Search Results (989)

Sound planning for urban mobility is a key facet of securing a sustainable future for our urban systems, and requires the careful and comprehensive assessment of its components, such as the status of the cities’ public transportation network, and how urban planners should invest in developing it. We use agent-based modelling, a tried and true method for such endeavours, for studying the history, planned future works and possible evolution of the tram line network in the Greater Geneva region. We couple these models with knowledge graphs, in a way that both are able to mutually enrich each other. Results show that the information organisation powers of knowledge graphs are highly relevant for effortlessly recounting past events and designing scenarios to be directly incorporated inside the agent-based model. The model features all 5 tram lines from the current real-world network, servicing a total of 15 communes. In turn, the model is capable of replaying past events, predicting future developments and exploring user-defined scenarios. It also harnesses its self-organisation properties to autonomously reconstruct an artificial public transportation network for the region based on two different initial networks, servicing up to 29 communes depending on the scenario. The data gathered from the simulation is effortlessly imported back into the initial knowledge graphs. The artificial networks closely resemble their real-world counterparts and demonstrate the predictive and prescriptive powers of our agent-based model. They constitute valuable assets towards a comprehensive assessment of urban mobility systems, compelling progress for the agent-based modelling field, and a convincing demonstration of its technical capabilities. Full article

The complex environmental conditions at Cr-contaminated sites, characterized by uneven ion distribution, oxidants competition, and limited solid-phase mobility, lead to inadequate mixing of Fe-based reducing agents with Cr, posing significant challenges to the effectiveness of Cr remediation through Cr-spinel precipitation. This study investigates the distinct roles of Fe(II), Fe(III), and Cr(III) in Cr-spinel crystallization under ambient temperature and pressure. X-ray diffraction, scanning electron microscopy, transmission electron microscopy, X-ray absorption near-edge structure spectroscopy, and Mössbauer spectroscopy were employed to elucidate the phase composition, microstructure, and ion coordination within the precipitates. Our findings indicate that Fe(II) acts as a catalyst in the formation of the spinel phase, occupying octahedral sites within the spinel structure. Under the catalytic influence of Fe(II), Fe(III) transitions into the spinel phase, occupying both the tetrahedral and the remaining octahedral sites. Meanwhile, Cr(III), due to its high octahedral site preference energy, preferentially occupies the octahedral sites. When Fe(II) or Fe(III) is present but does not meet the ideal stoichiometric ratio, a deficiency in Fe(II) leads to low yield and poor crystallinity of Cr-spinel, whereas a deficiency in Fe(III) can completely inhibit its formation. Conversely, when either Fe(II) or Fe(III) is in excess, the formation of Cr-spinel remains feasible. Furthermore, metastable Cr phases can be transformed into stable Cr-spinel by adjusting the Fe(II)/Fe(III)/Cr(III) ratio. These results highlight the broad range of conditions under which Cr-spinel mineralization can occur in environmental settings, enhancing our understanding of the mechanisms driving Cr-spinel formation in Cr-contaminated sites treated with Fe-based reducing agents. This research provides critical insights for optimizing Cr remediation strategies. Full article

Heavy metal (HM) contamination in soil poses a severe environmental threat, jeopardizing ecosystem health and potentially entering the food chain through plant uptake. Phytoremediation, a bioremediation technique utilizing plants to remove or immobilize contaminants, offers a sustainable and eco-friendly solution for HM remediation. This study investigated the interactive effects of arbuscular mycorrhizal fungi (AMF) and chelating agents (EDTA and DTPA) on the growth of maize (Zea mays L.) and alfalfa (Medicago sativa L.) cultivated in metal-contaminated soil and their impact on HM uptake by these plants. The findings revealed that AMF and chelating agents have complex interactive effects on plant growth and metal accumulation. Maize (Zea mays L.) shoot dry matter increased with AMF and chelating agents at lower concentrations. Both plants generally showed a significant (p ≤ 0.05) increase in shoot dry matter with amendments, with AMF × EDTA (10 mmol/kg) being the most effective for alfalfa. DTPA and EDTA generally reduced the DTPA-extractable metals in soil, suggesting potential for metal removal. However, the effects of AMF on metal availability were variable. Metal concentrations in maize (Zea mays L.) shoots increased with increasing DTPA and EDTA concentrations, while the effects of AMF were more complex. The alfalfa shoot metal content showed varied responses, with EDTA (5 mmol/kg) effectively reducing the metal uptake. In general, treatments involving chelating agents (DTPA and EDTA) tend to result in higher bioaccumulation factor (BF) values compared to the non-treated controls for most HMs in both plant species. Mycorrhizal fungi (AMF) treatment alone or in combination with chelating agents also showed that varied effects on HM uptake in both the alfalfa and maize treatments with chelating agents, especially at higher concentrations, generally promoted the greater translocation of HMs in both plant species. Both alfalfa and maize responded differently to treatments, with some treatments showing higher translocation factor (TF) values for certain HMs in one species compared to the other. Mycorrhizal fungi (AMF) treatment alone or in combination with chelating agents also showed varied effects on HM uptake and translocation in both alfalfa and maize. Further research is required to optimize remediation strategies that balance plant health and metal mobilization. Full article

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. Full article

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand’s evolving electric mobility landscape. Full article

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions. Full article

Rapid and specific diagnosis is necessary for both the treatment and prevention of infectious diseases. Bacteria and viruses that enter the bloodstream can trigger a strong immune response in infected animals and humans. The fluorescence polarization assay (FPA) is a rapid and accurate method for detecting specific antibodies in the blood that are produced in response to infection. One of the first examples of FPA is the non-competitive test for detecting brucellosis in animals, which was followed by the development of other protocols for detecting various infections. Fluorescently labeled polysaccharides (in the case of brucellosis and salmonellosis) or specific peptides (in the case of tuberculosis and salmonellosis, etc.) can be used as biorecognition elements for detecting infections. The availability of new laboratory equipment and mobile devices for fluorescence polarization measurements outside the laboratory has stimulated the development of new fluorescence polarization assays (FPAs) and the emergence of commercial kits on the market for the detection of brucellosis, tuberculosis, and equine infectious anemia viruses. It has been shown that, in addition to antibodies, the FPA method can detect both viruses and nucleic acids. The development of more specific and sensitive biomarkers is essential for the diagnosis of infections and therapy monitoring. This review summarizes studies published between 2003 and 2023 that focus on the detection of infections using FPA. Furthermore, it demonstrates the potential for using new biorecognition elements (e.g., aptamers, proteins, peptides) and the combined use of FPA with new technologies, such as PCR and CRISPR/Cas12a systems, for detecting various infectious agents. Full article

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks. Full article

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow. Full article

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols. Full article

This paper uses smart card data from Melbourne’s public transport network to model and evaluate the impacts of a flexible on-demand transport system. On-demand transport is an emerging mode of urban passenger transport that relies on meeting passenger demand for travel using dynamic and flexible scheduling using shared vehicles. Initially, a simulation model was developed to replicate existing fixed-schedule bus performance and was then extended to incorporate on-demand transport services within the same network. The simulation results were used to undertake a comparative analysis which included reliability, service quality, operational efficiency, network-wide effectiveness, and environmental impacts. The results showed that on-demand buses reduced average passenger trip time by 30%, increased vehicle occupancy rates from 8% to over 50%, and reduced emissions per passenger by over 70% on an average weekday compared to fixed-schedule buses. This study also offers insights for successful on-demand transport implementation, promoting urban sustainability. It also outlines future research directions, particularly the need for accurate short-term passenger demand prediction to improve service provision and passenger experience. Full article

Corynebacterium striatum, present in the microbiota of human skin and nasal mucosa, has recently emerged as a causative agent of hospital-acquired infections, notable for its resistance to multiple antimicrobials. Its mobilome comprises several mobile genetic elements, such as plasmids, transposons, insertion sequences and integrons, which contribute to the acquisition of antimicrobial resistance genes. This study analyzes the contribution of the C. striatum mobilome in the transfer and dissemination of resistance genes. In addition, integrative and conjugative elements (ICEs), essential in the dissemination of resistance genes between bacterial populations, whose role in C. striatum has not yet been studied, are examined. This study examined 365 C. striatum genomes obtained from the NCBI Pathogen Detection database. Phylogenetic and pangenome analyses were performed, the resistance profile of the bacterium was recognized, and mobile elements, including putative ICE, were detected. Bioinformatic analyses identified 20 antimicrobial resistance genes in this species, with the Ermx gene being the most predominant. Resistance genes were mainly associated with plasmid sequence regions and class 1 integrons. Although an ICE was detected, no resistance genes linked to this element were found. This study provided valuable information on the geographic spread and prevalence of outbreaks observed through phylogenetic and pangenome analyses, along with identifying antimicrobial resistance genes and mobile genetic elements that carry many of the resistance genes and may be the subject of future research and therapeutic approaches. Full article

Sustainability in cities, and its accurate and exhaustive assessment, represent a major keystone of environmental sciences and policy making in urban planning. This study aims to provide methods for a reproducible, descriptive, predictive and prescriptive analysis of urban residential choices and mobility, which are key components of an urban system’s sustainability. Using the DPSIR framework for building agent evolution rules, we design an agent-based model of the canton of Geneva, Switzerland. The model leverages real geographical data for the canton of Geneva and its public transportation network. The resulting simulations show the dynamics of the relocation choices of commuters, in terms of the function of their travel time by public transportation to their workplace. Results show that areas around the city centre are generally preferred, but high rent prices and housing availability may prevent most residents from relocating to these areas. Other preferred housing locations are distributed around major tram and train lines and where rent prices are generally lower. The model and its associated tools are capable of spatialising aggregated statistical datasets, inferring spatial correlations, and providing qualitative and quantitative analysis of relocation dynamics. Such achievements are made possible thanks to the efficient visualisation of our results. The agent-based modelling methodology represents an adequate solution for understanding complex phenomena related to sustainability in urban systems, which can be used as guidance for policy making. Full article

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent’s exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization. Full article

Task offloading and resource allocation is a research hotspot in cloud-edge collaborative computing. Many existing pieces of research adopted single-agent reinforcement learning to solve this problem, which has some defects such as low robustness, large decision space, and ignoring delayed rewards. In view of the above deficiencies, this paper constructs a cloud-edge collaborative computing model, and related task queue, delay, and energy consumption model, and gives joint optimization problem modeling for task offloading and resource allocation with multiple constraints. Then, in order to solve the joint optimization problem, this paper designs a decentralized offloading and scheduling scheme based on “task-oriented” multi-agent reinforcement learning. In this scheme, we present information synchronization protocols and offloading scheduling rules and use edge servers as agents to construct a multi-agent system based on the Actor–Critic framework. In order to solve delayed rewards, this paper models the offloading and scheduling problem as a “task-oriented” Markov decision process. This process abandons the commonly used equidistant time slot model but uses dynamic and parallel slots in the step of task processing time. Finally, an offloading decision algorithm TOMAC-PPO is proposed. The algorithm applies the proximal policy optimization to the multi-agent system and combines the Transformer neural network model to realize the memory and prediction of network state information. Experimental results show that this algorithm has better convergence speed and can effectively reduce the service cost, energy consumption, and task drop rate under high load and high failure rates. For example, the proposed TOMAC-PPO can reduce the average cost by from 19.4% to 66.6% compared to other offloading schemes under the same network load. In addition, the drop rate of some baseline algorithms with 50 users can achieve 62.5% for critical tasks, while the proposed TOMAC-PPO only has 5.5%. Full article