Search Results (29)

The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently. Full article

This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. Results show a significant reduction in emergency vehicle response times and a measurable increase in driver awareness of pedestrians at crossings. The findings underscore the potential of V2I technologies to improve traffic flow, reduce risks for vulnerable road users, and contribute to the advancement of safer and smarter urban transportation systems. Full article

Public transport is an integral part of sustainable urban development when its use is promoted by setting bus priority routes (BPRs). BPRs provide clear mobility benefits, but they raise pedestrian safety concerns. In this study, observations were conducted at signalized intersections with two types of BPRs, center-lane and curbside, aiming to characterize pedestrian crossing behaviors, with a particular focus on red-light crossings. We found that at intersections with center-lane BPRs, 30% of pedestrians crossed at least one crosswalk on red, while at another type, 11% crossed on red. Multivariate analyses showed that the risk of crossing on red was substantially higher at intersections with center-lane vs. curbside BPRs; it was also higher among pedestrians crossing to/from the bus stop, males, and young people but lower under the presence of other waiting pedestrians. Furthermore, among pedestrians crossing on red at center-lane BPRs, over 10% did not check the traffic before crossing and another 10% checked the traffic in the wrong direction, thus further increasing the risk. At center-lane BPRs, infrastructure solutions are needed to reduce pedestrian intention to cross on red. Additionally, education and awareness programs for pedestrians should be promoted to emphasize the heightened risk of red-light crossing at intersections with BPRs. Full article

This study introduces a multi-objective deep reinforcement learning (DRL)-based adaptive transit signal priority control framework designed to enhance safety and efficiency in mixed-autonomy traffic environments. The framework utilizes real-time data from connected and automated vehicles (CAVs) to define states, actions, and rewards, with traffic conflicts serving as the safety reward and vehicle waiting times as the efficiency reward. Transit signal priority strategies are incorporated, assigning weights based on vehicle type and passenger capacity to balance these competing objectives. Simulation modeling, based on a real-world intersection in Changsha, China, evaluated the framework’s performance across multiple CAV penetration rates and weighting configurations. The results revealed that a 5:5 weight ratio for safety and efficiency achieved the best trade-off, minimizing delays and conflicts for all vehicle types. At a 100% CAV penetration rate, delays and conflicts were most balanced, with buses showing an average waiting time of 4.93 s and 0.4 conflicts per vehicle, and CAVs achieving 1.97 s and 0.49 conflicts per vehicle, respectively. In mixed traffic conditions, the framework performed best at a 75% CAV penetration rate, where buses, cars, and CAVs exhibited optimal efficiency and safety. Comparative analysis with fixed-time signal control and other DRL-based methods highlights the framework’s adaptability and robustness, supporting its application in managing mixed traffic and enabling intelligent transportation systems for future smart cities. Full article

Sugarcane Pokkah Boeng (PB), a fungal disease caused by Fusarium spp., poses a significant threat to sugar industries globally. Breeding sugarcane varieties resistant to PB has become a priority, and the mining of PB resistance genes and the development of molecular markers provide a solid foundation for this purpose. This work comprehensively analyzes the genetic components of sugarcane’s resistance to PB using transcriptome sequencing. A segregating population was created by crossing the susceptible parent ROC25 with the resistant parent Yunzhe89-7, which is a traditional cultivar known for its PB resistance. Transcriptome analysis uncovered many differentially expressed genes (DEGs) associated with PB resistance. Utilizing weighted gene co-expression network analysis (WGCNA), we identified gene modules closely related to disease phenotypes. We annotated their functions with bioinformatics tools, particularly focusing on genes enriched in the plant immune response’s MAPK signaling pathway and ABC transporter synthesis pathways. In addition, by integrating whole-genome resequencing data of parental lines and transcriptome data of progeny, we identified a series of putative molecular markers that potentially effectively differentiate between highly resistant and susceptible materials. Our study provides crucial genetic resources and molecular methodologies that are essential for the advancement of sugarcane varieties with improved resistance to PB. These innovations are expected to accelerate the breeding process greatly. Full article

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios. Full article

Toona ciliata var. pubescens, known as “Chinese mahogany”, has high commercial value and is classified as a level II priority protected wild plant in China. However, due to overexploitation and its poor natural regeneration capacity, natural T. ciliata var. pubescens forests show varying degrees of decline in habitat adaptability. Arbuscular mycorrhizal fungi (AMF) symbiosis presents a potential strategy to enhance its regeneration. In this study, T. ciliata var. pubescens seedlings were inoculated with Septoglomus viscosum, followed by RNA-Seq analysis to compare gene expression differences between AMF-inoculated (AMI) and non-mycorrhizal (NM) treatments three months post-inoculation. A total of 16,163 differentially expressed genes (DEGs) were upregulated by AMF colonization, constituting 96.46% of the total DEGs. Specifically, 14,420 DEGs were exclusively expressed in the AMI treatment, while 35 DEGs were completely silenced. Most of the upregulated DEGs were located on the cell membrane, nucleus, and cytoskeleton and functioned in protein binding, S-adenosylmethionine-dependent methyltransferase activity, and lipid binding during cellular/macromolecule/protein localization, intracellular/protein transport, the cell cycle, and signal transduction. Additionally, lots of key genes related to oxidative stress responses, nutrient transport, and small GTPase-mediated signal transduction were found to be upregulated. These results suggest that AMF inoculation may enhance root cell growth by activating genes involved in nutrient uptake, stress responses, signal transduction, and substance transportation. This study elucidates the molecular mechanisms underlying the growth promotion of T. ciliata var. pubescens through AMF symbiosis, laying a foundation for the future application of AMF in its natural forest regeneration. Full article

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed. Full article

The efficient production of silkworm silk is crucial to the silk industry. Silk protein synthesis is regulated by the juvenile hormone (JH) and 20-Hydroxyecdysone (20E). Therefore, the genetic regulation of silk production is a priority. JH binding protein (JHBP) transports JH from the hemolymph to target organs and cells and protects it. In a previous study, we identified 41 genes containing a JHBP domain in the Bombyx mori genome. Only one JHBP gene, BmJHBPd2, is highly expressed in the posterior silk gland (PSG), and its function remains unknown. In the present study, we investigated the expression levels of BmJHBPd2 and the major silk protein genes in the high-silk-producing practical strain 872 (S872) and the low-silk-producing local strain Dazao. We found that BmJHBPd2 was more highly expressed in S872 than in the Dazao strain, which is consistent with the expression pattern of fibroin genes. A subcellular localization assay indicated that BmJHBPd2 is located in the cytoplasm. In vitro hormone induction experiments showed that BmJHBPd2 was upregulated by juvenile hormone analogue (JHA) treatment. BmKr-h1 upregulation was significantly inhibited by the overexpression of BmJHBPd2 (BmJHBPd2OE) at the cell level when induced by JHA. However, overexpression of BmJHBPd2 in the PSG by transgenic methods led to the inhibition of silk fibroin gene expression, resulting in a reduction in silk yield. Further investigation showed that in the transgenic BmJHBPd2OE silkworm, the key transcription factor of the JH signaling pathway, Krüppel homolog 1 (Kr-h1), was inhibited, and 20E signaling pathway genes, such as broad complex (Brc), E74A, and ultraspiracle protein (USP), were upregulated. Our results indicate that BmJHBPd2 plays an important role in the JH signaling pathway and is important for silk protein synthesis. Furthermore, our findings help to elucidate the mechanisms by which JH regulates silk protein synthesis. Full article

Looking ahead: transforming conventional public transport prioritization into C-ITS G5 services. The city of Frankfurt aims to digitize its public transport prioritization system in order to fulfill the requirements of future public transport communication standards and, moreover, to build on this very infrastructure for the development of imminent C-ITS services. Therefore, the communication systems of the mobility and transport provider VGF (Verkehrsgesellschaft Frankfurt am Main mbH) are being revised fundamentally by implementing new technologies for Car2X C-ITS G5 communication. The hardware components of the C-ITS system are strategically positioned with the help of a newly developed planning tool that identifies and determines the range of communication. For highly significant sites and locations of the hardware components, the calculated data are validated by utilizing measurements within a mobile setup. The operational stability and the development of previously unused potential are then carried out via the combination of the C-ITS services TSP (Traffic Signal Priority) and GLOSA (Green Light Optimized Speed Advisory). The overlay of the C-ITS services results in a high level of operational stability. As a result, potentials can be adequately employed through the sensible shifting of waiting times to the stops and a smooth flow of traffic through information on optimal speed and remaining times of the traffic light potentials. This paper presents a new methodology with which it is now possible to plan and evaluate C-ITS with regard to service distribution and radio propagation. Full article

This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future. Full article

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%. Full article

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport. Full article

Public transport priority is the development trend in public transport, and signal priority is its main means. In order to further improve the accuracy of delay calculation and realize the priority of bus signals, this paper proposes the idea of multiple conversion criteria and consideration of stop time for the coordination and control of bus and car mixed traffic flow trunk roads. First of all, on the basis of in-depth analysis of the differences in the characteristics of bus and car models, a multi-conversion standard delay calculation method is proposed, and its effectiveness is verified by simulation. The results show that compared with the single conversion standard delay calculation method, the average delay error of cars and buses calculated by this method is reduced by 22.54% and 82.21%, respectively. Then, the influence of bus stops on bus speed and delay is further analyzed, and the coordinated control model of bus priority trunk roads considering bus stops is constructed with the passenger capacity of each bus line and the per capita delay as the goal, and the solution is given. Finally, 178 randomly generated examples are used to verify and analyze the effectiveness and sensitivity of this model. Full article

Bus bunching is a severe problem that affects the service levels of public transport systems. Most of the previous studies in the field of Bus Signal Priority (BSP) and Transit Signal Priority (TSP) focus on reducing a bus delay at signalised intersections and ignore the importance of balancing the bus headways. However, since general BSP methods allocate uneven priorities for individual buses, the headways of bus sequences are prioritised or delayed randomly, increasing the likelihood of bus bunching. To address this problem and to improve the reliability of bus services, we propose an online optimisation model to determine the signal duration and splits for each traffic intersection and each signal cycle for bus priority. The proposed model is able to induce the signal timing back to a baseline when the BSP request frequency is low. Using the proposed model, a statistically significant reduction of 10.0% was achieved for bus headway deviation and 6.4% for passenger waiting times. The simulation-based evaluation results also indicate that the proposed model does not affect the efficiency of bus services and other vehicles significantly. Full article