Search Results (1,179)

In dense urban environments and large-scale events, Internet infrastructure often becomes overloaded due to high communication demand. Many of these communications are local and short-lived, exchanged between users in close proximity but still relying on global infrastructure, leading to unnecessary network stress. In this context, delay-tolerant networks (DTNs) offer an alternative by enabling device-to-device (D2D) communication without requiring constant connectivity. However, DTNs face significant challenges in routing due to unpredictable node mobility and intermittent contacts, making reliable delivery difficult. Considering these challenges, this paper presents a hybrid Beyond 5G (B5G) DTN architecture to provide private context-aware routing in dense scenarios. In this proposal, dynamic contextual notifications are shared among relevant local nodes, combining federated learning (FL) and edge artificial intelligence (AI) to estimate the optimal relay paths based on variables such as mobility patterns and contact history. To keep the local FL models updated with the evolving context, edge nodes, integrated as part of the B5G architecture, act as coordinating entities for model aggregation and redistribution. The proposed architecture has been implemented and evaluated in simulation testbeds, studying its performance and sensibility to the node density in a realistic scenario. In high-density scenarios, the architecture outperforms state-of-the-art routing schemes, achieving an average delivery probability of 77%, with limited latency and overhead, demonstrating relevant technical viability. Full article

Continuous and near-real-time monitoring of greenhouse gases (GHGs) is critical for achieving Net Zero emissions, ensuring early detection, compliance, accountability, and adaptive management. To this end, there is an increasing need to monitor GHGs at higher temporal resolutions, greater spatial resolutions, and larger coverage scales. However, spatial resolution and coverage remain significant challenges due to limited sensor network coverage and power sources for sensor nodes, even in urban areas. LoRaWAN, a cost-effective solution that provides long-range and high-penetration wireless connectivity with a low energy consumption, is an ideal choice for this application. Despite its promise, LoRaWAN faces several challenges, including a low data rate, low packet transmission rate, and low packet delivery success ratio, especially when the node density or environment variability is high. This paper presents a simulation-based analysis of a large-scale urban LoRaWAN sensor network operating in the CN470–510 MHz band, which is the only frequency band officially designated for low-power wide-area (LPWA) technologies such as LoRaWAN in China. This study investigates how the node density, sensor measurement update rate (i.e., update interval), and sensor measurement payload size affect two primary performance metrics: the sensor update delivery ratio (DR) and the radio frequency (RF) energy consumption (RFEC) per successful update. The performances of several enhanced adaptive data transmission algorithms in comparison to the conventional ADR+ algorithms are also analysed. The results indicate that both DR and RFEC are significantly influenced by the node density, sensor update rate, and payload size, with the effects being particularly significant under high-node-density and high-update-rate conditions. The analysis further reveals that the ADR-NODE-KAMA algorithm consistently achieves the best performance across most scenarios, providing up to a 2% improvement in DR and a reduction of 10–15 mJ in RFEC per successful sensor measurement update. Additionally, the sensor measurement payload size is shown to have a substantial impact on network performance, with each added sensor measurement contributing to a DR reduction of up to 2.24% and an increase in RFEC of approximately 80 mJ. LoRaWAN network operators can gain practical insights from these findings to optimize the performance and efficiency of large-scale GHG monitoring deployments. Full article

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article

Geological data of the region indicate that the Canary Islands have not been connected to the mainland before. However, fossil evidence suggests some kind of faunal exchange with Africa during the late Neogene. After extensive field work during past years, a re-evaluation of the fossil remains of the first terrestrial vertebrates that settled and thrived on the Canary Islands is presented, with special attention to the long-debated identity of birds that laid large-sized eggs, reported some decades ago on Lanzarote Island. The age of the eggshell-bearing deposits has been recently updated as Early Pliocene (ca. 4 Ma). The dispersal mode of these terrestrial birds to reach the island was an unsolvable challenge in previous studies because the regional geography of the sea bottom was neglected, as well as the chronological succession of events in the formation of the Canary Eastern Ridge, which increased attention to a unique case of arrival of ratites on an island never before united with the mainland. The few animals found in northern Lanzarote (ratites, snakes, turtles, terrestrial snails and bite marks on eggshells pointing to a jagged and unknown large predator) probably made the sea crossing from the mainland in different ways. Two scenarios are contemplated. In both, the circumstances facilitating the faunal transit from Africa to the Canaries ceased after the early Pliocene, around 4 Ma, since these animals have never managed to cross the Canary Channel again. Full article

In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it constructs virtual replicas of vehicles, roads, and infrastructure, enabling in-depth analysis and optimal decision-making for traffic scenarios. In vehicular networks, existing information caching and transmission systems suffer from low real-time information update and serious transmission delay accumulation due to outdated storage mechanism and insufficient interference coordination, thus leading to a high age of information (AoI). In response to this issue, we focus on pairwise road side unit (RSU) collaboration and propose a digital twin-integrated framework to jointly optimize information caching and communication power allocation. We model the tradeoff between information freshness and resource utilization to formulate an AoI-minimization problem with energy consumption and communication rate constraints, which is solved through deep reinforcement learning within digital twin systems. Simulation results show that our approach reduces the AoI by more than 12 percent compared with baseline methods, validating its effectiveness in balancing information freshness and communication efficiency. Full article

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article

Accurate wave forecasting under typhoon conditions is essential for coastal safety in the Pearl River Estuary. This study explores the use of Random Forest (RF) and Long Short-Term Memory (LSTM) models to predict significant wave heights, using SWAN-simulated data from 87 historical typhoon events. Ten representative typhoons were reserved for independent testing. Results show that the LSTM model outperforms RF in 3 h forecasts, achieving a lower mean RMSE and higher R², particularly in capturing wave peaks under highly dynamic conditions. For 6 h forecasts, both models exhibit decreased accuracy, with RF performing slightly better in stable scenarios, while LSTM remains more responsive in complex wave evolution. Generalization tests at three nearby stations demonstrate that both models, especially LSTM, retain strong predictive skill beyond the training location. These findings highlight the potential of combining numerical wave models with machine learning for short-term, data-driven wave forecasting in typhoon-prone and observation-sparse regions. The study also points to future improvements through integration of wind field predictors, model updating strategies, and ensemble meteorological data. Full article

This paper proposes an underwater-acoustic class-incremental few-shot learning (UACIL) method for streaming data processing in practical underwater-acoustic target recognition scenarios. The core objective is to expand classification capabilities for new classes while mitigating catastrophic forgetting of existing knowledge. UACIL’s contributions encompass three key components: First, to enhance feature discriminability and generalization, an enhanced frequency-domain attention module is introduced to capture both spatial and temporal variation features. Second, it introduces a prototype classification mechanism with two operating modes corresponding to the base-training phase and the incremental training phase. In the base phase, sufficient pre-training is performed on the feature extraction network and the classification heads of inherent categories. In the incremental phase, for streaming data processing, only the classification heads of new categories are expanded and updated, while the parameters of the feature extractor remain stable through prototype classification. Third, a joint optimization strategy using multiple loss functions is designed to refine feature distribution. This method enables rapid deployment without complex cross-domain retraining when handling new data classes, effectively addressing overfitting and catastrophic forgetting in hydroacoustic signal classification. Experimental results with public datasets validate its superior incremental learning performance. The proposed method achieves 92.89% base recognition accuracy and maintains 68.44% overall accuracy after six increments. Compared with baseline methods, it improves base accuracy by 11.14% and reduces the incremental performance-dropping rate by 50.09%. These results demonstrate that UACIL enhances recognition accuracy while alleviating catastrophic forgetting, confirming its feasibility for practical applications. Full article

The Multi-Mode Model (MMM) is a physics-based anomalous transport model integrated into TRANSP for predicting electron and ion thermal transport, electron and impurity particle transport, and toroidal and poloidal momentum transport. While MMM provides valuable predictive capabilities, its computational cost, although manageable for standard simulations, is too high for real-time control applications. MMMnet, a neural network-based surrogate model, is developed to address this challenge by significantly reducing computation time while maintaining high accuracy. Trained on TRANSP simulations of DIII-D discharges, MMMnet incorporates an updated version of MMM (9.0.10) with enhanced physics, including isotopic effects, plasma shaping via effective magnetic shear, unified correlation lengths for ion-scale modes, and a new physics-based model for the electromagnetic electron temperature gradient mode. A key advancement is MMMnet’s ability to predict all six transport coefficients, providing a comprehensive representation of plasma transport dynamics. MMMnet achieves a two-order-of-magnitude speed improvement while maintaining strong correlation with MMM diffusivities, making it well-suited for real-time tokamak control and scenario optimization. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

The multi-strategy optimized dream optimization algorithm (MSDOA) is proposed to address the challenges of inadequate search capability, slow convergence, and susceptibility to local optima in intelligent optimization algorithms applied to UAV three-dimensional path planning, aiming to enhance the global search efficiency and accuracy of UAV path planning algorithms in 3D environments. First, the algorithm utilizes Bernoulli chaotic mapping for population initialization to widen individual search ranges and enhance population diversity. Subsequently, an adaptive perturbation mechanism is incorporated during the exploration phase along with a lens imaging reverse learning strategy to update the population, thereby improving the exploration ability and accelerating convergence while mitigating premature convergence. Lastly, an Adaptive Individual-level Mixed Strategy (AIMS) is developed to conduct a more flexible search process and enhance the algorithm’s global search capability. The performance of the algorithm is evaluated through simulation experiments using the CEC2017 benchmark test functions. The results indicate that the proposed algorithm achieves superior optimization accuracy, faster convergence speed, and enhanced robustness compared to other swarm intelligence algorithms. Specifically, MSDOA ranks first on 28 out of 29 benchmark functions in the CEC2017 test suite, demonstrating its outstanding global search capability and conver-gence performance. Furthermore, UAV path planning simulation experiments conducted across multiple scenario models show that MSDOA exhibits stronger adaptability to complex three-dimensional environments. In the most challenging scenario, compared to the standard DOA, MSDOA reduces the best cost function fitness by 9% and decreases the average cost function fitness by 12%, thereby generating more efficient, smoother, and higher-quality flight paths. Full article

Centipedes (Chilopoda, Myriapoda) are crucial soil predators, yet their vulnerability to climate and land use change remains unexplored. We assess the impact of these drivers on Greek centipedes, identify current and future biodiversity hotspots, and evaluate the effectiveness of the Natura 2000 Network of protected areas for their conservation. We used an updated species occurrence database of Greek centipedes, derived from literature reviews and museum collections, and evaluated database completeness and geographic sampling biases. Species Distribution Models were employed to predict future distribution shifts under climate and land use change scenarios. Biodiversity hotspots were identified based on species richness (SR) and corrected-weighted endemism (CWE) metrics. We overlapped SR and CWE metrics against the Natura 2000 Network to assess its effectiveness. We found that sampling effort is highly heterogeneous across Greece. All species are projected to experience range contractions, particularly in the 2080s, with variation across scenarios and taxa. Current biodiversity hotspots are concentrated in the south Aegean islands and mainland mountain ranges, where areas of persistent high biodiversity are also projected to occur. The Natura 2000 Network currently covers 52% of SR and 44% of CWE hotspots, with projected decreases in SR coverage but increases in CWE coverage. Our work highlights the vulnerability of Greek centipedes to climate and land use change and reveals conservation shortfalls within protected areas. We identify priority areas for future field surveys, based on sampling bias and survey completeness assessments, and highlight the need for further research into mechanisms driving centipede responses to global change. Full article

Background: The phenomenon of pink teeth represents a notable observation in forensic science, although its interpretation remains complex and not directly attributable to a specific cause of death. Methods: This systematic review provides an updated and comprehensive overview of the morphological and histological mechanisms associated with this finding, with a focus on hemoglobin diffusion and pigment accumulation during putrefaction rather than on detailed biochemical pathways. Results: Environmental conditions, especially high humidity and moderate temperatures, are identified as key facilitators. The synthesis of the available evidence, including case reports, observational series, and experimental studies, confirms that pink discoloration is primarily linked to postmortem hemoglobin diffusion following erythrocyte breakdown and release of heme groups into dentinal structures. This process occurs more frequently under conditions that preserve hemoglobin and facilitate its migration into dental tissues. Importantly, pink teeth have been documented across a wide spectrum of postmortem scenarios, such as hanging, drowning, carbon monoxide poisoning, and prolonged exposure to humid environments, indicating that their presence is neither pathognomonic nor exclusively associated with a specific cause of death. Assessment methods include semi-quantitative visual scoring systems (e.g., SPTC and SPTR), spectrophotometric assays, and histochemical analyses for hemoglobin derivatives. Recent advances in digital forensics, particularly micro-computed tomography and artificial intelligence–based segmentation, may further support the objective evaluation of chromatic dental changes. Conclusions: This review underscores the need for standardized approaches to the identification, classification, and analysis, both qualitative and quantitative, of pink teeth in medico-legal practice. Although not diagnostic in isolation, their systematic study enhances our understanding of decomposition processes and contributes supplementary interpretive data in forensic investigations. Full article

Rapid urban growth poses distinct energy and environmental challenges in various regions of the world. This study evaluated the technical and economic feasibility of a grid-connected photovoltaic system in Santo Domingo Tehuantepec, Oaxaca, Mexico, using Homer Pro software, version 3.14.2, to simulate realistic scenarios. The analysis incorporated local climate data, residential load profiles, and updated economic parameters for 2024. System optimization resulted in an installed capacity of 173 kW of solar panels and 113 kW of inverters, yielding a levelized cost of energy (LCOE) of MXN 1.43/kWh, a return on investment (ROI) of 5.3%, an internal rate of return (IRR) of 8%, and a simple payback period of 10 years. The projected annual energy output was 281,175 kWh, covering 36% of the local energy demand. These results highlight the potential for integrating renewable energy into urban contexts, offering significant economic and environmental benefits. The integration of public policy with urban planning can enhance energy resilience and sustainability in intermediate cities. This study also supports the application of tools such as Homer Pro in designing energy solutions tailored to local conditions and contributes to a fair and decentralized energy transition. Full article