Search Results (22)

Background/Objectives: Baseball pitchers accumulate extreme upper extremity forces during the repetitive, high-velocity motion of throwing, which can lead to shoulder dysfunction and overuse injuries. Although scapular dyskinesis has been linked to various shoulder pathologies, there is a lack of evidence on the specific scapular patterns predisposing pitchers to injury. Methods: A total of 85 professional pitchers from a single professional baseball organization participated in the entirety of this study. All subjects had their scapular positions and motion patterns measured via a digital goniometer prior to the beginning of a competitive season. Scapular upward/downward rotation, anterior/posterior tilt, and internal/external rotation were assessed with the shoulder at rest and during elevation to 120° in the scapular plane. Overuse injuries of the shoulder/elbow sustained during the subsequent competitive season were documented by the team’s medical staff, with statistical comparisons between the injured (n = 34) and non-injured (n = 51) group for each scapular measure. Results: Pitchers who sustained shoulder/elbow injuries demonstrated significantly more scapular anterior tilt during humeral elevation compared to those without an injury (p = 0.04). The difference in anterior tilt between the two groups was 3.8° and had a medium effect size, suggesting clinical relevance. No significant between-group differences were found in any other scapular positions or motions (p > 0.22). Conclusions: Pitchers with increased scapular anterior tilt were more likely to sustain a shoulder/elbow injury, highlighting this kinematic feature as a potential risk factor. This finding suggests that anterior tilt might contribute to soft tissue strain, increasing injury susceptibility in pitchers. Full article

Airport congestion in metropolitan clusters (Metroplex systems) poses significant challenges, particularly when capacity reductions occur due to adverse weather conditions. This study introduces a two-stage robust optimization model aimed at improving the robustness of flight slot allocation in multi-airport systems under such uncertainties. In the first stage, the model minimizes deviations from requested slots while respecting airport and waypoint capacities, turnaround times, and adjustment limits. The second stage dynamically adjusts slot allocations to minimize worst-case displacement costs under potential capacity constraints, ensuring robustness against disruptions. The model is validated using real data from the Beijing–Tianjin–Hebei Metroplex, which includes 468 peak-hour flights. The results demonstrate the model’s effectiveness in eliminating demand–capacity violations, particularly at critical airports such as Beijing Daxing, where initial peak demand exceeded capacity by 36.2%. Post-optimization, the model ensures dynamic capacity adherence and adaptive resource allocation, with varying adjustment intensities across airports (12.7% at Beijing Capital, 28.4% at Daxing, and 39.0% at Tianjin Binhai). Compared to a single-stage robust optimization approach, the two-stage model reduces worst-case displacement by 28.2%, highlighting its superior adaptability. This computationally efficient framework, solved via Gurobi 12.0.2/Python 3.11.9, enhances operational robustness through integrated waypoint modeling and a two-stage decision architecture. Full article

While rainfall occurs for several reasons, climate change and urbanization influence its frequency and geographical disparities. Although recent research suggests that urbanization may lead to increased rainfall, insights into how urbanization can trigger rainfall remain limited. We selected the Dallas–Fort Worth (DFW) metroplex, which has minimal orographic and coastal influences, to analyze the urban impact on rainfall. DFW was divided into 256 equal grids (10 km × 10 km) and grouped into four clusters using K-means clustering based on the urbanization ratio. Using Multi-Sensor Precipitation Estimator data (with a spatial resolution of 4 km), we examined rainfall exceeding the 95th percentile (i.e., extreme rainfall) on low synoptic days to highlight localized effects. The urban heat island (UHI) effect was estimated based on the average temperature difference between the urban core and the other three non-urban clusters. Multiple rainfall events were monitored on an hourly basis. Potential linkages between urbanization, the UHI, extreme rainfall, wind speed, wind direction, convective inhibition, and convective available potential energy were evaluated. An intense UHI within the DFW area triggered a tornado, resulting in maximum rainfall in the urban core area under high wind speeds and a dominant wind direction. Our findings further clarify the role of urbanization in generating extreme rainfall events, which is essential for developing better policies for urban planning in response to intensifying extreme events due to climate change. Full article

The terminal maneuvering area (TMA) serves as a critical transition zone between upper enroute airways and airports, representing one of the most complex regions for managing high volumes of arrival and departure traffic. This paper presents the multi-route TMA scheduling problem as an optimization challenge aimed at optimizing TMA interventions, such as rerouting, speed control, time-based metering, dynamic minimum time separation, and holding procedures; the objective function minimizes schedule deviations and the accumulated holding time. Furthermore, the problem is formulated as a mixed-integer linear program (MILP) to facilitate finding solutions. A rolling horizon control (RHC) dynamic optimization framework is also introduced to decompose the large-scale problem into manageable subproblems for iterative resolution. To demonstrate the applicability and effectiveness of the proposed scheduling models, a hub airport—Chengdu Tianfu International Airport (ICAO code: ZUTF) in the Cheng-Yu Metroplex—is selected for validation. Numerical analyses confirm the superiority of the proposed models, which are expected to reduce aircraft delays, shorten airborne and holding times, and improve airspace resource utilization. This study provides intelligent decision support and engineering design ideas for the macroscopic-level collaborative optimization framework of the Integrated Arrival–Departure and Surface (IADS) system. Full article

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges. Full article

This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R² value of 0.96 across all target variables, including 0.99 for PM_2.5 and 0.91 for PM_10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively. Full article

In this study, our aim is to diagnose how two quasi-linear convective systems (QLCS) are organized so one can determine the possible role of the city of Chicago, IL, USA, in modifying convective precipitation systems. In this Part I of a two-part study, we employ large-scale analyses, radiosonde soundings, surface observations, and Doppler radar data to diagnose the precursor atmospheric circulations that organize the evolution of two mesoscale convective systems and compare those circulations to radar and precipitation. Several multi-scale processes are found that organize and modify convection over the Chicago metroplex. Two sequential quasi-linear convective systems (QLCS #1 and #2) were organized that propagated over Chicago, IL, USA, during an eight-hour period on 5–6 July 2018. The first squall line (QLCS #1) built from the southwest to the northeast while strengthening as it propagated over the city, and the second (QLCS #2) propagated southeastwards and weakened as it passed over the city in association with a polar cold front. The weak upper-level divergence associated with a diffluent flow poleward of an expansive ridge built over and strengthened a low-level trough and confluence zone, triggering QLCS #1. Convective downdrafts from QLCS #1 produced a cold pool that interacted with multiple confluent low-level jets surrounding and focused on the metroplex urban heat island, thus advecting the convection poleward over the metroplex. The heaviest precipitation occurred just south-southeast of Midway Airport, Chicago. Subsequently, a polar cold front propagated into the metroplex, which triggered QLCS #2. However, the descending air above it under the polar jet and residual cold pool from QLCS #1 rapidly dissipated the cold frontal convection. This represents a case study where very active convection built over the metroplex and was likely modified by it, as evidenced in numerical simulations to be described in Part II. Full article

Integrated arrival–departure and surface scheduling (IADS) is a critical research task in next-generation air traffic management that aims to harmonize the complex and interrelated processes of airspace and airport operations in the Metroplex. This paper investigates the microscopic-level collaborative optimization framework for IADS operations, i.e., the problem of coordinating aircraft scheduling on runways and taxiways. It also describes the mixed-integer linear programming (MILP) bi-layer decision support for solving this problem. In runway scheduling, a combined arrival–departure scheduling method is introduced based on our previous research, which can identify the optimal sequence of arrival and departure streams to minimize runway delays. For taxiway scheduling, the Multi-Route Airport Surface Scheduling Method (MASM) is proposed, aiming to determine the routes and taxi metering for each aircraft while minimizing the gap compared with the runway scheduling solution. Furthermore, this paper develops a feedback mechanism to further close the runway and taxiway schedule deviation. To demonstrate the universality and validity of the proposed bi-layer decision support method, two hub airports, Chengdu Shuangliu International Airport (ICAO code: ZUUU) and Chengdu Tianfu International Airport (ICAO code: ZUTF), within the Cheng-Yu Metroplex, were selected for validation. The obtained results show that the proposed method could achieve closed-loop decision making for runway scheduling and taxiway scheduling and reduce runway delay and taxi time. The key anticipated mechanisms of benefits from this research include improving the efficiency and predictability of operations on the airport surface and maintaining situational awareness and coordination between the stand and the tower. Full article

Over time, the rapid expansion of civil aviation infrastructure has led to the establishment of multi-airport systems (MASs) or Metroplex, where airports situated in close proximity form interconnected networks. Given that individual airport capacities often fall short of meeting flight scheduling demands, devising effective multi-airport flight scheduling methods becomes imperative. This article introduces a novel MAS slot expansion configuration framework centered on coupling terminal areas. In contrast to conventional airport capacity slot expansion approaches, this framework demonstrates superior configurational efficacy within respective airport terminal environments. The model outlined in this research identifies the terminal control sector as the pivotal resource node within the interconnected terminal area, aiming to maximize the total expanded slots while minimizing the overall unfairness among airports within the terminal airspace. Employing the ε-constraint method facilitates the transformation of the minimization objective into solvable constraint conditions. Subsequently, leveraging Beijing Metroplex as a case study, the research devises benchmark, single-airport, multi-airport minimum, and multi-airport maximum scenarios to compare and analyze configuration outcomes in terms of key resource allocation impacts and coupled resource utilization efficiencies. Ultimately, employing the AirTOp fast-time simulation model validates each scenario, demonstrating that the proposed configuration method yields reduced delay levels and fewer conflicts in simulation environments. Full article

As atmospheric CO₂ emissions and the trend of urbanization both increase, the ability to accurately assess the CO₂ budget from urban environments becomes more important for effective CO₂ mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO₂ modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO₂ analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO₂ values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km⁻² month⁻¹ captured in Summer 2016 by the three measurement towers, providing evidence of the receptors’ ability to constrain the domain. Finally, a case study showcases the model’s ability to capture the rapid change in CO₂ when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO₂ investigations in the region, which can inform policymakers carrying out CO₂ mitigation strategies. Full article

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning. Full article

The civil aviation industry is experiencing significant growth in air traffic density within terminal areas, necessitating improved air traffic efficiency. In China’s pursuit of world-class airport clusters, operational complexities arise due to the co-location of these airports in the same terminal area airspace, resulting in lower operational efficiency. To mitigate congestion and flight delays, this study proposes an integrated model that considers multiple runways and route selections, accounting for actual route point restrictions. Utilizing actual operational data from Shanghai metroplex, the proposed model is validated. The study focuses on the airport metroplex system and presents a comprehensive mixed-integer programming (MIP) model for arrival sequencing, considering multiple airports, runways, and routes. The maximum landing efficiency is adopted as the objective function, optimizing arrival scheduling while considering time intervals, route selection, and landing constraints. The Multi-waypoint Rolling Horizon Control (MWRHC) algorithm is employed to tackle time-efficiency challenges, ensuring flight safety by continuous monitoring of flights in the terminal area. Comparative analysis reveals the algorithm’s superior optimization performance for single-runway airports compared to dual-runway airports. Overall, the proposed model and algorithm effectively improve the efficiency of multi-airport arrival scheduling in airport metroplex systems. Full article

The development of a Multiple Airport Region (MAR) could lead to fierce competition. To evaluate the level of homogenization between airports in a MAR, a homogenization evaluation method based on multi-dimensional indicators was developed. A multi-dimensional indicator system is proposed that takes into account infrastructure, integrated support, operational efficiency and airline networks. Then the Critic method and the Delphi method are used to assign hierarchical weights for each dimension of the indicators. The multi-layer homogenization matrix of the airport pairs within the MAR is derived. For airport pairs with high comprehensive homogenization, suggestions are provided according to analysis of the indicators. This study paper selected three typical MARs internationally to demonstrate the advantage of the proposed approach. The airport pairs with high a homogenous coefficient (greater than 0.5) were selected to analyze the reasons causing high homogeneity. Results show that the multi-dimensional indicators and hierarchical fusion captured the characteristics of the homogenization of MAR. Most airport pairs in New York MAR and in London MAR had strong differentiation of route network layout, airport pairs in Greater Bay MAR had ambiguous division of labor and low homogenization of route network, except CAN and SZX airports. Suggestions are discussed separately to mitigate the homogeneity of the airports in the MAS, thus, to improve the operation performance of the MARs. Full article

Disasters have different shapes, and one of them is sudden landslides, which can put the safety of highway users at risk and result in crucial economic damage. Along with the risk of human losses, each day a highway malfunctions causes high expenses to citizens, and repairing a failed highway is a time- and cost-consuming process. Therefore, correct highway functioning can be categorized as a high-priority reliability factor for cities. By detecting the failure factors of highway embankment slopes, monitoring them in real-time, and predicting them, managers can make preventive, preservative, and corrective operations that would lead to continuing the function of intracity and intercity highways. Expansive clay soil causes many infrastructure problems throughout the United States, and much of Mississippi’s highway embankments and fill slopes are constructed of this clay soil, also known as High-Volume Change Clay Soil (HVCCS). Landslides on highway embankments are caused by recurrent volume changes due to seasonal moisture variations (wet-dry cycles), and the moisture content of the HVCCS impacts soil shear strength in a vadose zone. Soil Matric Suction (SMS) is another indication of soil shear strength, an essential element to consider. Machine learning develops high-accuracy models for predicting the SMS. The current work aims to develop hybrid intelligent models for predicting the SMS of HVCCS (known as Yazoo clay) based on field instrumentation data. To achieve this goal, six Highway Slopes (HWS) in Jackson Metroplex, Mississippi, were extensively instrumented to track changes over time, and the field data was analyzed and generated to be used in the proposed models. The Artificial Neural Network (ANN) with a Bayesian Regularization Backpropagation (BR-BP) training algorithm was used, and two intelligent systems, Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA) were developed to optimize the ANN-BR algorithm for predicting the HWS’ SMS by utilizing 13,690 data points for each variable. Several performance indices, such as coefficient of determination (R²), Mean Square Error (MSE), Variance Account For (VAF), and Regression Error Characteristic (REC), were also computed to analyze the models’ accuracy in prediction outcomes. Based on the analysis results, the PSO-ANN outperformed the BOA-ANN, and both had far better performance than ANN-BR. Moreover, the rainfall had the highest impact on SMS among all other variables and it should be carefully monitored for landslide prediction HWS. The proposed hybrid models can be used for SMS prediction for similar slopes. Full article

Inter-pathologist variation is widely recognized across human and veterinary pathology and is often compounded by missing animal or clinical information on pathology submission forms. Variation in pathologist threshold levels of resident inflammatory cells in the tissue of interest can further decrease inter-pathologist agreement. This study applied a predictive modeling tool to bladder histology slides that were assessed by four pathologists: first without animal and clinical information, then with this information, and finally using the predictive tool. All three assessments were performed twice, using digital whole-slide images (WSI) and then glass slides. Results showed marked variation in pathologists’ interpretation of bladder slides, with kappa agreement values of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our predictive tool was applied, for digital WSI and glass slides. The concurrence of test pathologists to the reference diagnosis was 60% overall. This study provides a starting point for the use of predictive modeling in standardizing pathologist agreement in veterinary pathology. It also highlights the importance of high-quality whole-slide imaging to limit the effect of digitization on inter-pathologist agreement and the benefit of continued standardization of tissue assessment in veterinary pathology. Full article