336 research outputs found
Supplemental_Table_3 – Supplemental material for Axitinib overcomes multiple imatinib resistant cKIT mutations including the gatekeeper mutation T670I in gastrointestinal stromal tumors
No full textSupplemental material, Supplemental_Table_3 for Axitinib overcomes multiple imatinib resistant cKIT mutations including the gatekeeper mutation T670I in gastrointestinal stromal tumors by Feiyang Liu, Fengming Zou, Cheng Chen, Kailin Yu, Xiaochuan Liu, Shuang Qi, Jiaxin Wu, Chen Hu, Zhenquan Hu, Juan Liu, Xuesong Liu, Li Wang, Juan Ge, Wenchao Wang, Tao Ren, Mingfeng Bai, Yujiao Cai, Xudong Xiao, Feng Qian, Jun Tang, Qingsong Liu and Jing Liu in Therapeutic Advances in Medical Oncology</p
Supplementary_material-updated – Supplemental material for Axitinib overcomes multiple imatinib resistant cKIT mutations including the gatekeeper mutation T670I in gastrointestinal stromal tumors
No full textSupplemental material, Supplementary_material-updated for Axitinib overcomes multiple imatinib resistant cKIT mutations including the gatekeeper mutation T670I in gastrointestinal stromal tumors by Feiyang Liu, Fengming Zou, Cheng Chen, Kailin Yu, Xiaochuan Liu, Shuang Qi, Jiaxin Wu, Chen Hu, Zhenquan Hu, Juan Liu, Xuesong Liu, Li Wang, Juan Ge, Wenchao Wang, Tao Ren, Mingfeng Bai, Yujiao Cai, Xudong Xiao, Feng Qian, Jun Tang, Qingsong Liu and Jing Liu in Therapeutic Advances in Medical Oncology</p
Validation of the use of the ROSIER scale in prehospital assessment of stroke
No full textAim: To determine the utility of the Recognition of Stroke in the Emergency Room (ROSIER) scale as a stroke recognition tool among Chinese patients in the prehospital setting. Materials and Methods: Compared with the Cincinnati Prehospital Stroke Scale (CPSS), emergency physicians prospectively used the ROSIER as a stroke recognition tool on suspected patients in the prehospital setting. And, the final discharge diagnosis of stroke or transient ischemic attack made by neurologists, after assessment and review of clinical symptomatology and brain imaging findings, was used as the reference standard for diagnosis in the study. Then, the ROSIER and the CPSS like sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), related coefficient (r) and Kappa value were calculated. Results: In this study, 540 of 582 suspected stroke patients met the study criteria. The CPSS showed a diagnostic Se of 88.77% (95% confidence intervals [CI] 86.11-91.43%), Sp of 68.79% (95% CI 64.88-72.70%), PPV of 87.40% (95% CI 85.97-88.83%), NPV of 71.52% (95% CI 67.71-75.33%) and r of 0.503. Relatively, the ROSIER showed a diagnostic Se of 89.97% (95% CI 87.44-92.64%), Sp of 83.23% (95% CI 80.08-86.38%), PPV of 92.66% (95% CI 90.46-94.86%), NPV of 77.91% (95% CI 74.41-81.41%) and r of 0.584. According to the final discharge diagnosis, both the ROSIER and the CPSS were associated with the final discharge diagnosis (P 0.05). Conclusions: The ROSIER is a sensitive and specific stroke recognition tool for health providers′ use among Chinese patients in the prehospital setting. However, it cannot be used to confidently rule out or identify stroke as a diagnosis. Comprehensive clinical assessment and further examination on potential stroke patients are still important and cannot be replaced. When it is difficult to objectively complete the ROSIER for patients, the CPSS could replace it in the prehospital setting
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Enhanced Assessment of Intelligent Traffic Control Devices for Vehicle And Pedestrian Safety
No full textImproving safety for both motorists and pedestrians has become a national priority in the United States. To address vehicle safety at signalized intersections, flashing yellow arrows (FYAs) have been introduced as an alternative to circular green signals (CGs) during permissive left turns. FYAs have demonstrated effectiveness in reducing driver confusion and crash frequency at intersections. For pedestrian safety at unsignalized locations, pedestrian hybrid beacons (PHBs) have been implemented to provide traffic control and facilitate safer street crossings. While both technologies have demonstrated effectiveness in increasing driver-yielding rates and reducing motor vehicle and pedestrian crashes, existing research lacks comprehensive evaluations of FYAs and PHBs across both individual (driver behavior analysis) and aggregate (crash analysis) levels using empirical data, particularly in the post-COVID-19 era. Based on real-world observations, driver behavior has become more aggressive compared to pre-COVID-19, which may result in more traffic violations and affect the safety effectiveness of these traffic control devices. This dissertation addresses these limitations through a series of evaluations conducted in Arizona, with methodologies and findings that can be applied more broadly. These evaluations include: (1) an analysis of differences in left-turn driver behavior at FYAs and CGs; (2) an investigation of crash trends associated with different FYA operation periods; (3) an examination of driver behavior and interactions at PHBs during nighttime conditions in the post-pandemic era; and (4) an identification of factors influencing pedestrian and bicycle crashes near PHBs.
Permissive left-turn indications, such as FYAs and CGs, can significantly influence left-turn driver behavior, thereby affecting intersection safety. This research leveraged probe data to assess speeds, accelerations, and decelerations of left-turning vehicles at 106 FYA and 116 CG intersection approaches in Tucson, Arizona. Driver-yielding behavior can be indirectly assessed through average speeds, while hard accelerations and decelerations are often associated with potential safety concerns. Tobit and linear mixed-effects models were employed to evaluate factors affecting left-turn behavior. Results showed that FYA approaches generally exhibited lower speeds and higher decelerations than CGs. Vehicles in the outer lanes of dual left-turn approaches with FYAs showed greater acceleration fluctuations. Additionally, vehicles on dual left-turn lane approaches, particularly with FYAs, exhibited lower average speeds than those on single left-turn approaches.
Additionally, the optimal FYA operation periods for specific intersections remain uncertain, with limited research on the safety effects of 24-hour versus time-of-day (TOD) FYA operation periods. Moreover, FYAs are typically used at intersections with a single left-turn lane, leaving their safety impact at dual left-turn lane intersections underexplored. This dissertation evaluated the safety effectiveness of FYAs across different operation periods and left-turn lane configurations. Empirical Bayes (EB) before-and-after studies were conducted using multivariate adaptive regression splines (MARS) and negative binomial (NB) models as safety performance functions (SPFs) to assess the safety effectiveness of different FYA operation periods. SPFs were developed for different combinations of crash types and varying numbers of left-turn lanes. Results showed that 24-hour and TOD FYA operation periods reduced crashes by 8.76% to 50% at intersections with a single or dual left-turn lanes. However, switching from 24-hour to TOD increased total crashes by 31.2% at dual left-turn lane intersections, while single left-turn lane intersections showed a 60% decrease in rear-end crashes.
While safety concerns can be effectively addressed at signalized intersections with FYAs, the safety implications at unsignalized locations remain unclear. With this in mind, another focus of this dissertation is to explore the effectiveness of PHBs. Specifically, nighttime driver behavior and social interactions among drivers, such as peer imitation, at PHBs remain understudied post-pandemic. This dissertation examined these behaviors using video data from four PHB locations in Pima County, Arizona. Results indicated that 94% to 97% of drivers stopped during the steady red phase, but compliance dropped to 53% during the flashing red phase. Among leading-following pairs in a platoon, 50% to 83% of following drivers mimicked the leading vehicle’s behavior, even when the leading driver violated traffic laws. At intersections with a speed limit of 25 mph, 41.7% of drivers resumed travel during the flashing red phase, even when pedestrians were in the crosswalk.
Moreover, contributing factors to pedestrian and bicycle crashes near PHBs remain overlooked, as few studies have recognized situations where individuals may cross roads without activating the PHB, potentially raising safety concerns. Therefore, this dissertation identified characteristics of pedestrians and bicyclists prone to crossing without PHB activation, as well as differences between crash-prone and non-crash-prone PHB locations. This dissertation also examined the factors that impact pedestrian and bicycle crashes in proximity to activated PHBs and accessible PHBs in Tucson, Arizona. Using descriptive analysis and Bayesian multilevel Poisson-Lognormal regressions, results showed that young individuals and males were more likely to cross without PHB activation. Crash odds increased when approach speeds decreased 5 to 10 minutes before crashes and at night (even with activated PHBs) but decreased in regions with more non-White individuals and higher household incomes.
This dissertation provides an understanding of how FYAs and CGs influence driver behavior across various geometric configurations and how FYA operation periods affect intersection safety. The findings support transportation agencies in making informed decisions on FYA deployment and operations. Additionally, this dissertation examines nighttime driver behavior, social interactions, and factors contributing to pedestrian and bicycle crashes near PHBs, guiding PHB design, implementation, education, and traffic control strategies. Furthermore, structured frameworks for evaluating traffic control devices are provided in this dissertation. The proposed methodologies, including MARS, Tobit, and Bayesian multilevel Poisson-Lognormal model, improve crash predictions, address data challenges such as censoring, and manage small sample sizes and overdispersion. The findings also offer actionable recommendations for mid-sized cities with similar demographics and driving patterns considering FYA and PHB implementations.Originally embargoed until 10/01/2026; released on July 14, 2025 per author request, Kimberl
ANALYSIS ON THE INFLUENCE OF OPTIMIZATION PATH OF LOGISTICS SUPPLY CHAIN ECONOMIC MANAGEMENT ON CONSUMERS’ ONLINE SHOPPING PSYCHOLOGY
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Multiple-grid adaptive integral method for general multi-region problems
No full texttextEfficient electromagnetic solvers based on surface integral equations (SIEs) are developed for the analysis of scattering from large-scale and complex composite structures that consist of piecewise homogeneous magnetodielectric and perfect electrically/magnetically conducting (PEC/PMC) regions. First, a multiple-grid extension of the adaptive integral method (AIM) is presented for multi-region problems. The proposed method accelerates the iterative method-of-moments solution of the pertinent SIEs by employing multiple auxiliary Cartesian grids: If the structure of interest is composed of K homogeneous regions, it introduces K different auxiliary grids. It uses the k^{th} auxiliary grid first to determine near-zones for the basis functions and then to execute AIM projection/anterpolation, propagation, interpolation, and near-zone pre-correction stages in the k^{th} region. Thus, the AIM stages are executed a total of K times using different grids and different groups of basis functions. The proposed multiple-grid AIM scheme requires a total of O(N^{nz,near}+sum({N_k}^Clog{N_k}^C)) operations per iteration, where N^{nz,near} denotes the total number of near-zone interactions in all regions and {N_k}^C denotes the number of nodes of the k^{th} Cartesian grid. Numerical results validate the method’s accuracy and reduced complexity for large-scale canonical structures with large numbers of regions (up to 10^6 degrees of freedom and 10^3 regions). Then, a Green function modification approach and a scheme of Hankel- to Teoplitz-matrix conversions are efficiently incorporated to the multiple-grid AIM method to account for a PEC/PMC plane. Theoretical analysis and numerical examples show that, compared to a brute-force imaging scheme, the Green function modification approach reduces the simulation time and memory requirement by a factor of (almost) two or larger if the structure of interest is terminated on or resides above the plane, respectively. In addition, the SIEs are extended to cover structures composed of metamaterial regions, PEC regions, and PEC-material junctions. Moreover, recently introduced well-conditioned SIEs are adopted to achieve faster iterative solver convergence. Comprehensive numerical tests are performed to evaluate the accuracy, computational complexity, and convergence of the novel formulation which is shown to significantly reduce the number of iterations and the overall computational work. Lastly, the efficiency and capabilities of the proposed solvers are demonstrated by solving complex scattering problems, specifically those pertinent to analysis of wave propagation in natural forested environments, the design of metamaterials, and the application of metamaterials to radar cross section reduction.Electrical and Computer Engineerin
A 2-stage vision-based localization methodology for efficient automatic charging of electric vehicles in uncertain environments
No full textData availability statement:
The data that support the findings of this study are available from the corresponding author upon reasonable request.Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.Funding Research supported by the State Key Laboratory of Digital Manufacturing Equipment and Technology, Grant No. DMETKF2021018. GJYC program of GuangZhou, Grant ID. 2024D03J0005. Chunhui Project Foundation of the Education Department of China, Grant No. 202201789
Essays on Digital Goods and Online Markets
No full textInformation technology has revolutionized the way in which sellers engage with potential customers and distribute their products through online channels. However, they also face increasing challenges to remain competitive. For example, in the software industry, the plethora of available applications leads to a highly competitive landscape, making it difficult for new entrants to gain visibility and attract consumer interest. For online platforms, the platform owner not only serves as an intermediary for sellers and buyers but also introduces its own private-label products, further intensifying competition with third-party sellers.
This dissertation investigates the strategic actions sellers undertake to tackle these challenges. In the first essay, we build a game-theoretical model to examine two prevalent strategies, seeding and time-limited freemium, that developers can employ to spur adoption by helping consumers directly or indirectly learn the value of their products. We offer managerial recommendations on the optimal circumstances for implementing each strategy, considering factors such as social and self-learning dynamics, adoption costs, and product value depreciation.
In the second essay, we study the impacts of Amazon launching its private-label products and engaging in self-preferencing for these products on third-party sellers. Our findings show that although Amazon favors its own products in search results, the average sales of third-party products in the affected categories increase more than those in unaffected categories. We then investigate several mechanisms that could contribute to this change. We find that Amazon's private-label products displace lower-quality sellers, foster variety in product designs, and serve as valuable references for third-party sellers to improve their searchability. These factors potentially lead to higher sales and ultimately an increase in consumer welfare, with little impact on prices.Ph.D
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