Search Results (117)

The ramifications of climate change, which are projected to lead to increased drought, desertification, and water scarcity, are expected to have a significant impact on the agricultural sector of Türkiye, particularly in the Mediterranean coastal regions. This study presents an extensive evaluation of potential agricultural drought conditions in southwestern Türkiye, using a high-resolution, convection-permitting (0.025°) modeling approach. We employ a single, physically consistent model chain, dynamically downscaling the CMIP6 MPI-ESM-HR Earth System Model with the COSMO-CLM regional climate model at a convection-permitting (CP) resolution (0.025°) under IPCC Shared Socioeconomic Pathways SSP3-7.0, reflecting a high-emission scenario with regional socioeconomic challenges. Southwestern Türkiye, situated at the intersection of the Mediterranean and continental climates, hosts rare climatic and ecological conditions that sustain a highly productive and diverse agricultural system. This region forms the backbone of Türkiye’s agricultural economy but is increasingly vulnerable to climate variability and fluctuations that threaten its agricultural stability and resilience. Our study employs a novel approach that utilizes multivariate assessment of agricultural drought in the Mediterranean Region by integrating precipitation, soil moisture, and temperature variables from 2.5 km resolution climate simulations. Agricultural drought conditions were evaluated using the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSI), and the Standardized Temperature Index (STI), derived by normalizing respective climate variables from climate simulations spanning from 1995 to 2014 for the historical period, from 2040 to 2049 and from 2070 to 2079 for future projections. CP climate simulations (CPCSs) exhibit a modest warm and dry bias during all seasons but slightly wetter conditions during summer when compared with station observations. Correlations between indices indicate that soil moisture variations in the future will become more sensitive to changes in temperature rather than precipitation. Results from this specific model chain reveal that the probability of compound events where precipitation and soil moisture deficits coincide with anomalously high temperatures will rise for all threshold levels under the SSP3-7.0 scenario towards the end of the century. For the most severe conditions (|Z| > 1.2), the compound likelihood increases to about 3%, highlighting the enhanced occurrence of rare events in a changing climate. These findings, conditional on the model and scenario used, provide a high-resolution, physically grounded perspective on the potential intensification of agricultural drought regimes. Full article

Classical binomial interval methods often exhibit poor performance when applied to extreme conditions, such as rare-event scenarios or small-sample estimations. Recent frequentist and Bayesian approaches have improved coverage in small samples and rare events. However, they typically rely on fixed error margins that do not scale with the magnitude of the proportion. This distorts uncertainty quantification at the extremes. As an alternative method to reduce these boundary distortions, we propose a novel hybrid approach. It blends Bayesian, frequentist, and approximation-based techniques to estimate robust and adaptive intervals. The variance incorporates sampling variability, Wilson score margin of error, a tuned credible level, and a gamma regularization term that is inversely proportional to sample size. Extensive simulation studies and real-data applications demonstrate that the proposed method consistently achieves better coverage proportions at all sample sizes and proportions. It provides more conservative interval widths below a sample size of 50 and competitively narrower widths from moderate to large sample sizes, especially beyond 50, compared to the Jeffreys’ and Wilson score intervals. Geometric analysis of the tuning curves demonstrates how the blended method adaptively tunes credible levels across binomial extremes. It starts at higher values for small samples and gradually flattens into near-linear, symmetric trajectories as sample size increases. This ensures robust coverage and balanced sensitivity. Our method offers a theoretically grounded, computationally efficient, and practically robust estimation of rare-event intervals. These intervals have applications in safety-critical reliability, epidemiology, and early-phase clinical trials. Full article

Reliability evaluation of power distribution systems is computationally intensive, as standard Monte Carlo simulations require extensive sampling to accurately estimate rare event-based indices like SAIDI and SAIFI. This paper introduces a multi-objective cross-entropy approach for reliability evaluation of power distribution systems, aiming to accelerate reliability evaluation by optimizing importance sampling reference parameters. The multi-objective approach aims to optimize a set of objective functions related to systemic and load point reliability indices. A deduction of an analytical solution for the optimization of reference parameters of the cross-entropy method is developed, taking into account the standard hypotheses used in reliability assessments. The proposed method has been validated on a real 181-node Brazilian distribution feeder. Results show that the proposed approach can accelerate the convergence of estimates for reliability indices in comparison with the crude Monte Carlo approach and the single-objective CE method. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article

This study focuses on the impact of wastewater discharges from the Besòs treatment plant on the coastal water quality of Barcelona, particularly under adverse weather conditions. A simplified mathematical model was developed to predict, in real time, the concentration of bacterial indicators (Enterococci and E. coli) along nearby beaches. This model aims to quickly detect contamination events and trigger alerts to evacuate swimming areas before water quality tests are completed. The simulator uses meteorological data—such as wind direction and speed, rainfall intensity, and solar irradiance, among others—to anticipate pollution levels without requiring immediate water sampling. The model was tested against real-world scenarios and validated with historical meteorological and bacteriological data collected over six years. The results show that bacterial pollution occurs mainly during intense rainfall events combined with specific wind conditions, particularly when winds blow from the southeast (SE) or east–southeast (ESE) at moderate to high speeds. These wind patterns carry under-treated wastewater toward the coast. Conversely, winds from the north or northwest tend to disperse the contaminants offshore, posing little to no risk to swimmers. This study confirms that pollution events are relatively rare—about two per year—but pose significant health risks when they do occur. The simulator proved reliable, accurately predicting contamination episodes without producing false alarms. Minor variables such as water temperature or suspended solids showed limited influence, with wind and sunlight being the most critical factors. The model’s rapid response capability allows public authorities to take swift action, significantly reducing the risk to beachgoers. This system enhances current water quality monitoring by offering a predictive, cost-effective, and preventive tool for beach management in urban coastal environments. Full article

Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. Full article

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis—Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

Rare (only 10) observations, made in the southern topside ionosphere during 2015–2016, demonstrate the amplification of westward subauroral polarization streams (SAPS) up to 3000 m/s near the Harang region. The observed amplified SAPS flows were streaming antisunward after midnight and sunward at midnight, where the dusk convection cell intruded dawnward. One SAPS event illustrates the elevated electron temperature (Te; ~5500 K) and the stable auroral red arc developed over Rothera. Three inner-magnetosphere SAPS events depict the Harang region’s earthward edge within the plasmasheet’s earthward edge, where the outward SAPS electric (E) field (within the downward Region 2 currents) and inward convection E field (within the upward Region 2 currents) converged. Under isotropic or weak anisotropic conditions, the hot zone was fueled by the interaction of auroral kilometric radiation waves and electron diamagnetic currents. Generated for the conjugate topside ionosphere, the SAMI3 simulations reproduced the westward SAPS flow in the deep electron density trough, where Te became elevated, and the dawnward-intruding westward convection flows. We conclude that the near-midnight westward SAPS flow became amplified because of the favorable conditions created near the Harang region by the convection E field reaching subauroral latitudes and the positive feedback mechanisms in the SAPS channel. Full article

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dimensional space, followed by adaptive clustering to identify events of interest. Simulation results demonstrate the system’s ability to normalize the collection of acoustic events across varying abundance levels, with rare events retained at rates of 80–85% while frequent sounds are reduced to 3–10% retention. Initial field deployments on caribou, African elephants, and bighorn sheep show promising application across diverse species and ecological contexts. Power consumption analysis indicates the need for additional optimization to achieve multi-month deployments. This technology enables the creation of novel wilderness datasets while addressing the limitations of traditional static acoustic monitoring approaches, offering new possibilities for wildlife research, ecosystem monitoring, and conservation efforts. Full article

This study evaluates the impact of precipitation ensembles on flood hazards in the Ouémé River Basin by coupling the hydrological HBV and hydrodynamic HEC–RAS model. Both models were calibrated and validated to simulate hydrological and hydraulic processes. Meteorological and hydrometric data from 1994 to 2016, along with flood maps and DEM are used. Evapotranspiration data are calculated using Hargreaves–Samani formula. The coupling HBV–HEC–RAS models enabled the generation of ensemble hydrographs, flood maps, flood probability maps and additional statistics in West Africa for the first time, offering a comprehensive understanding of flood dynamics under uncertainty. Ensemble hydrographs and maps obtained enhance decision-making by showing discharge scenarios, spatial flood variability, prediction reliability, and probabilities, supporting targeted flood management and resource planning under uncertainty. The findings underline the need for a comprehensive strategy to mitigate both common and rare flood events while accounting for spatial uncertainties inherent in hydrological and hydraulic modeling. Full article

This study investigates the use of adaptive cluster sampling (ACS) for estimating bark stripping damage in forests, employing the Hansen–Hurwitz (HH) and Horvitz–Thompson (HT) estimators. Through simulations, we analysed the total, summer, and new bark stripping damage with varying grid sizes and sample sizes in eight full-censused stands in Northern Styria/Austria. The results showed that the HT estimator consistently had lower standard errors (SEs) (variability of the sample mean from the true population mean) than the HH estimator. SEs decreased with increasing grid space for new and summer damages, but increased for total damage up to 35 m, then remained stable. Inclusion probabilities (IP) were highest for total damage. ACS showed precision gains, particularly for rare and clustered damages like new damage, but did not achieve the target SE of 10%. Adaptive sampling is most beneficial for monitoring rare and clustered events, though precision remains limited, especially for new damage. The study suggests ACS is suitable for rare damage types (e.g., summer and new bark stripping wounds) but requires further refinement to meet operational precision targets. Future work should focus on integrating adaptive designs with practical field methods, such as fixed-radius plots and refined damage criteria. Full article

The rupture of the contact wire (CW) of a railway overhead contact line (OCL or catenary) is expected to be a rare event. However, when it occurs, and a pantograph transits under the already broken section of the CW, this can have catastrophic consequences for the pantograph which in turn can cause a further extension of the damaged portion on the OCL with a consequent disruption in the service and cause there to be a long time before the operating condition can be restored. Therefore, the prevention of such events through effective catenary monitoring is gaining significant attention. The purpose of this work is to investigate the feasibility of a monitoring system that can be installed at each end of an OCL section which is able to detect the occurrence of a broken CW event, sending an alert to the management traffic system, so as to stop the train traffic before the damaged catenary is reached by other trains. A nonlinear dynamic analysis is employed to model the OCL’s response following a simulated CW rupture and identify a set of variables that can be measured at the line’s extremities related to the occurrence of breakage in the CW. Several locations of the rupture of a CW section along the line are simulated to investigate the influence on the time pattern of the measured variables and consequently on the extraction of a signature. Finally, a proposed measurement setup is presented, combining accelerometers and displacement transducers, instead of the direct measurement of the axial load of the OCL conductors. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems primarily consist of pre-cooling air handling units (PAUs), air handling units (AHUs), and air ducts. Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. Therefore, to address the various challenges in temperature and humidity control, a more sophisticated control approach is required to make high-level decisions and coordinate the operation of HVAC components. This paper utilizes Large Language Models (LLMs) as a core component for interpreting complex operational scenarios and making high-level decisions. A chain-of-thought mechanism is designed to enable comprehensive reasoning through LLMs, and an algorithm is developed to convert LLM decisions into executable HVAC control commands. This approach leverages adaptive guidance through parameter matrices to seamlessly integrate LLMs with underlying MPC controllers. Simulated experimental results demonstrate that the improved control strategy, optimized through LLM-enhanced Model Predictive Control (MPC), significantly enhances the energy efficiency and stability of HVAC system control. During the summer conditions, energy consumption is reduced by 33.3% compared to the on–off control strategy and by 6.7% relative to the conventional low-level MPC strategy. Additionally, during the system startup phase, energy consumption is slightly reduced by approximately 17.1% compared to the on–off control strategy. Moreover, the proposed method achieves superior temperature stability, with the mean squared error (MSE) reduced by approximately 35% compared to MPC and by 45% relative to on–off control. Full article

The development of satellite communications has received considerable attention in recent years. Early satellite communications were dominated by voice and low-speed data services, but now they must support high-speed multimedia services. Low Earth Orbit (LEO) satellites, because of their lower altitude orbits, have much smaller transmission loss and delay than Geostationary Earth Orbit (GEO) satellites, and they are an important part of the future realization of high-bandwidth and low-latency multimedia services. Among them, the on-demand streaming service has a large number of users in terrestrial communication and is also an important service component that will be in satellite communication environments in the future. However, LEO satellites face many challenges in handover and accessing due to their fast moving speed. Although many handover and access schemes for LEO satellites have been proposed and evaluated in existing studies, most of them stay at the level of quality of service (QoS), and few of them have been studied at the level of quality of experience (QoE). These studies also rarely consider the performance of multimedia services, including streaming services, in satellite communication environments, and there is no relevant simulation system to evaluate and examine them. Therefore, this paper builds a simulation system for streaming services in LEO satellite communication environments in order to simulate the initial buffering, rebuffering, and idle state of the users during service. Then, access and handover schemes for the QoE level of streaming service are proposed. Finally, our proposed scheme is evaluated based on this simulation system. From the simulation results, the simulation system proposed in this paper can successfully realize the various functions of users in on-demand streaming services and record the initial buffering and rebuffering events of users. And the streaming QoE-based access and handover scheme proposed in this paper can perform well in satellites, which operate within a resource-constrained environment. Full article