The morphometric features of the biceps groove were measured to investigate their correlation with the injury of the pulley and the long head of the biceps tendon (LHBT).
A total of 126 patients ...undergoing arthroscopic rotator cuff repair surgery had their morphological features of bicipital groove evaluated on a 3D reconstruction model of the humeral head. The groove width, groove depth, opening angle, medial wall angle, and inclination angle of the bicipital groove were measured for each patient. During the surgery, the type of injury to the biceps pulley and the degree of long head of biceps tendon injury were assessed. The correlations of these injury assessments with bicipital groove measurements were analyzed.
The average groove width was(12.3 ± 2.1) mm. The average groove depth was(4.9 ± 1.4) mm. The average groove inclination angle was 26.3° ± 8.1°. The average opening angle was 89.8° ± 18.4°. The average medial groove wall angle was 40.6° ± 7.9°.Sixty six patients had injury of the biceps pulley structure, and their Martetschläger classifications were as follows: type I injury in 12 patients, type II injury in 18 patients, and type III injury in 36 patients. The Lafosse grades of Lesions of LHBT were as follows: 72 cases were grade 0 injury, 30 cases were grade I injury, and 24 cases were grade II injury. We found no significant correlation between the opening width, depth, inclination angle, opening angle, and medial wall angle of the morphological features of bicipital groove and injuries of the pulley and the LHBT. The correlation between pulley structure injury and lesions of LHBT was statistically significant.
Lesions of LHBT show strong correlation with pulley injuries.This study does not find a correlation between the injury of the pulley or the LHBT and bicipital groove morphology.
To enhance the resilience of transmission systems with offshore wind farms before the advent of typhoons, a proactive unit commitment scheme is proposed. A novel scenario tree is proposed to quantify ...the uncertain impacts of typhoons on offshore wind farms, transmission lines, and system states, where the inertia support from offshore wind farms and random system state are in‐cooperated. The frequency security constraint is captured by a multi‐variative piece‐wise linear function, where the frequency nadir are simulated under varying inertia and disturbance combinations. The unit commitment problem is formulated as a two‐stage stochastic optimisation problem with nonlinear recourse. This problem is solved by a multi‐cuts Benders decomposition algorithm with finite iteration convergence property. Simulations are conducted on a modified IEEE‐RTS 79 test system with offshore wind farms. Results verify the effectiveness of the proposed model regarding the load shedding and frequency nadir enhancement.
Capture the uncertain impacts on power systems within offshore wind farms using a scenario tree, with uncertain offshore wind output, inertia, line failure, and system state.
Formulate the proactive unit commitment problem as a two‐stage stochastic programming problem with nonlinear recourse.
Reformulate the proactive unit commitment problem as a deterministic mixed‐integer linear programming problem.
Verify the proactive resilient unit commitment performance on a modified IEEE‐RTS 79 test system with offshore wind farms.
Electric vertical takeoff and landing (eVTOL) aircraft have emerged as a potential alternative to the existing transportation system, offering a transition from two-dimensional commuting and ...logistics to three-dimensional mobility. As a groundbreaking innovation in both the automotive and aviation sectors, eVTOL holds significant promise but also presents notable challenges. This paper aims to address the overall aircraft design (OAD) approach specifically tailored for eVTOL in the context of Urban Air Mobility (UAM). In contrast to traditional OAD methods, this study introduces and integrates disciplinary methodologies specifically catered to eVTOL aircraft design. A case study is conducted on a tilt-duct eVTOL aircraft with a typical UAM mission, and the disciplinary performance, including initial sizing, aerodynamics, electric propulsion systems, stability and control, weight, mission analysis and noise, is examined using the OAD methodologies. The findings demonstrate that the current approach effectively evaluates the fundamental aircraft-level performance of eVTOL, albeit further high-fidelity disciplinary analysis and optimization methods are required for future MDO-based eVTOL overall aircraft design.
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•The unique features of the OAD approach for eVTOL compared to conventional aircraft have been identified.•The disciplinary methods of eVTOL OAD have been introduced and integrated on the basis of conventional OAD framework.•A tilt-duct eVTOL aircraft with typical UAM mission profile has been designed and studied using the OAD methods.•Further development on high-fidelity MDO is recommended for enabling the adoption of eVTOL for UAM applications.
Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates ...and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections.
This study reviews tools and approaches developed at universities and institutes for conceptual and preliminary aircraft design. Problem, solution, and behavioral space covered by each tool are ...discussed and a categorization for the methods underlying the different disciplinary tools is proposed. Special attention is given to the search method, if any, embedded in or supported by each tool to explore the proliferation of Multidisciplinary Design Optimization (MDO) in aircraft design tools. The study shows that many tools are available but most are proprietary and none covers all the aspects of the conceptual and preliminary design process. MDO is only a small element in most of the tools. The review can be used for the formulation of requirements and necessities for future aircraft design tools.
Understanding individual mobility behavior is critical for modeling urban transportation. Different types of emerging data sources such as mobile phone records, social media posts, GPS observations, ...and smart card transactions have been used to reveal individual mobility behavior. In this paper, spatio-temporal mobility behaviors are reported using large-scale data collected from a ride-hailing service platform. Using passenger-level travel information, to characterize temporal movement patterns, trip generation characteristics, and distribution of gap time between consecutive trips are revealed. To understand spatial mobility patterns, we observe the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of ride-hailing services users, compared to the findings of existing studies based on other data sources. The results show the potential of developing high-resolution individual-level mobility models that can predict the demand for emerging mobility services with high fidelity and accuracy.
Concrete creep results in the strain increment of concrete-filled steel tube. In this article, a new method was established for creep prediction of concrete-filled steel tube considering the ...creep-recovery effect of the concrete core with creep model and separate creep-recovery model, named two-function method. Five creep models including the recent proposed B4 model and two separate creep-recovery models were used in the method. First, the creep models were compared with 197 test curves of creep of sealed concrete. The results show that the B4 model offers the highest prediction accuracy for load duration time longer than 200 days. Then, the two-function method was used for creep prediction of normal strength concrete-filled steel tube and high-strength concrete-filled steel tube. For comparison, the creep prediction method for concrete-filled steel tube based on the principle of superposition was also adopted. The principle of superposition did not predict the creep of concrete-filled steel tube accurately. The calculation results based on the two-function method are closer to the test data, especially for normal strength concrete-filled steel tube. Finally, the uncertainty of creep in concrete-filled steel tube was analyzed considering the variability of the concrete strength. The results indicate that the variances calculated by the methods with the MC 2010 creep model are smaller than that of the B4 model. The creep strains of concrete-filled steel tube calculated by the two-function method are subject to normal distribution.
•A passenger-to-driver matching model for commuter carpooling was initially proposed.•38.3% of trips can successfully form carpooling trips and save fuel significantly.•The participant flexibilities ...can help matching performance, especially when participant rate in a lower level.•The optimal fee-sharing ratio is not the traditional half to half.•The disincentives seem to have a greater effect on carpooling choice than the incentives.
For the transport sector, promoting carpooling to private car users could be an effective strategy over reducing vehicle kilometers traveled. Theoretical studies have verified that carpooling is not only beneficial to drivers and passengers but also to the environment. Nevertheless, despite carpooling having a huge potential market in car commuters, it is not widely used in practice worldwide. In this paper, we develop a passenger-to-driver matching model based on the characteristics of a private-car based carpooling service, and propose an estimation method for time-based costs as well as the psychological costs of carpooling trips, taking into account the potential motivations and preferences of potential carpoolers. We test the model using commuting data for the Greater London from the UK Census 2011 and travel-time data from Uber. We investigate the service sensitivity to varying carpooling participant rates and fee-sharing ratios with the aim of improving matching performance at least cost. Finally, to illustrate how our matching model might be used, we test some practical carpooling promotion instruments. We found that higher participant role flexibility in the system can improve matching performance significantly. Encouraging commuters to walk helps form more carpooling trips and further reduces carbon emissions. Different fee-sharing ratios can influence matching performance, hence determination of optimal pricing should be based on the specific matching model and its cost parameters. Disincentives like parking charges and congestion charges seem to have a greater effect on carpooling choice than incentives like preferential parking and subsidies. The proposed model and associated findings provide valuable insights for designing an effective matching system and incentive scheme for carpooling services in practice.
On 28 December 2014, the Beijing subway’s fare policy was changed from “Two Yuan” per trip to the era of Logging Ticket Price, charging users by travel mileage. This paper aims at investigating the ...effects of Beijing subway’s new fare policy on the riders’ attitude, travel pattern and demand. A survey analysis was conducted to identify the effects of the new fare policy for Beijing subway on riders’ satisfaction degree and travel pattern associated with the potential influencing factors using Hierarchical Tree-based Regression (HTBR) models. The model results show that income, travel distance and month of travel have significant impacts on the subway riders’ satisfaction degree, while trip purpose, car ownership and travel frequency significantly influence the riders’ stated travel pattern. Overall, the degree of satisfaction could not be effectively recovered within five months after the new fare policy, but the negative public attitude did not depress the subway demand continuously. Based on the further time sequence analyses of the passenger flow volume data for two years, it is concluded that the new policy made the ridership decrease sharply in the first month but gradually came back to the previous level four months later, and then the passenger flow volume kept steady again. The findings in this study indicate that the new fare policy realized the purpose of lowering the government’s financial pressure but did not reduce the subway ridership in a long term perspective.
Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSM0) and the overall efficiency of traffic networks. The real-time data, collected from ...Internet of Things (loT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learning-based approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.
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