Search Results (558)

This paper presents a high-spatial-resolution 3D system to simulate air quality in urban environments by coupling the WRF/Chem regional model with the PALM4U computational fluid dynamics model, together with an emission model using the SUMO microscopic traffic model. The system has been applied to two experiments in the city of Madrid, Spain. The first study quantifies the impact of four high-rise buildings on pollutant dispersion. The second evaluates the effect of changing tree types (broad-leaf vs. needle-leaf) in the Retiro Park on NO₂ and O₃ concentrations. Both simulations adopt a multiscale approach, using detailed 3D urban morphology, traffic flow data and meteorological conditions. In the first experiment, high-rise buildings caused local variations in NO₂ and O₃ of up to 15% and 20%, respectively. In the second experiment, replacing broad-leaf trees with needle-leaf trees led to a mean NO₂ reduction of 1.69% across 90.67% of the study area. This research demonstrates the value of integrated CFD modeling for planning urban mitigation strategies and optimizing air quality in complex urban environments. Full article

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

Traffic signal control at signalized intersections plays a key role in mitigating urban congestion, reducing vehicle emissions, and improving road safety. This study examines three signal control strategies at a four-approach isolated intersection simulated using the Simulation of Urban Mobility (SUMO) microscopic traffic simulator: a baseline fixed-time plan, a Webster-optimized fixed-time plan, and a queue-responsive adaptive controller implemented through the Traffic Control Interface (TraCI). The strategies were evaluated under balanced traffic demand of 600 vehicles per hour per approach over a 3600 s simulation period. Performance was assessed using eight indicators related to mobility, environmental impact, and safety, including average delay, travel time, queue length, network speed, throughput, CO₂ emissions, fuel consumption, and time-to-collision events. The results indicate that the adaptive controller produced the greatest improvements, reducing delay by 14.3%, travel time by 13.6%, CO₂ emissions by 9.3%, fuel consumption by 9.4%, and TTC conflicts by 11.2%, while increasing network speed by 47.9%. The Webster-optimized plan achieved moderate improvements, lowering delay by 4.8% and fuel consumption by 5.0% without additional infrastructure requirements. Overall, the findings suggest that both signal re-timing and queue-responsive adaptive control can enhance intersection performance, with the preferred approach depending on available infrastructure and implementation costs. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

This paper evaluates the feasibility of real-time traffic data analysis on resource-constrained edge devices using a hybrid processing approach. The proposed architecture integrates an LF Edge eKuiper complex event processing engine, deployed within Docker containers, with a native YOLO deep learning model for pedestrian detection. The model processes video frames at 480 × 240 resolution on CPU-only Raspberry Pi devices, achieving up to 30 FPS. The research specifically investigates the performance limits of Raspberry Pi 3 and Raspberry Pi 4 platforms when simultaneously processing high-throughput simulated traffic data from the SUMO simulator (Belgrade scenario, with vehicle distributions and densities adjusted for small, medium, and large traffic volumes) and live video streams, respectively. Experimental results indicate that while both platforms can process up to 2600 messages per second in the settings without image processing, the introduction of a camera sensor reveals a significant hardware bottleneck. The Raspberry Pi 4 maintains robust real-time performance with an average complex event detection latency of less than 500 ms. In contrast, the Raspberry Pi 3 exhibits severe performance degradation, with image processing delays exceeding 8 s, rendering it unsuitable for real-time safety alerts. The findings demonstrate that with appropriate hardware selection, edge-based complex event processing can successfully detect critical safety events, such as sudden vehicle acceleration near pedestrians, without relying on cloud infrastructure. Full article

Educational robotics (ER) and robotics competitions offer an effective context for developing STEM (Science, Technology, Engineering, and Mathematics) competencies, technical skills, and soft skills in engineering degrees. However, current platforms reveal a pedagogical and technical gap: closed commercial systems restrict access to hardware, while open solutions frequently lack a robust and structured architecture for educational settings. Moreover, in both cases, many platforms do not achieve the hardware requirements of the most demanding competitions. To address this issue, the present article presents the design, implementation, and validation of EASYbot, a modular open-hardware robotics platform based on Arduino. The system integrates a microcontroller, a dual USB–battery power supply, high-performance motor power stages, and a plug-and-play interface for input/output and communication peripherals, enabling its use in several competition categories such as mini-sumo or maze robots. The platform is complemented by a state-based programming model and supports libraries that facilitate a learning assessment. The platform provides a scalable ecosystem, enabling students to progress from initial prototyping to optimised hardware control. The validation process encompasses a range of assessments, including technical tests, usability, and adoption evaluation through surveys. Full article

Adaptive traffic signal control (ATSC) remains a critical challenge for urban mobility. In this direction, deep reinforcement learning (DRL) has been widely investigated for ATSC, showing promising improvements in simulated environments. However, a noticeable gap remains between simulation-based results and practical implementations, due to reward formulations that do not address phase instability. Stochastic variations may trigger premature phase changes (“flickers”), affecting signal behavior and potentially limiting deployment in real scenarios. Although several works have examined delay, queues, and decentralized coordination, stability-focused variables remain comparatively less explored, particularly in single yet complex intersections. This study proposes a decentralized DRL model for ATSC with noise injection (ATSC-DRLNI) applied to a single intersection, introducing a stability-oriented reward function that integrates flickers, queue length, and advantage actor-critic (A2C) learning feedback. The model is evaluated in the Simulation of Urban MObility (SUMO) platform and compared against seven baseline methods, using real traffic data from a Mexican city for calibration and validation. Results suggest that penalizing flickers may contribute to more stable phase transitions, while reductions of up to 40% in queue length were observed in heavy-traffic scenarios. These findings indicate that incorporating stability-related variables into reward functions may help in implementing DRL-based ATSC studies. Full article

SUMOylation may be involved in preeclampsia (PE) progression. We aimed to investigate the roles of SUMOylation in PE and its underlying mechanism using animal and cell models. A rat PE model was established by dexamethasone (DEX) treatment from pregnancy day 7.5–17.5. HTR8 and BeWo trophoblasts were used as cell models. Placental RNA-seq analysis coupled with Western blotting showed upregulated SUMOylation in placentas of DEX-treated rats. SUMOylation inhibitor TAK-981 treatment robustly alleviated PE-like features including reduced blood pressure and improved renal injury, fetal weight, spiral artery remodeling and placental blood flow in DEX-treated rats. DEX increased SUMOylation in HTR8 and BeWo cells. TAK-981 reversed DEX-induced dysfunction in HTR8 and BeWo cells, such as migration, invasion and syncytialization. Mass spectrum analysis of SUMO1 immunoprecipitation coupled with functional validation showed that SUMOylated proteins related to oxidative stress caused by DEX were reversed by TAK-981 in cultured trophoblasts. TAK-981 mitigated placental oxidative stress in DEX-treated rats. GEO database combined with Western blotting showed upregulated SUMOylation in human placentas with PE. Our findings indicate that protein SUMOylation is one of the key events in PE, particularly in that associated with high glucocorticoid exposure. Targeting placental SUMOylation might be a promising therapeutic strategy for PE. Full article

Vehicular Ad Hoc Networks (VANETs) are a key enabler of intelligent transportation systems, supporting safety-critical and latency-sensitive applications through vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, rapidly changing network topologies, and heterogeneous wireless conditions pose significant challenges to traditional distributed networking approaches, particularly in terms of quality of service (QoS) stability and handover performance. Software-Defined Networking (SDN) offers promising solutions by enabling centralized control, programmability, and flexible deployment of network functions. This paper presents an SDN-enabled vehicular networking platform designed for realistic, system-level experimentation under dynamic mobility conditions. The proposed platform tightly couples microscopic vehicular mobility generated by SUMO with wireless network emulation in Mininet-WiFi, enabling real-time interaction between vehicle movement, wireless connectivity, and SDN control decisions, where a custom SDN controller implements mobility-aware traffic management and handover handling across roadside units. Extensive experimental scenarios evaluate throughput, packet loss, jitter, and end-to-end latency under varying traffic loads and mobility patterns. Results indicate that SDN-based centralized control improves QoS consistency relative to the unmanaged baseline configuration considered in this study. The proposed platform provides practical insights and a reproducible experimental framework for the design and evaluation of software-defined vehicular networking systems. Full article

Oxidative stress disrupts protein function through direct oxidation and triggers adaptive post-translational modifications. Among these, small ubiquitin-like modifier (SUMO)-ylation mediates fast and reversible remodeling of nuclear and cytoplasmic proteins. Redox regulation of the SUMO E1–E2 conjugation complex and specific SUMO proteases, such as SENP1 and SENP3, allows ROS to influence SUMO turnover and substrate selectivity. This defines SUMOylation as a versatile stress-response module under oxidative stress. In this review, we describe oxidative stress-induced remodeling of SUMO conjugation and deconjugation, with a focus on SUMO2/3 responses that transiently adjust transcription, DNA damage repair, and nuclear body dynamics. We discuss disease-relevant SUMO targets and pathological alterations in SUMO regulation across four major disease categories: neurodegenerative diseases, cardiovascular disease, cancer, and diabetes/metabolic diseases. In addition, we summarize emerging evidence connecting redox-sensitive SUMO remodeling to germ-cell function and reproductive health. Together, these perspectives highlight the dual role of SUMOylation as both a driver of stress adaptation and a tractable target for informing therapeutic strategies targeting the SUMO pathway. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

The conserved protein SetDB1 has been identified in various vertebrate and invertebrate groups. It plays key roles in vital processes such as germline and nervous system development, immune response, tumorigenesis, cell cycle progression, and others. SetDB1 is initially characterized as an enzyme that methylates lysine 9 on histone H3, leading to gene silencing, which is traditionally considered its primary function. However, SetDB1 also targets about a dozen nuclear, cytoplasmic, and membrane proteins as substrates. Moreover, some functions of SetDB1 do not require methyltransferase activity. Due to its SUMO-interacting motif, Tudor domain, and methyl-binding domains, SetDB1 interacts with a wide range of complexes that regulate protein stability and activity, signal transduction pathways, and chromatin spatial organization. In this review, we aim to expand the classical view of SetDB1 as solely a histone methyltransferase and to highlight the broader diversity of its functions. Full article

Vehicular Ad Hoc Network (VANET) traffic–network co-simulation is a foundational methodology for the engineering evaluation of vehicle-to-everything (V2X) protocols and cooperative Intelligent Transportation System (ITS) applications before field deployment. However, with research objectives and experimental conditions varying widely, existing studies still lack a systematic paradigm for parameter configuration and experimental workflows. As a result, researchers often rely on experience-based settings, which can bring high time and computational overhead, long experimental cycles, and limited reproducibility. To address these issues, this paper proposes a simulation cost modeling and configuration optimization methodology for traffic–network co-simulation. By profiling and structurally modeling key overheads, such as initialization and traffic- and network-side execution, we characterize how traffic, network, and control parameters jointly affect total simulation overhead. We formulate a minimum-cost configuration optimization model under constraints of statistical validity and experimental comparability. We further develop a configuration solving mechanism and a structured workflow for simulation experiment configuration to complement empirical tuning with a more systematic approach, thereby improving the reproducibility of simulation studies. The study is grounded in a representative urban road-network co-simulation scenario based on Simulation of Urban MObility (SUMO), Veins, and Objective Modular Network Testbed in C++ (OMNeT++). Simulation results show that the proposed method reduces simulation overhead while keeping conclusions on key performance metrics consistent, thereby providing a more efficient and statistically credible evaluation basis for application-oriented urban VANET studies related to traffic safety, transportation efficiency, and wireless-system performance. Full article

Cells rely heavily on DNA repair networks to survive genomic damage. For repairing double-strand breaks, Non-Homologous End Joining (NHEJ) remains the primary pathway, which is largely controlled by the DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Researchers have long studied how phosphorylation drives this kinase. However, recent data point to an important additional layer of control. Drawing on evidence accumulated over the past two decades, we propose a “Spatiotemporal Logic Circuit” model for DNA-PKcs regulation. In this model, SUMO-associated interactions may help stabilize synaptic assembly, HUWE1-mediated neddylation may facilitate kinase activation at Lys4007, and K48-linked ubiquitination—potentially involving RNF144A—may contribute to the turnover of persistent repair complexes. Importantly, we frame these UBL-mediated events within the broader autophosphorylation-driven conformational cycle of DNA-PKcs, which remains central to NHEJ progression. Additionally, we highlight the structural interface where activation and degradation signals may converge and the extraction barrier posed by the massive DNA-PKcs scaffold. From a translational perspective, we argue that the exceptional size of DNA-PKcs (~470 kDa) and its topological entrapment on DNA render it an unusually challenging PROTAC target—one that may require p97/VCP-assisted extraction before proteolysis can proceed. We also highlight the underappreciated risk that E3 ligase loss-of-function, already documented in BET-PROTAC resistance, may similarly undermine DNA-PKcs degrader strategies. Full article

Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems. Full article