangzhou were captured from Tencent Street View, and street elements were extracted by pyramid scene parsing network. Data on six mood state indicators (motivated, happy, positive-social emotion, focused, relaxed, and depressed) were collected from 1590 participants via an online platform called Assessing the Effects of Street Views on Mood. A machine learning approach was proposed to predict the effects of street environment on mood in large urban areas in Guangzhou. A series of statistical analyses including stepwise regression, ridge regression, and lasso regression were conducted to assess the effects of street view elements on mood. Results: Streets in urban fringe areas were more likely to produce motivated, happy, relaxed, and focused feelings in residents than those in city center areas. Conversely, areas in the city center, a high-density built environment, were more likely to produce depressive feelings. Street view elements have different effects on the six mood states. “Road” is a robust indicator positively correlated with the “motivated” indicator and negatively correlated with the “depressed” indicator. “Sky” is negatively associated with “positive-social emotion” and “depressed” but positively associated with “motivated”. “Building” is a negative predictor for the “focused” and “happy” indicator but is positively related to the “depressed” indicator, while “vegetation” and “terrain” are the variables most robustly and positively correlated with all positive moods. Conclusion: Our findings can help urban designers identify crucial areas of the city for optimization, and they have practical implications for urban planners seeking to build urban environments that foster better mental health

Improved Decoupling Control for a Powershift Automatic Mechanical Transmission Employing a Model-Based PID Parameter Autotuning Method

Automatic mechanical transmission (AMT) with a gearshift assistant mechanism is a novel transmission architect concept aiming to improve the torque interruption and driveline jerk of AMT. During the shifting process, the shifting performance deteriorates as the varying road gradient and the friction coefficient worsen the coupling effect between the motor torque and the clutch friction torque. This paper focuses on improving the controller’s robustness of AMT with a gearshift assistant mechanism against the perturbed parameters during the stage of torque gap filling. In this paper, a detailed powertrain simulation model was presented. Based on a decoupling controller and a disturbance compensator, proportional-integral-differential (PID) controllers are applied to enhance the robustness and the decoupling effect. The PID parameters are automatically tuned by employing the Nelder-Mead method. In the tuning process, a cost function was established to demonstrate the outputs’ reference tracking performance, and the PID parameters are tuned by minimizing the cost function. Finally, the tuned parameters are stored in PID maps to make them adjustable online. Simulation results show that with the perturbed parameters well estimated, the upshift process was successful and the torque filling effect was also acceptable. The proposed transmission is a promising structure for industry applications