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Abstract Background The commercial utilization of genetically modified soybeans has yielded substantial economic advantages. Nevertheless, the genetic drift towards wild soybeans is one of the main ecological risks that needs to be addressed. Previous experiments demonstrated the absence of fitness cost or florescence overlap in hybrid offspring resulting from the crossbreeding of transgenic soybean GTS40-3-2 and Zhengzhou wild soybeans. In this study, hybrid progeny was systematically crossed with wild soybeans to establish a backcross progeny system. This system was employed to evaluate the ecological risk associated with the backcross progeny of transgenic and wild soybeans. Results The findings indicated that the offspring from the backcross exhibited glyphosate tolerance. Furthermore, the expression of foreign proteins in the backcross offspring was notably lower than in the transgenic soybean, and there was no significant difference when compared to the hybrid progeny. Parameters such as germination rate, aboveground biomass, pods per plant, full seeds per plant, and 100-grain weight exhibited no significant differences between the negative and positive lines of the backcross progenies, and no fitness cost was identified in comparison to wild soybeans. These results underscore the potential for foreign genes to propagate within other wild soybeans, which requires continuous attention. Conclusions The widespread adoption of genetically modified soybeans has undeniably led to substantial economic gains. However, the research findings emphasize the critical importance of addressing the ecological risks posed by genetic drift towards wild soybeans. The backcross progeny system established in this study indicates that the potential for foreign gene dissemination to wild soybean populations warrants continued attention and mitigation strategies.

Ambient air pollution has been reported to be a risk factor for hypertensive disorders of pregnancy (HDP). Past studies have reported supportive evidence, but evidence from China is scarce and does not integrate the different periods of the pregnancy course. In this study, 1945 pregnant women with HDP and healthy pregnancies between 2016 and 2022 from the Renmin Hospital of Wuhan University registry network database were analysed. The geographic information, biological information and demographic information of the case were fused in the analysis. Machine learning methods were used to obtain the weight of the variable. Then, we used the generalized linear mixed model to evaluate the relationship between increased exposure to each pollutant at different periods of HDP and examined it in different groups. The results showed that SO2 had the predominate impact (12.65 %) on HDP compared with other air pollutants. SO2 exposure was associated with an increased risk of HDP. Increased unit SO2 concentrations were accompanied by an increased risk of HDP (OR = 1.33, 95 % CI: 1.13, 1.566), and the susceptible window for this effect was mainly in the first trimester (OR = 1.242, 95 % CI: 1.092, 1.412). In addition, SO2 exposure was associated with an increased risk of HDP in urban maternity (OR = 1.356, 95 % CI: 1.112, 1.653), obese maternity (OR = 3.58, 95 % CI: 1.608, 7.971), no higher education maternity (OR = 1.348, 95 % CI: 1.065, 1.706), nonzero delivery maternity (OR = 1.981, 95 % CI: 1.439, 2.725), maternal with first time maternity (OR = 1.247, 95 % CI: 1.007, 1.544) and other groups. In summary, SO2 exposure in early pregnancy is one of the risk factors for HDP, and the increased risk of HDP due to increased SO2 exposure may be more pronounced in obese, urban, low-education, and nonzero delivery populations.

Wild soybeans retain many substances significantly reduced or lost in cultivars during domestication. This study utilized LC-MS to analyze metabolites in the seed coats and embryos of wild and cultivated soybeans. 866 and 815 metabolites were identified in the seed extracts of both soybean types, with 35 and 10 significantly differing metabolites in the seed coat and embryos, respectively. The upregulated metabolites in wild soybeans are linked to plant defense, stress responses, and nitrogen cycling. MALDI-MSI results further elucidated the distribution of these differential metabolites in the cotyledons, hypocotyls, and radicles. In addition to their role in physiological processes like growth and response to environmental stimuli, the prevalent terpenoids, lipids, and flavonoids present in wild soybeans exhibit beneficial bioactivities, including anti-inflammatory, antibacterial, anticancer, and cardiovascular disease prevention properties. These findings underscore the potential of wild soybeans as a valuable resource for enhancing the nutritional and ecological adaptability of cultivated soybeans.

Abstract Background Crop-wild hybridization has generated great concerns since gene flow can be an avenue for transgene escape. However, a rather limited number of studies on risk assessment regarding the dispersion of transgenes from GM soybean to populations of its wild relatives have been previously conducted. Results The results of the 3-year experiment demonstrated that hybrids between GM soybeans and wild soybean had lower seed germination and higher seed productivity than GM soybean. Both of these features of hybrid (especially F2 and F3) were similar to those of wild soybean. Furthermore, the foreign protein was stably expressed in hybrid EPSPS positive plants; however, no difference was observed in agronomic measurements between hybrids that are glyphosate sensitive or resistant, homozygous or heterozygous for the transgene, indicating that the presence of the EPSPS transgene does not affect the vigor of hybrid. In contrast, hybridization between GM soybean and wild soybean may have more impact on hybrid growth and fecundity, this increase in biomass and yield confers a potential competition benefit to hybrids. Conclusions Gene flow from GM soybean to wild soybean has the potential to promote the adaptability of hybrids and may increase the possibility of dispersal of transgenes in wild soybean relatives.

ABSTRACTElectric Taxis (ETs) are the most favored alternatives to Gasoline Taxis (GTs) in cities that aim to reduce environmental pollution. How to develop a reasonable scale on which GTs are substituted by ETs remains a challenge to governments due to the dynamics and complexity of the taxi system. To address this challenge, this paper develops a discrete-event-based simulation framework to simulate participants in the system and estimate the results under different substitution scales, which are helpful to understanding the status changing law of entities under different substitution scales, such as the operating indices of ETs, the unsatisfied travel requirements of passengers, and the usage state of charging facilities. The framework abstracts the behavioral process of ETs into three elements, namely, entity, behavior, and event. The entities are constructed from the information derived from the trajectory data. The behaviors are defined by rules following behavioral logic under anxiety psychology, which is caused by the limited range of ETs. The events are triggered based on rules from reality. With the help of this framework, a multi-objective optimization model is developed to obtain the optimal substitution scale of GTs in the case study area of Zhengzhou City. Overall, the approach could provide a practical tool to address this challenge, which could support further studies of the effect of ETs on urban taxis.

Abstract Predicting urban crowd flow spatial distributions plays a critical role in optimizing urban public safety and traffic congestion management. The spatial dependency between regions and the temporal dynamics of the local crowd flow are two important features in urban crowd flow prediction. However, few studies considered geographic characteristic in terms of spatial features. To fill this gap, we propose an urban crowd flow prediction model integrating geographic characteristics (FPM-geo). First, three geographic characteristics, proximity, functional similarity, and road network connectivity, are fused by a residual multigraph convolution network to model the spatial dependency relationship. Then, a long short-term memory network is applied as a framework to integrate both the temporal dynamic patterns of local crowd flow and the spatial dependency between regions. A 4-day mobile phone dataset validates the effectiveness of the proposed method by comparing it with several widely used approaches. The result shows that the root mean square error decreases by 15.37% compared with those of the typical models with the prediction interval at the 15-min level. The prediction error increases with the crowd flow size in a local area. Moreover, the error reaches the top of the morning peak and the evening peak and slopes down to the bottom at night.

BackgroundEarly triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. ObjectiveThe purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. MethodsIn all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. ResultsAmong several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians’ assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). ConclusionsThe 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

Exogenous genes of transgenic crops are usually transferred to their wild-type relatives through pollen-mediated gene flow, which may change the ecological fitness and ability to invade wild populations, resulting in the weeding of wild plants and other unpredictable environmental impacts. In this study, the F1 generation of herbicide-resistant soybeans and wild soybeans was obtained by artificial pollination, F2 generation seeds were obtained by self-crossing, and the fitness of the parents and their F1 and F2 generations were tested. The foreign protein EPSPS was expressed normally in the hybrid between transgenic and wild soybeans; however, the protein expression was significantly lower than that in transgenic soybeans. The fitness of the F1 hybrid between transgenic and wild soybeans was significantly lower than that of its parent. Compared with those of the wild soybeans, the F2 generation soybeans improved in some fitness indices, while the emergence rate, pollen germination rate, and number of full seeds per pod, pods per plant, and full seeds per plant did not significantly differ. The aboveground biomass and 100-seed weight of the F2 generation were higher than those of wild soybeans. Fitness among the F2-negative plants, homozygous, and heterozygous positive plants did not significantly vary. Improved fitness and presence of foreign genes in the F2 soybean were not significantly correlated. As the F2 generation of transgenic and wild soybeans had no fitness cost and the flowering stage were overlapped, the foreign gene might still spread in the wild soybean population.

A 49-d feeding study was conducted to evaluate the effects of the genetically modified (GM) maize strain C0030.3.5 on Japanese quails (Coturnix japonica) in terms of body performance and egg quality. Furthermore, the bodily fats of transgenic proteins in the Japanese quails were investigated. The results showed that the parameters body weight, hematology, serum chemistry, relative organ weight, and histopathological appearance were normal in male and female quails that consumed GM diets, and no differences could be attributed to the varying diets in regard to the laying performances or nutrient egg compositions between the groups. Furthermore, the transgenic Cry1Ab and EPSPS proteins were undetectable by Western blot in the blood, organ, fecal, and whole egg samples of quails fed a diet containing GM maize. The results obtained after 49 d suggested that consumption of C0030.3.5 transgenic feed did not adversely affect quail health or egg quality, and there was no evidence of transgenic protein translocation to the blood, tissues, feces, and eggs. Based on the different parameters assessed, C0030.3.5 transgenic maize is a safe food source for quails that does not differ in quality from non-GM maize.

Massive green tides caused by Ulva prolifera have annually occurred in the Yellow Sea since 2007, which has attracted much attention from the government and society. There are associations between the green tides in the Yellow Sea and social response in the social media (i.e., Sina Weibo), which are bidirectional and could be captured by the bidirectional neural network. For instance, how to detect daily U. prolifera green tides by fusing remote sensing data with social media data, and how to use the observed U. prolifera green tides to infer the social response are two challenges of State Oceanic Administration, China. This article first illustrated that there are bidirectional associations between green tides and Sina Weibo data. Then, this article introduced a bidirectional spatio-temporal associative memory neural network (BSAMNN) model for modeling this bidirectional association from the spatio-temporal perspective. BSAMNN first extracted six characteristics from green tides and nine characteristics from the social responses in 2016–2019. Second, these characteristics were split by year, and the characteristics in 2016–2018 were, respectively, put into the bidirectional associative memory neural network (BAM), which is a two-layer artificial neural network. Based on the BAM results and the observed data, the residual network was constructed. Third, BSAMNN extracted the spatio-temporal rules from the characteristics in 2016–2018 as the constraints through the mining rule algorithm and modify the results via sea surface wind and ocean surface current. Last, BSAMNN put the characteristics in 2019 into BAM and used the residual network to modify the results, which was constrained by the spatio-temporal rules. The feasibility and reliability of our approach were demonstrated by using the U. prolifera green tides in 2019. The average accuracy, false alarm rate, and missing alarm rate of BSAMNN results were 0.69, 0.25, and 0.31, respectively, which was 0.11 higher, 0.10 lower, and 0.11 lower than that of the traditional BAM. The results indicated that our method is an effective alternative of linking the U. prolifera green tides and its public sentiments on social media.

A salient scene is an area within an image that contains visual elements that stand out from surrounding areas. They are important for distinguishing landmarks in first-person-view (FPV) applications and determining spatial relations in images. The relative spatial relation between salient scenes acts as a visual guide that is easily accepted and understood by users in FPV applications. However, current digitally navigable maps and location-based services fall short of providing information on visual spatial relations for users. This shortcoming has a critical influence on the popularity and innovation of FPV applications. This paper addresses the issue by proposing a method for detecting visually salient scene areas (SSAs) and deriving their relative spatial relationships from continuous panoramas. This method includes three critical steps. First, an SSA detection approach is introduced by fusing region-based saliency derived from super-pixel segmentation and the frequency-tuned saliency model. The method focuses on a segmented landmark area in a panorama. Secondly, a street-view-oriented SSA generation method is introduced by matching and merging the visual SSAs from continuous panoramas. Thirdly, a continuous geotagged panorama-based referencing approach is introduced to derive the relative spatial relationships of SSAs from continuous panoramas. This information includes the relative azimuth, elevation angle, and the relative distance. Experiment results show that the error for the SSA relative azimuth angle is approximately ± 6° (with an average error of 2.67°), and the SSA relative elevation angle is approximately ± 4° (with an average error of 1.32°) when using Baidu street-view panoramas. These results demonstrate the feasibility of the proposed approach. The method proposed in this study can facilitate the development of FPV applications such as augmented reality (AR) and pedestrian navigation using proper spatial relation.

Visual localization is an accurate and low-cost indoor localization solution. A bottleneck for visual localization is the computation efficiency of continuous image searching and matching. In this paper, an indoor visual localization method is proposed to realize continuous and accurate indoor localization based on image matching. This method uses smartphones to collect multi-sensor data, including video frames and inertial readings. To improve the computation efficiency of the proposed visual localization method, a spatial model is developed to optimize the spatial organization of geo-tagged images in a dataset. Several spatial constraint-based image searching strategies are also designed to further reduce the computation time. Based on the spatial model and spatial constraint-based strategies, a visual localization algorithm is proposed. The experimental results show that the localization errors of the image querying, continuous offline localization and online localization of this method are approximately 0.4 m, 0.7 m and 0.9 m, respectively. This method can achieve an accuracy of 1.3 m, even under a random camera opening condition. The average computation time (i.e.. the average time needed to provide a location estimation result) is approximately 0.59 s. The results indicate that the proposed method can realize efficient and continuous indoor localization with high localization accuracy.

“Smartphone Zombie” (or Smombie) is a new term that describes pedestrians playing with their mobile phones while walking. Research has shown that the usage of mobile phone distracts pedestrians, slows walking and increases the risk of car accidents. Relevant measures and laws have been taken to decrease the phenomenon; however, these measures and laws are costly, and the effects are limited. Therefore, the motivation of this study is to develop an efficient approach to identify and remind Smombies at intersections to reduce accident occurrences. In this approach, a framework for Smombie context awareness is proposed that integrates behavior information from pedestrians within physical and virtual space. In the framework, a set of sensors is employed to recognize the gesture of playing mobile phones. A modified multi-information fusion algorithm is developed to make Smombies aware of their physical context by combining fuzzy mathematics and Dempster-Shafer (D-S) evidence theory. Experiments indicate that the recall and precision of the algorithm are above 0.9, which means the proposed methods can effectively identify Smombies at crossroads.

Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with crowd dynamics (TTDP-CD), which is quantified with the crowd dynamics indicators derived from mobile tracking data in terms of crowd flow, crowd interaction, and crowd structure. TTDP-CD attempts to minimize the perceived crowding and maximize the assessed value of destinations while minimizing the total distance and proposes a two-stage route strategy of “global optimization first, local update later” to deal with the sudden increase in crowding in realistic scenarios. An evolutionary algorithm is extended with container-index coding, mixed mutation operators, and a global archive to create a personalized day tour route at the urban scale. To corroborate the performance of this approach, a case study was carried out in Dalian, China. The results demonstrate that the suggested method outperforms previous approaches, such as NSGA-II, MOPSO, MOACO, and WSM, in terms of performance and solution quality and decreases real-time crowding by an average of 7%.

Three-dimensional (3D) printing concept has been successfully employed in regenerative medicine to achieve individualized therapy due to its benefit of a rapid, accurate, and predictable production process. Traditional biocomposites scaffolds (SCF) are primarily utilised for bone tissue engineering; nevertheless, over the last few years, there has already been a dramatic shift in the applications of bioceramic (BCR) SCF. As a direct consequence, this study focused on the structural, degeneration, permeation, and physiological activity of 3D-printed BCR (3DP-B) SCF with various conformations and work systems (macros, micros, and nanos ranges), as well as their impacts on the mechanical, degeneration, porosity, and physiological activities. In addition, 3DP-B SCF are highlighted in this study for potential uses applied from bone tissue engineering (BTE) to bone tumor treatment. The study focused on significant advances in practical 3DP-B SCF that can be utilized for tumor treatment as well as bone tissue regeneration (BTR). Given the difficulties in treating bone tumors, these operational BCR SCF offer a lot of promise in mending bone defects caused by surgery and killing any remaining tumor cells to accomplish bone tumor treatment. Furthermore, a quick assessment of future developments in this subject was presented. The study not only summarizes recent advances in BCR engineering, but it also proposes a new therapeutic strategy focused on the extension of conventional ceramics’ multifunction to a particular diagnosis.

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.

The modern outdoor advertising industry is introducing new technologies to attract attention and enhance effective reach. However, displaying the pertinent advertisements (ads) and maximizing their coverage are still a challenge, as little is known about the interests of passersby. In this paper, we attempted to fill this gap by 1) identifying the individuals' interests extracted from the mobile phone internet usage data, and 2) analyzing the individuals' mobility patterns acquired from the mobile phone location data. Based on these data, the problem of distributing the outdoor ads to the maximum reach of the potentially interested users was further formulated as a maximal covering location problem (MCLP). Specifically, a continuous space maximal coverage model, the MCLP-complementary coverage (MCLP-CC), was utilized to search for the optimal locations for a given category of advertisements. Finally, the proposed methodology was applied to Wuxue, a city in central China, as a case study. The calculation results show that when setting the same number of places for outdoor advertising, our approach achieves an average of 69% improvement compared with the current dominant ads placement approach (selecting the most crowded location) in reaching the potential target audiences. An average of 15% improvement compared with the classic MCLP model. Overall, this paper demonstrates the value of the mobile phone data for interest-driven outdoor advertising and its potential applications in other mobile services.

Obtaining trajectory-related data through crowdsourcing is a useful approach for acquiring and updating information on road networks because these data are easily accessible and up to date. However, it is challenging to ensure the accuracy of shape- and connectivity-related information on road networks when this is extracted from massive amounts of data. To address this challenge, this paper proposes an inertia mutation energy model (IMEM) to extract and mine information on road networks based on crowdsourced trajectories by using features of images of vehicular trajectories to mine the connectivity between roads. The central assumption of the model is that a road segment may have inertial energy to extend to another road segment that decreases in case of the change in direction. Three critical steps are involved in extracting information concerning roads. First, images of the trajectory and corresponding feature images are constructed, and high-quality trajectories are filtered to split the road into several segments. Second, the IMEM is proposed to measure the potential extension of each road segment with the aim of mining connectivity-related information between the given trajectories. Finally, the centerline of the road is obtained using mathematical morphology and a thinning algorithm. The proposed algorithm was tested by using Global Position System trajectories of the Didi Taxi in Wuhan, China, and the results show that it reduced time cost by over 99% compared with vector algorithms proposed in the literature. Moreover, it enhanced the precision of the results of extraction by 10%-20% compared with traditional kernel density evaluation algorithms.

Ulva prolifera (U. prolifera), a seaweed species in the family Ulvaceae, has been causing green tides in the Yellow Sea, China, every year since 2008. This has attracted the attention of the government and the scientific community because of its influence on the region's marine economy and coastal zone environment. Remote sensing, surveillance ships, and airplanes are three common surveillance approaches used by the State Oceanic Administration (SOA) for U. prolifera detection in the Yellow Sea. However, improving the accuracy and convenience of daily U. prolifera detection by fusing the data from these sources, and obtaining the coverage area and distribution of U. prolifera without human intervention, are two challenges that need to be addressed. To this end, this study proposes an improved multi-source evidence fusion method based on Dempster-Shafer evidence theory to detect the distribution and coverage area of U. prolifera using data from multiple sources (MODIS images, Sentinel-2A images, and surveillance ship data). This improved approach can handle the conflicts caused by differences in image characteristics and spatial resolutions as well as differences between surveillance ship data and remote sensing data. The capability of the improved method to extract the distribution and coverage area of U. prolifera was tested experimentally. The results agree with the reference data supplied by SOA in terms of spatial position and geometry. The fusion of multi-source data demonstrates better results in the detection of U. prolifera coverage area and spatial distribution compared with those from individual data sources. The improved approach can be applied to daily and emergent monitoring tasks for U. prolifera detection.

The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error. However, it is vulnerable to mismatch problems, which are usually caused by the randomness of human activities. This paper proposes a structure landmark map matching-based indoor localization approach. Structure landmarks refer to special spatial structures (e.g., intersections, corridors, or corners), which are visually salient in a local environment. These landmarks are visually recognizable in indoor spaces because of their distinct shapes. This paper integrates visual and inertial information to recognize the structure landmarks by using a Bayesian classifier. An algorithm is also proposed to realize indoor localization without prior knowledge of the initial location or the turning angles of people. This approach decreases the accumulative localization error of PDR by matching the detected structure landmarks to the ground-truth values. The experimental results showed that the identification accuracy of the structure landmark was about 90% and the matching accuracy was 92%. The mean off-line localization error was about 1.2 m. Compared with the AMM-based method, this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed.

The environmental release and biosafety of transgenic Bt crops have attracted global attention. China has a large area of saline-alkali land, which is ideal for large-scale production of Bt transgenic rice. Therefore an understanding of the fitness of Bt transgenic rice in saline-alkaline soils and the ability to predict its long-term environmental effects are important for the future sustainable use of these crops. In the present study, we aimed to evaluate the fitness of cry1Ab/c transgenic rice in both farmland and natural ecosystems. Transgenic cry1Ab/c rice Huahui1, for which a national biosafety certificate was obtained, was grown on normal farmland and saline-alkaline soils in a glass greenhouse. The expression pattern of exogenous Cry1Ab/c protein, and vegetative and reproductive fitness of rice were assessed. The expression of the exogenous Cry1Ab/c protein in the transgenic rice grown on saline-alkaline soil was lower than that in the strain grown on farmland soil. Under both the soil conditions, vegetative growth abilities, as evaluated by tiller number and biomass, and reproductive growth abilities, as measured by filled grain number and filled grain weight per plant, showed a significantly higher fitness cost for Huahui1 than that for the parental rice Minghui63 grown under the same soil conditions. In saline-alkaline soil, the fitness cost of Huahui1was moderately higher than that of Minghui63. Therefore, the ecological risk of cry1Ab/c transgenic rice is not expected to be higher than that of parental rice Minghui63 if the former escapes into natural saline-alkaline soil. The results of the present study provide a scientific basis to improve environmental safety assessment of the insect-resistant transgenic rice strain Huahui1 before commercialization.

Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.

Vehicle routing optimization (VRO) designs the best routes to reduce travel cost, energy consumption, and carbon emission. Due to non-deterministic polynomial-time hard (NP-hard) complexity, many VROs involved in real-world applications require too much computing effort. Shortening computing time for VRO is a great challenge for state-of-the-art spatial optimization algorithms. From a spatial-temporal perspective, this paper presents a spatial-temporal Voronoi diagram-based heuristic approach for large-scale vehicle routing problems with time windows (VRPTW). Considering time constraints, a spatial-temporal Voronoi distance is derived from the spatial-temporal Voronoi diagram to find near neighbors in the space-time searching context. A Voronoi distance decay strategy that integrates a time warp operation is proposed to accelerate local search procedures. A spatial-temporal feature-guided search is developed to improve unpromising micro route structures. Experiments on VRPTW benchmarks and real-world instances are conducted to verify performance. The results demonstrate that the proposed approach is competitive with state-of-the-art heuristics and achieves high-quality solutions for large-scale instances of VRPTWs in a short time. This novel approach will contribute to spatial decision support community by developing an effective vehicle routing optimization method for large transportation applications in both public and private sectors.

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

Automatic Identification System (AIS) data could support ship movement analysis, and maritime network construction and dynamic analysis. This study examines the global maritime network dynamics from multi-layers (bulk, container, and tanker) and multidimensional (e.g., point, link, and network) structure perspectives. A spatial-temporal framework is introduced to construct and analyze the global maritime transportation network dynamics by means of big trajectory data. Transport capacity and stability are exploited to infer spatial-temporal dynamics of system nodes and links. Maritime network structure changes and traffic flow dynamics grouping are then possible to extract. This enables the global maritime network between 2013 and 2016 to be investigated, and the differences between the countries along the 21st-century Maritime Silk Road and other countries, as well as the differences between before and after included by 21st-century Maritime Silk Road to be revealed. Study results indicate that certain countries, such as China, Singapore, Republic of Korea, Australia, and United Arab Emirates, build new corresponding shipping relationships with some ports of countries along the Silk Road and these new linkages carry significant traffic flow. The shipping dynamics exhibit interesting geographical and spatial variations. This study is meaningful to policy formulation, such as cooperation and reorientation among international ports, evaluating the adaptability of a changing traffic flow and navigation environment, and integration of the maritime economy and transportation systems.

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

The world’s largest macroalgal blooms, Ulva prolifera, have appeared in the Yellow Sea every summer on different scales since 2007, causing great harm to the regional marine economy. In this study, the Normalized Difference of Vegetation Index (NDVI) index was used to extract the green tide of Ulva prolifera from MODIS images in the Yellow Sea in 2016⁻2018, to investigate its spatiotemporal patterns and to calculate its occurrence probability. Using the standard deviational ellipse (SDE), the morphological characteristics of the green tide, including directionality and regularity, were analyzed. The results showed that the largest distribution and coverage areas occurred in 2016, with 57,384 km2 and 2906 km2, respectively and that the total affected region during three years was 163,162 km2. The green tide drifted northward and died out near Qingdao, Shandong Province, which was found to be a high-risk region. The coast of Jiangsu Province was believed to be the source of Ulva prolifera, but it was probably not the only one. The regularity of the boundary shape of the distribution showed a change that was opposite to the variation of scale. Several sharp increases were found in the parameters of the SDE in all three years. In conclusion, the overall situation of Ulva prolifera was still severe in recent years, and the sea area near Qingdao became the worst hit area of the green tide event. It was also shown that the sea surface wind played an important part in its migration and morphological changes.

Visual landmarks are important navigational aids for research into and design of applications for last mile pedestrian navigation, e.g., business card route of pedestrian navigation. The business card route is a route between a fixed origin (e.g., campus entrance) to a fixed destination (e.g., office). The changing characteristics and combinations of various sensors’ data in smartphones or navigation devices can be viewed as invisible salient landmarks for business card route of pedestrian navigation. However, the advantages of these invisible landmarks have not been fully utilized, despite the prevalence of GPS and digital maps. This paper presents an improvement to the Dempster–Shafer theory of evidence to find invisible landmarks along predesigned pedestrian routes, which can guide pedestrians by locating them without using digital maps. This approach is suitable for use as a “business card” route for newcomers to find their last mile destinations smoothly by following precollected sensor data along a target route. Experiments in real pedestrian navigation environments show that our proposed approach can sense the location of pedestrians automatically, both indoors and outdoors, and has smaller positioning errors than purely GPS and Wi-Fi positioning approaches in the study area. Consequently, the proposed methodology is appropriate to guide pedestrians to unfamiliar destinations, such as a room in a building or an exit from a park, with little dependency on geographical information.

Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber in the U.S., or Didi and Kuaidi in China. In 2014, Didi and Kuaidi triggered a nationwide subsidy war, with possible effects on the spatial inequality of taxi services. Taxi trajectory data from Shenzhen collected during the subsidy war shows that this competition reduced spatial inequality in the inner city but aggravated it in the outer city. In this study, a measure of service rate to depict the quantity of taxi services is proposed to calculate a Gini coefficient for evaluating change in the spatial inequality of taxi services. The Theil index and its decomposition were used to distinguish the contribution of Traffic Analysis Zones (TAZs) in the inner and the outer city and compare them to the overall spatial inequality of taxi services in Shenzhen, TAZs in the outer city had greater inequality in taxi services than the inner city. Furthermore, the primary contributor to overall inequality in taxi services was inequality within, rather than between, the inner and outer city. Moreover, the mean values for the changed service rates in the inner city were always larger than the outer city, and the inner city had a more equitable changed service rate than the outer city. These results could serve as a foundation for improving taxi services citywide.

Previous research on the geographies of food put a considerable focus on analyzing how different types of food or ingredients are consumed across different places. Little is known, however, about how food culture is manifested through various cooking traditions as well as people’s perceptions over different culinary styles. Using a data set captured from one of the largest online review sites in China (www.dianping.com), this study demonstrates how geo-referenced social review data can be leveraged to better understand the geographic prevalence and mix of regional cuisines in Chinese cities. Based on information of millions of restaurants obtained in selected cities (i.e., provincial capitals and municipalities under direct supervision of the Chinese central government), we first measure by each city the diversity of restaurants that serve regional Chinese cuisines using the Shannon entropy, and analyze how cities with different characteristics are geographically distributed. A hierarchical clustering algorithm is then used to further explore the similarities of consumers’ dining options among these cities. By associating each regional Chinese cuisine to its origin, we then develop a weighted distance measure to quantify the geographic prevalence of each cuisine type. Finally, a popularity index (POPU) is introduced to quantify consumers’ preferences for different regional cuisines. We find that: (1) diversity of restaurants among the cities shows an “east–west” contrast that is in general agreement with the socioeconomic divide in China; (2) most of the cities have their own unique characteristics, which are mainly driven by a large market share of the corresponding local cuisine; (3) there exists great heterogeneity of the geographic prevalence of different Chinese cuisines. In particular, Chuan and Xiang, which are famous for their spicy taste, are widely distributed across the mainland China and (4) among the top-tier restaurants ranked by the consumers in a city, the local cuisine is not usually favored, while other cuisines are favored by consumers in many different cities. This study demonstrates the use of social review data as a cost-effective approach of studying urban gastronomy and its relationship with human activities.

The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development.

How should the efficiency of searching for real objects in real scenes be measured? Traditionally, when searching for artificial targets, e.g., letters or rectangles, among distractors, efficiency is measured by a reaction time (RT) × Set Size function. However, it is not clear whether the set size of real scenes is as effective a parameter for measuring search efficiency as the set size of artificial scenes. The present study investigated search efficiency in real scenes based on a combination of low-level features, e.g., visible size and target-flanker separation factors, and high-level features, e.g., category effect and target template. Visible size refers to the pixel number of visible parts of an object in a scene, whereas separation is defined as the sum of the flank distances from a target to the nearest distractors. During the experiment, observers searched for targets in various urban scenes, using pictures as the target templates. The results indicated that the effect of the set size in real scenes decreased according to the variances of other factors, e.g., visible size and separation. Increasing visible size and separation factors increased search efficiency. Based on these results, an RT × Visible Size × Separation function was proposed. These results suggest that the proposed function is a practicable predictor of search efficiency in real scenes.

Mobile phone location data is a newly emerging data source of great potential to support human mobility research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city's mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0

Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g., Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments.

The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual’s records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2- to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.

Investigating human mobility patterns can help researchers and agencies understand the driving forces of human movement, with potential benefits for urban planning and traffic management. Recent advances in location-aware technologies have provided many new data sources (e.g., mobile phone and social media data) for studying human space-time behavioral regularity. Although existing studies have utilized these new datasets to characterize human mobility patterns from various aspects, such as predicting human mobility and monitoring urban dynamics, few studies have focused on human convergence and divergence patterns within a city. This study aims to explore human spatial convergence and divergence and their evolutions over time using large-scale mobile phone location data. Using a dataset from Shenzhen, China, we developed a method to identify spatiotemporal patterns of human convergence and divergence. Eight distinct patterns were extracted, and the spatial distributions of these patterns are discussed in the context of urban functional regions. Thus, this study investigates urban human convergence and divergence patterns and their relationships with the urban functional environment, which is helpful for urban policy development, urban planning and traffic management.