Search Results (207)

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

This research examines the traffic disruptions resulting from the collapse of the Francis Scott Key Bridge in Baltimore, utilizing advanced econometric methods and real-time ClearGuide data. Employing Fixed Effects (FEs), Mixed Effects (MEs), Difference-in-Differences (DiDs), and stratified regression models, the study uniquely examines the impacts of congestion across Immediate, Fall, and Winter periods, distinctly separating AM and PM peak patterns. Significant findings include severe PM peak congestion, up to four times greater than AM peak congestion, particularly on critical corridors such as the Harbor Tunnel Thruway northbound and MD-295 northbound. Initial route-level impacts were heterogeneous, gradually becoming uniform as the network adapted. The causal DiD analysis provides strong evidence that increased congestion is causally linked to proximity to the collapse. It is anticipated that incorporating the suggested framework will yield insightful information for stakeholders and decision-makers, such as targeted freight restriction, peak-hour dynamic pricing, corridor-specific signal adjustments, and investments in real-time traffic monitoring systems to strengthen transportation network resilience. Full article

Due to the complexity of the unstructured environment and the high-level requirement of smoothness when a tracked transportation vehicle is traveling, making the vehicle travel as safely and smoothly as when a skilled operator is maneuvering the vehicle is a critical issue worth studying. To this end, this study proposes a trajectory-planning method for human-like maneuvering. First, several field equipment operators are invited to manipulate the model vehicle for obstacle avoidance driving in an outdoor scene with densely distributed obstacles, and the manipulation data are collected. Then, in terms of the lateral displacement, by comparing the similarity between the data as well as the curvature change degree, the data with better smoothness are screened for processing, and a dataset of human manipulation behaviors is established for the training and testing of the trajectory-planning network. Then, using the dynamic parameters as constraints, a two-stage planning approach utilizes a modified deep network model to map trajectory points at multiple future time steps through the relationship between the spatial environment and the time series. Finally, after the experimental test and analysis with multiple methods, the root-mean-square-error and the mean-average-error indexes between the planned trajectory and the actual trajectory, as well as the trajectory-fitting situation, reveal that this study’s method is capable of planning long-step trajectory points in line with human manipulation habits, and the standard deviation of the angular acceleration and the curvature of the planned trajectory show that the trajectory planned using this study’s method has a satisfactory smoothness. Full article

Introduction: Walking is perceived as the most important bodily function for persons with multiple sclerosis (pwMS) and is impaired in more than 70% of pwMS. In addition, the effect of multiple sclerosis (MS) on gait pattern increases in fast walking and during fatiguing exercises, altering the spatiotemporal gait parameters and walking reserve. Objectives: The objective of this study is to investigate the impact of a 12 min intermittent-walking protocol on spatiotemporal gait parameters and on the fatigability of pwMS, as well as the association with perceived exertion and reported symptoms of fatigue. Methods: Twenty-six persons with relapse-remitting MS and twenty-eight healthy controls (HCs) were included in this cross-sectional study. The Modified Fatigue Impact Scale and the Symbol Digit Modality Test were used to evaluate fatigue symptoms and cognitive function, respectively. Participants walked six times during an uninterrupted 2-min period. Before, during the rest periods and after the last 2 min walk, the rate of perceived exertion (RPE) was measured using the Borg Scale, and the spatiotemporal gait parameters were assessed with GaitRite. The cut-off value of 10% deceleration of the distance walked index classified pwMS into two groups: MS Fatigable (MS-F) and MS Non-Fatigable (MS-NF). One-way and two-way Analyses of variance (ANOVAs) were used to verify the effect of time and groups, respectively. Results: PwMS walked slower, travelled shorter distances, and presented shorter step lengths compared to HCs. No effects of the intermittent-walking protocol were found for all pwMS, but the MS-F group had deteriorated walking speed, step length, and cadence. Walking dysfunction was associated with perceived fatigability, reported symptoms of fatigue, cognitive function, and disability. Reported symptoms of fatigue was associated with perceived exertion but not with performance fatigability. Conclusions: Changes in gait parameters were weak to moderately associated with performance fatigability and the perception of effort and disability but not with reported fatigue symptoms, highlighting distinct constructs. The walking speed reserve and step length reserve also emerged as potential early markers of performance decline. Full article

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article

Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely populated urban centers like Casablanca. This paper presents an innovative approach to optimizing last-mile delivery by integrating public transportation into the logistics network to address these challenges. A custom-built environment is developed, utilizing public transportation nodes as transshipment nodes for standardized packets of goods, combined with a realistic simulation of traffic conditions through the integration of the travel time index (TTI) for Casablanca. The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. The experiments show that scenarios integrating public transportation yield significantly higher mean rewards—up to 1.49 million—and more stable policy convergence, compared to negative outcomes when public transportation is absent. The highest-performing configuration, combining PPO with segmented training and public transport integration, achieves the best value loss (0.0129) and learning stability, albeit with a trade-off in task completion. This research introduces a novel, scalable reinforcement learning framework to optimize pickup and delivery with time windows by exploiting both public transportation and traditional delivery vehicles. Full article

The location of housing has a significant influence on its pricing. Generally, spatial self-correlation and spatial heterogeneity phenomena affect housing price data. Additionally, time is a crucial factor in housing price modeling, as it helps understand market trends and fluctuations. Currency market fluctuations also directly affect housing prices. Therefore, in addition to the physical features of the property, such as the area of the residential unit and building age, the rate of exchange (dollar price) is added to the independent variable set. This study used the real estate transaction records from Iran’s registration system, covering February, May, August, and November in 2017–2019. Initially, 7464 transactions were collected, but after preprocessing, the dataset was refined to 7161 records. Unlike feedforward neural networks, the generalized regression neural network does not converge to local minimums, so in this research, the Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) for housing price modeling was developed. In addition to being able to model the spatial–time heterogeneity available in observations, this algorithm is accurate and faster than MLR, GWR, GRNN, and GCW-GRNN. The average index of the adjusted coefficient of determination in other methods, including the MLR, GWR, GTWR, GRNN, GCW-GRNN, and the proposed GTCW-GRNN in different modes of using Euclidean or travel distance and fixed or adaptive kernel was equal to 0.760, 0.797, 0.854, 0.777, 0.774, and 0.813, respectively, which showed the success of the proposed GTCW-GRNN algorithm. The results showed the importance of the variable of the dollar and the area of housing significantly. Full article

Gardnerella vaginalis (GV), an opportunistic pathogen excessively proliferated in vaginal dysbiosis, causes systemic inflammation including vaginitis, neuroinflammation, and osteitis. To understand its systemic inflammation-triggering factor, we purified extracellular vesicles isolated from GV (gEVs) and examined their effect on the occurrence of vaginitis, osteitis, and neuroinflammation in mice with and without ovariectomy (Ov). The gEVs consisted of lipopolysaccharide, proteins, and nucleic acid and induced TNF-α and RANKL expression in macrophage cells. When the gEVs were vaginally exposed in mice without Ov, they significantly induced RANK, RANKL, and TNF-α expression and NF-κB⁺ cell numbers in the vagina, femur, hypothalamus, and hippocampus, as observed in GV infection. The gEVs decreased time spent in the open field (OT) in the elevated plus maze test by 47.3%, as well as the distance traveled in the central area (DC) by 28.6%. In the open field test, they also decreased the time spent in the central area (TC) by 39.3%. Additionally, gEVs decreased spontaneous alteration (SA) in the Y-maze test by 33.8% and the recognition index (RI) in the novel object recognition test by 26.5%, while increasing the immobility time (IT) in the tail suspension test by 36.7%. In mice with OV (Ov), the gEVs also induced RANK, RANKL, and TNF-α expression and increased NF-κB⁺ cell numbers in the vagina, femur, hypothalamus, and hippocampus compared to vehicle-treated mice. When gEVs were exposed to mice with Ov, gEVs also reduced the DC, TC, OT, SA, and RI to 62.1%, 62.7%, 28.2%, 90.7%, and 85.4% of mice with Ov, respectively, and increased IT to 122.9% of mice with Ov. Vaginally exposed fluorescein-isothiocyanate-tagged gEVs were detected in the blood, femur, and hippocampus. These findings indicate that GV-derived gEVs may induce systemic inflammation through the activation of RANK/RANKL-involved NF-κB signaling, leading to systemic disorders including vaginitis, osteoporosis, depression, and cognitive impairment. Therefore, gEVs may be an important risk factor for vaginitis, osteoporosis, depression, and cognitive impairment in women. Full article

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects; however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals. Full article

Background: The framework of measuring effective coverage is conceptually straightforward, yet translation into a single metric is quite intractable. An estimation of a metric linking need, access, utilization, and service quality is imperative for measuring the progress towards Universal Health Coverage. A coverage metric obtained from a household survey alone is not succinct as it only captures the service contact which cannot be considered as actual service delivery as it ignores the comprehensive assessment of provider–client interaction. The study was thus conducted to estimate a one-composite metric of effective coverage by linking varied datasets. Methods: The study was conducted in a rural, remote, and fragile setting in India. Tools encompassing a household survey, health facility assessment, and patient exit survey were administered to ascertain measures of contact coverage and quality. A gamut of techniques linking the varied surveys were employed such as (a) exact match linking and (b) ecological linking using GIS approaches via administrative boundaries, Euclidean buffers, travel time grid, and Kernel density estimates. A composite metric of effective coverage was estimated using linked datasets, adjusting for structural and process quality estimates. Further, the horizontal inequities in effective coverage were computed using Erreygers’ concentration index. The concordance between linkage approaches were examined using Wald tests and Lin’s concordance correlation. Results: A significantly steep decline in measurement estimates was found from crude coverage to effective coverage for an entire slew of linking approaches. The drop was more exacerbated for structural-quality-adjusted measures vis-à-vis process-quality-adjusted measures. Overall, the estimates for effective coverage and inequity-adjusted effective coverage were 36.4% and 33.3%, respectively. The composite metric of effective coverage was lowest for postnatal care (10.1%) and highest for immunization care (78.7%). A significant absolute deflection ranging from −2.1 to −5.5 for structural quality and −1.9 to −8.9 for process quality was exhibited between exact match linking and ecological linking. Conclusions: Poor quality of care was divulged as a major factor of decline in coverage. Policy recommendations such as bolstering the quality via the effective implementation of government flagship programs along with initiatives such as integrated incentive schemes to attract and retain workforce and community-based monitoring are suggested. Full article

To maximize arterial green wave bandwidth utilization, this study aims to minimize average travel delays at coordinated intersections and maximize vehicle throughput. In view of the aforementioned points, the present paper sets out a collaborative optimization method for the control of related intersection groups. The method combines multi-level speed guidance with green wave coordinated control. In an intelligent and connected environment (ICE), the driving trajectory of the initial vehicle is determined in each optimization cycle following the receipt of active speed guidance. Subsequently, the driving trajectories of subsequent vehicles are calculated, with an assessment made as to whether they can leave the intersection before the end of the green light. The subsequent step involves the calculation of a characteristic index, comprising the average speed of the arterial coordination section and its corresponding phase offset. The phase offset is then optimized with the objective of maximizing the comprehensive bandwidth of green wave coordination within the control range. The maximum average speed and the bidirectional cycle comprehensive green wave bandwidth are employed as the control objectives. Finally, a model is constructed through the combination of multi-level vehicle speed guidance with bidirectional cycle green wave coordinated control. A bi-level combinatorial optimization method is constructed through a combinatorial deep Q learning method, named Deep Q Network-Genetic Algorithm (DQNGA), with the objective of obtaining the global optimal solution. Finally, the reliability of the method is validated using traffic flow data and map sensor data on several associated road sections in a city. The results demonstrate that the proposed method reduces the average delay and number of stops by 20.76% and 44.49%, respectively, outperforming conventional traffic control strategies. This suggests that the issue of inefficient utilization of green light time in arterial coordinated signal control has been effectively addressed. Consequently, the efficiency of intersections in the intelligent and connected environment has been enhanced. Full article

Tourism is a key component of many global cities, contributing to their development. This paper examines the relationship between the Travel and Tourism Development Index (TTDI) and the Presence of Global Cities (PGC). Using linear regression models, we analyzed this relationship across different regions and income groups based on a sample of 119 countries, focusing on how variations in PGC are associated with changes in TTDI scores. We analyzed data and results from 2019 (pre-COVID-19), 2021 (during COVID-19), and 2024 (post-COVID-19). The analysis revealed a consistent positive relationship between the PGC and the TTDI across these years, suggesting that countries with higher PGC levels generally achieve higher TTDI scores, emphasizing the important role of global city performance in tourism development. Moreover, the results indicated that while the relationship between global city performance and tourism development is stable over time, it varies across regions and income groups. These findings underscore the importance of global city performance in boosting tourism development and competitiveness, offering valuable insights for policymakers and guiding future research. Full article

Leaky waveguides (LWs) are low-refractive-index films deposited on glass substrates. In these, light can travel in the film while leaking out at the film–substrate interface. The angle at which light can travel in the film is dependent on its refractive index and thickness, which can change with pH when the film is made of pH-responsive materials. Herein, we report an LW comprising a waveguide film made of a synthetic hydrogel containing the monomers acrylamide and N-[3-(dimethylamino)propyl]methacrylamide (DMA) and a bisacrylamide crosslinker for pH measurements between 4 and 8. The response of the LW pH sensor was reversible and the response times were 0.90 ± 0.14 and 2.38 ± 0.22 min when pH was changed from low to high and high to low, respectively. The reported LW pH sensor was largely insensitive to typical concentrations of common interferents, including sodium chloride, urea, aluminum sulfate, calcium chloride, and humic acid. Compared to a glass pH electrode, the measurement range is smaller but is close to the range required for monitoring the pH of drinking water. The pH resolution of the hydrogel sensor was ~0.004, compared to ~0.01 for the glass electrode. Full article