Search Results (207)

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters. 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

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

During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 10⁶ m³. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers. Full article

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models. Full article

This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N₂] ratio information obtained by the TIMED/GUVI, and electron density (Ne) observations from Swarm satellites. The Prophet time series forecasting model is employed to detect ionospheric anomalies. VTEC variations reveal significant daytime increases in GNSS stations such as GAMG, URUM, and CMUM after the onset of the geomagnetic storm on 1 December, indicating a dayside positive ionospheric response primarily driven by prompt penetration electric fields (PPEF). In contrast, the stations JFNG and CKSV show negative responses, reflecting regional differences. The [O]/[N₂] ratio increased significantly in the southern region between 25°N and 40°N, suggesting that atmospheric gravity waves (AGWs) induced thermospheric compositional changes, which played a crucial role in the ionospheric disturbances. Ne observations from Swarm A and C satellites further confirmed that the intense ionospheric perturbations were consistent with changes in VTEC and [O]/[N₂], indicating the medium-scale traveling ionospheric disturbance was driven by atmospheric gravity waves. Precise point positioning (PPP) analysis reveals that ionospheric variations during the geomagnetic storm significantly impact GNSS positioning precision, with various effects across different stations. Station GAMG experienced disturbances in the U direction (vertical positioning error) at the onset of the storm but quickly stabilized; station JFNG showed significant fluctuations in the U direction around 13:00 UT; and station CKSV experienced similar fluctuations during the same period; station CMUM suffered minor disturbances in the U direction; while station URUM maintained relatively stable positioning throughout the storm, corresponding to steady VTEC variations. These findings demonstrate the substantial impact of ionospheric disturbances on GNSS positioning accuracy in southern and central China during the geomagnetic storm. This study reveals the complex and dynamic processes of ionospheric disturbances over China during the 1–2 December 2023 storm, highlighting the importance of ionospheric monitoring and high-precision positioning corrections during geomagnetic storms. The results provide scientific implications for improving GNSS positioning stability in mid- and low-latitude regions. Full article

Long-term continuous and reliable real-time ocean wave height data are important for climatologists, offshore industries, leisure craft users, and marine forecasters. However, maintaining data continuity and reliability is challenging due to offshore equipment failures and sparse in situ observations. Opposing interactions between wind-driven ocean waves generate acoustic waves near the ocean surface, which can convert to seismic waves at the seafloor and travel through the Earth’s solid structure. These low-frequency seismic waves, known as secondary microseisms, are clearly recorded on terrestrial seismometers offering land-based access to ocean wave states via seismic ground vibrations. Here, we demonstrate the potential of this by estimating ocean Significant Wave Heights (SWHs) in the Northeast Atlantic using continuous recordings from a land-based seismic network in Ireland. Our method involves connecting secondary microseism amplitudes with the ocean waves that generate them, using an Artificial Neural Network (ANN) to quantify the relationship. Time series data of secondary microseism amplitudes together with buoy-derived and numerical model ocean significant wave heights are used to train and test the ANN. Application of the ANN to previously unseen data yields SWH estimates that closely match in situ buoy observations, located approximately 200 km offshore, Northwest of Ireland. Terrestrial seismic data are relatively cheap to acquire, with reliable weather-independent data streams. This suggests a pathway to a complementary, exceptionally cost-effective, data-driven approach for future operational applications in real-time SWH determination. Full article

The process of ultrasonic atomization involves a series of dynamic/topological deformations of free surface, though not always, of a bulk liquid (initially) below the air. This study focuses on such dynamic interfacial alterations realized by changing some acousto-related operating conditions, including ultrasound excitation frequency, acoustic strength or input power density, and the presence/absence of a “stabilizing” nozzle. High-speed, high-resolution imaging made it possible to qualitatively identify four representative transitions/demarcations: (1) the onset of a protrusion on otherwise flat free surface; (2) the appearance of undulation along the growing protuberance; (3) the triggering of emanating beads fountain out of this foundation-like region; and (4) the induction of droplets bursting and/or mist spreading. Quantitatively examined were the two-parameters specifications—on the degrees as well as induction—of the periodicity in the protrusion-surface and beads-fountain oscillations, detected over wider ranges of driving/excitation frequency (0.43–3.0 MHz) and input power density (0.5–10 W/${\mathrm{c}\mathrm{m}}^2$) applied to the ultrasound transducer of flat surface on which the nozzle was either mounted or not. The resulting time sequence of images processed for the extended operating ranges, regarding the fountain structure pertaining, in particular, to recurring beads, confirms the wave-associated nature, i.e., their size “scalability” to the ultrasound wavelength, predictable from the traveling wave relationship. The thresholds in acoustic conditions for each of the four transition states of the fountain structure have been identified—notably, the onset of plausible “bifurcation” in the chain-beads’ diameter below a critical excitation frequency. Full article

Vaccine preventable diseases remain the leading causes of death among children in Sub-Saharan Africa. Background/Objectives: As vaccines provide the best possible protection only when all required doses are received, it is essential to understand how the published literature is designed and conducted regarding the completion of recommended childhood vaccine series for children aged 12–23 months in SSA. Methods: A comprehensive search was conducted across five databases (PubMed, Embase, CINAHL, Web of Science, and Google Scholar) to identify the relevant literature published between January 2000 through December 2023. Results: A total of 53 studies meeting the inclusion criteria were identified from the five databases. Over half of the studies used a cross-sectional design (n = 32, 60.4%), and less than half of the studies were conducted in Ethiopia (n = 23, 43.4%). The prevalence and associated factors of vaccination series completion were the most commonly explored topics in the literature. The most frequently reported factors associated with vaccine series completion included the caregiver’s education level, household wealth status, number of children under five in the household, knowledge of immunization, maternal education, place of residence, gender of the household head or decision maker, utilization of antenatal or postnatal care visits, place of delivery, distance to a healthy facility or travel time, and possession of a vaccination card. Conclusions: This scoping review identified methodological gaps in the published literature, including a lack of publications from many Sub-Saharan Africa countries and insufficient evidence on trends and inequalities in vaccine series completion. Future research on vaccine series completion is recommended to address these gaps. Full article

In this study, we propose a generalized framework based on the Simple Equations Method (SEsM) for finding exact solutions to systems of fractional nonlinear partial differential equations (FNPDEs). The key developments over the original SEsM in the proposed analytical framework include the following: (1) an extension of the original SEsM by constructing the solutions of the studied FNPDEs as complex composite functions which combine two single composite functions, comprising the power series of the solutions of two simple equations or two special functions with different independent variables (different wave coordinates); (2) an extension of the scope of fractional wave transformations used to reduce the studied FNPDEs to different types of ODEs, depending on the physical nature of the studied FNPDEs and the type of selected simple equations. One variant of the proposed generalized SEsM is applied to a mathematical generalization inspired by the classical Boussinesq model. The studied time-fractional Boussinesq-like system describes more intricate or multiphase environments, where classical assumptions (such as constant wave speed and energy conservation) are no longer applicable. Based on the applied SEsM variant, we assume that each system variable in the studied model supports multi-wave dynamics, which involves combined propagation of two distinct waves traveling at different wave speeds. As a result, numerous new multi-wave solutions including combinations of different hyperbolic, elliptic, and trigonometric functions are derived. To visualize the wave dynamics and validate the theoretical results, some of the obtained analytical solutions are numerically simulated. The new analytical solutions obtained in this study can contribute to the prediction and control of more specific physical processes, including diffusion in porous media, nanofluid dynamics, ocean current modeling, multiphase fluid dynamics, as well as several geophysical phenomena. Full article

Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks. Full article

Between 2019 and 2022, passenger volume on China’s urban rail transit system sharply declined due to strict COVID-19 control measures. On 8 January 2023, China implemented the “Class B infectious disease Class B management” policy, marking a significant shift towards a more relaxed approach to epidemic control. The main objective of this study is to evaluate the immediate and lasting effects of this policy on urban rail transit passenger volume. We used interrupted time series (ITS), combined with quasi-Poisson regression models and counterfactual analysis, to analyze monthly urban rail transit operation data covering the period from January 2021 to June 2024 for 42 cities. Our analysis shows that, relative to the expected trend without any intervention, monthly average passenger volume increased by approximately 101.34% after the policy’s implementation, with significant immediate effects observed in 41 cities and significant lasting effects observed in 33 cities. The study concludes that the “Class B infectious disease Class B management” policy has generally promoted the nationwide recovery of urban rail transit passenger volume, although with significant heterogeneity across cities. This result indicates that the reduction in travel restrictions and the restoration of public safety, resulting from the relaxation of COVID-19 prevention and control measures, contributed to the overall recovery of urban rail transit. This study not only provides innovative methodological insights but also offers valuable guidance on developing more effective urban planning strategies and urban rail transit operational measures in the post-pandemic era. Full article

Using a laboratory-scale system, consisting of a primary disinfection tank (PDT) and three intermittently mixed reactors (R1–R3) in series, bulk water and biofilm contributions to chlorine decay were quantified. The reactors (surface-to-volume ratio: 23.7 m⁻¹; retention time in each reactor: 42.6 ± 1.18 h) were fed with plant-filtered water (PFW). Secondary disinfection was carried out in R1. Free chlorine concentration decreased with travel time (R1: 1.2 mg/L; R2: 0.6 mg/L; and R3: 0.12 mg/L). The bacterial number (ATP) decreased from 67 pg/mL in PFW and remained at ~2–3 pg/mL in R1 and R2 but increased back to 68 pg/mL in R3. First-order chlorine decay rate coefficients decreased from R1 to R2, as expected, but increased by five-fold from R2 to R3. The increased bacterial number (ATP) in R3 and batch chlorine decay tests confirmed that bulk water (soluble compounds, microbes, and sediments) contributed approximately 40% of the decay, and the biofilm contributed 60% in R3. When ATP levels in the reactors were combined with literature data, the bacterial number increased significantly when free chlorine decreased below 0.2 mg/L, but data between 0.2 and 0.5 mg/L are limited. More investigation is needed in the future for chlorine < 0.5 mg/L regarding bacterial regrowth and its effect on bulk water chlorine decay. Full article

Route planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-world applicable routing solutions. This framework incorporates practical challenges, such as charging station queue lengths, which significantly influence travel time and energy consumption. Using K-means clustering, the methodology groups charging stations based on geographical proximity, allowing for optimal stop selection and minimizing unnecessary detours. GA optimization is used to refine these routes by evaluating key factors, including travel distance, queue dynamics, and time, to determine paths with the fewest charging stops while maintaining efficiency. By integrating these two techniques, the proposed framework achieves a balance between computational simplicity and adaptability to changing conditions. A series of experiments have demonstrated the framework’s ability to identify the shortest and least congested routes with strategically placed charging stops. The dynamic nature of the model ensures adaptability to evolving real-world scenarios, such as fluctuating queue lengths and travel demands. This research demonstrates the effectiveness of this approach for identifying the shortest, least congested routes with the most optimal charging stations, resulting in significant advancements in sustainable transportation and EV route optimization. Full article

Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models. Full article