Search Results (5,882)

In order to consider the impact of multiple uncertainties on the interaction between the distribution network operator (DNO) and photovoltaic powered charging stations (PVCSs), this paper proposes a regulation strategy for a distribution network with a PVCS based on bi-level stochastic optimization. First, the interaction framework between the DNO and PVCS is established to address the energy management and trading problems of different subjects in the system. Second, considering the uncertainties in the electricity price and PV output, a bi-level stochastic model is constructed with the DNO and PVCS targeting their respective interests. Furthermore, the conditional value-at-risk (CVaR) is introduced to measure the relationship between the DNO’s operational strategy and the uncertain risks. Next, the Karush–Kuhn–Tucker (KKT) conditions and duality theorem are utilized to tackle the challenging bi-level problem, resulting in a mixed-integer second-order cone programming (MISCOP) model. Finally, the effectiveness of the proposed regulation strategy is validated on the modified IEEE 33-bus system. Full article

This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km² of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m²) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than >60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km² or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO₂. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services. Full article

The objective of this study is to evaluate the ability of inverse techniques to estimate the resistance and the capacity of a highly insulated multilayer wall under real weather conditions. The wall is equipped with temperature sensors inside and on its inner and outer surfaces, and the boundary conditions have been measured over a 14-day period. Uncertainties on various parameters of the model are evaluated, including internal and external convective heat transfer coefficients (±20% and ±7 W.m^-².K⁻¹ respectively), external long-wave heat transfer coefficient (±0.15 W.m⁻².K⁻¹) and solar absorption coefficient (±0.06). A sensitivity analysis demonstrated the high correlation with some parameters defining the thermal performance of the walls (thermal resistance or capacity). A solution is proposed to limit the number of identified parameters, while allowing the identification of the thermal resistance and the thermal capacity of the walls. There are two cases: either the weather conditions are accurately measured (temperature, short- and long-wave radiation) and the thermal characteristics can be assessed, or intrusive sensors are installed, and the thermal characteristics can be evaluated more accurately. Full article

Considerable uncertainty exists regarding the overall effects of future climate change on forests in arid mountains, and the elevational range of drought-induced tree growth decline remains unclear. Tianshan is the largest mountain in arid regions globally. Here, we analyzed tree ring data of pure stands of Schrenk spruce (Picea schrenkiana Fisch. et Mey.) in the Jiangbulake region in the eastern Tianshan Mountains along an elevational gradient (1800–2600 m a.s.l.). The radial growth of P. schrenkiana trees declined in three of the nine sample strips (1800–2100 m a.s.l.) over the last two decades. P. schrenkiana growth response (measured by the tree ring width index, RWI) to temperature significantly changed at an elevational “inflection point” at 2100–2200 m. RWI was significantly negatively correlated with temperature at low elevations, whereas the opposite was observed at high elevations. Precipitation and minimum temperatures in winter and spring and mean temperatures in spring and summer were the main drivers of P. schrenkiana growth, with the effect of maximum temperatures on tree growth concentrated in the spring. In addition to climate warming in the study area since the 1970s, tree growth (as measured by the basal area increment, BAI) at elevations below 2200 m initially increased and then decreased. Tree growth at higher elevations continues to increase. Since 2000, the average RWI at high elevations exceeded that at low elevations. The average BAI values at high and low elevations have gradually approached each other in recent decades, although lower elevations exhibited higher values in the past. Full article

In this study, we investigate the volatility spillover effects across uncertainty indices (Infectious Disease Equity Market Volatility Tracker (IDEMV) and Geopolitical Risk Index (GPR)), carbon emissions, crude oil, natural gas, and green assets (green bonds and green stock) under extreme market conditions based on the quantile connectedness approach. The empirical findings reveal that the total and directional connectedness across green assets and other variables in extreme market conditions is much higher than that in the median, and there is obvious asymmetry in the connectedness measured at the extreme lower and upper quantiles. Our findings suggest that the uncertainty caused by COVID-19 has a more significant impact on green assets than the uncertainty related to the Russia–Ukraine war under normal and extreme market conditions. Furthermore, we discover that the uncertainty indices are more important in predicting green asset volatility under extreme market conditions than they are in the normal market. Finally, we observe that the dynamic total spillover effects in the extreme quantiles are significantly higher than those in the median. Full article

Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple colocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the ~2 h preceding an observation. Full article

During the loading process, significant external position disturbances occur in the electro–hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. The various states of the servo valve and the pressures within the hydraulic cylinder chambers are then examined. Building on this foundation, the paper proposes a nonlinear multi-mode variable structure independent load port electro–hydraulic load simulation system that is tailored for specific loading conditions. Secondly, in light of the significant motion disturbances present, this paper proposes an integral sliding mode active disturbance rejection composite control strategy that is based on fixed-time convergence. Based on the structure of the active disturbance rejection control framework, the fixed-time integral sliding mode and active disturbance rejection control algorithms are integrated. An extended state observer is designed to accurately estimate the lumped disturbance, effectively compensating for it to achieve precise loading of the independent load port electro–hydraulic load simulation system. The stability of the designed controller is also demonstrated. The results of the simulation research indicate that when the command input is a step signal, the pressure control accuracy under the composite control strategy is 99.94%, 99.86%, and 99.76% for disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz, respectively. Conversely, when the command input is a sinusoidal signal, the pressure control accuracy remains high, measuring 99.94%, 99.8%, and 99.6% under the same disturbance frequencies. Furthermore, the simulation demonstrates that the influence of sensor random noise on the system remains within acceptable limits, highlighting the effective filtering capabilities of the extended state observer. This research establishes a solid foundation for the collaborative control of load ports and the engineering application of the independent load port electro–hydraulic load simulation system. Full article

This study investigates the impact of rising temperatures on residential water use (RWU) in Seoul from 2015 to 2021, addressing the challenges of urban water sustainability under climate change. Using advanced models—convolutional neural networks (CNNs), long short-term memory (LSTM) Networks, eXtreme Gradient Boosting (XGBoost), and Bayesian Neural Networks (BNNs)—we examined RWU prediction accuracy and incorporated a method to quantify prediction uncertainties. As a result, the BNN model emerged as a robust alternative, demonstrating competitive predictive accuracy and the capability to account for uncertainties in predictions. Recent studies highlight a strong correlation between rising temperatures and increased RWU, especially during summer, with tropical nights (with temperatures above 25 °C) becoming more common; Seoul experienced a record 26 consecutive tropical nights in July 2024, underscoring a trend toward higher RWU. To capture these dynamics, we employed Shared Socioeconomic Pathway (SSP) scenarios and downscaled the KACE-1-0-G Global Climate Model (GCM) for Seoul, projecting a progressive increase in RWU: 0.49% in the F1 period (2011–2040), 1.53% in F2 (2041–2070), and 2.95% in F3 (2071–2100), with significant rises in maximum RWU across these intervals. Our findings highlight an urgent need for proactive measures to secure water resources in response to the anticipated increase in urban water demand driven by climate change. Full article

²²Ne(α, γ)²⁶Mg is pivotal in the understanding of several open astrophysical questions, as the nucleosynthesis beyond Fe through the s-process, but its stellar reaction rate is still subject to large uncertainties. These mainly arise from its extremely low rate in the Gamow energy region, whose measurement is hampered by the unavoidable presence of the cosmic ray background noise. A possibility to overcome this issue is to perform the measurement in a quasi background-free environment, such as that offered by the underground Bellotti Ion Beam Facility at LNGS. This is the key idea of EASγ experiment. In this study, the signal from the de-excitation of the compound nucleus ²⁶Mg has been simulated and its detection has been investigated both on surface and deep-underground laboratories. The simulation results show the enhancement in sensitivity achieved by performing the measurement deep underground and with an additional shielding, yielding to unprecedented sensitivity. Full article

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization; however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −1024 Hu~−107 Hu, −1024 Hu~−103 Hu, and −1024 Hu~−160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −103 Hu~−50 Hu, −100 Hu~−38 Hu, −153 Hu~−51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from −8 Hu~18 Hu, −8 Hu~13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu~3071 Hu, 20 Hu~3071 Hu, and the brine CT values of the Arctic sea ice ranged from −6 Hu~3071 Hu. The errors between the three-dimensional ice model divided by threshold ranges and measured sediment content were less than 0.003 g/cm³, which verified the high accuracy of the established microscopic model. It provided a scientific basis for ice engineering, ice remote sensing, and ice disaster prevention. Full article

Since the 1990s, Petri nets have been used in systems biology for quantitative modeling. Despite the increasing number of models developed during this period, doubts remain about their biological relevance. Although biological systems predominantly exhibit intracellular or cellular structures, the models rely largely on deterministic predictions, failing to capture the inherent randomness and uncertainties of such systems. The question arises whether these models accurately describe the dynamic behavior of biological systems. This paper introduces a methodology for selecting the appropriate modeling paradigms in systems biology. Initially, we construct a Petri net model and perform deterministic, stochastic, and fuzzy stochastic simulations. Then we perform various statistical tests to measure the discrepancies between the simulation results. Based on scale-density analysis, we determine the modeling approach that best approximates the biological system. Finally, we compare the results of the statistical tests and the scale-density analysis to identify the optimal modeling approach. We applied the proposed methodology to the synthesis of spinal motor neuron protein from the spinal motor neuron-2 gene. Analysis revealed significant discrepancies between the simulation results of different modeling paradigms. Due to the sparse nature of the underlying drug-disease network, we conclude that the fuzzy stochastic paradigm provides the most biologically relevant results. We predict drug combinations that could lead to an up to 149-fold increase in spinal motor neuron protein levels, indicating a promising treatment for the disease. This methodology has the potential for application to other gene-drug-disease networks and broader biological systems. Full article

In the real-world operation of unmanned helicopters, various state constraints, system uncertainties and multisource disturbances pose considerable risks to their safe fight. This paper focuses on anti-disturbance adaptive safety fixed-time control design for the uncertain unmanned helicopter subject to partial state constraints and multiple disturbances. Firstly, a developed safety protection algorithm is integrated with the fixed-time stability theory, which assures the tracking performance and guarantees that the partial states are always constrained within the time-varying safe range. Then, the compensation mechanism is developed to weaken the adverse impact induced by the filter errors. Simultaneously, the influence of the multisource disturbances on the system stability are weakened through the It$\widehat{o}$ differential equation and high-order disturbance observer. Further, the fuzzy logic system is constructed to approximate the system uncertainties caused by the sensor measurement errors and complex aerodynamic characteristics. Stability analysis proves that the controlled unmanned helicopter is semi-globally fixed-time stable in probability, and the state errors converge to a desired region of the origin. Finally, simulations are provided to illustrate the performance of the proposed scheme. Full article

With a view to improve measurements, this paper presents a statistical approach for characterizing the behaviour of roughness parameters based on measurements performed on ground surface topographies (grit #080/#120). A S neox^TM (Sensofar^®, Terrassa, Spain), equipped with three optical instrument modes (Focus Variation (FV), Coherence Scanning Interferometry (CSI), and Confocal Microscopy (CM)), is used according to a specific measurement plan, called Morphomeca Monitoring, including topography representativeness and several time-based measurements. Previously applied to the Sa parameter, the statistical approach based here solely on the Quality Index (QI) has now been extended to a multi-parameter approach. Firstly, the study focuses on detecting and explaining parameter disturbances in raw data by identifying and quantifying outliers of the parameter’s values, as a new first indicator. This allows us to draw parallels between these outliers and the surface topography, providing reflection tracks. Secondly, the statistical approach is applied to highlight disturbed parameters concerning the instrument mode used and the concerned grit level with two other indicators computed from QI, named homogeneity and number of modes. The applied method shows that a cleaning of the data containing the parameters values is necessary to remove outlier values, and a set of roughness parameters could be determined according to the assessment of the indicators. The final aim is to provide a set of parameters which best describe the measurement conditions based on monitoring data, statistical indexes, and surface topographies. It is shown that the parameters Sal, Sz and Sci are the most reliable roughness parameters, unlike Sdq and S5p, which appear as the most unstable parameters. More globally, the volume roughness parameters appear as the most stable, differing from the form parameters. This investigated point of view offers thus a complementary framework for improving measurement processes. In addition, this method aims to provide a global and more generalizable alternative than traditional methods of uncertainty calculation, based on a thorough analysis of multi-parameter and statistical indexes. Full article

The state of charge (SoC) is a critical parameter in lithium-ion batteries and their alternatives. It determines the battery’s remaining energy capacity and influences its performance longevity. Accurate SoC estimation is essential for making informed charging and discharging decisions, mitigating the risks of overcharging or deep discharge, and ensuring safety. Battery management systems rely on SoC estimation, utilising both hardware and software components to maintain safe and efficient battery operation. Existing SoC estimation methods are broadly classified into direct and indirect approaches. Direct methods (e.g., Coulumb counting) rely on current measurements. In contrast, indirect methods (often based on a filter or observer) utilise a model of a battery to incorporate voltage measurements besides the current. While the latter is more accurate, it faces challenges related to sensor drift, computational complexity, and model inaccuracies. The need for more precise and robust SoC estimation without increasing complexity is critical, particularly for real-time applications. Recently, sliding mode observers (SMOs) have gained prominence in this field for their robustness against model uncertainties and external disturbances, offering fast convergence and superior accuracy. Due to increased interest, this review focuses on various SMO approaches for SoC estimation, including first-order, adaptive, high-order, terminal, fractional-order, and advanced SMOs, along with hybrid methods integrating intelligent techniques. By evaluating these methodologies, their strengths, weaknesses, and modelling frameworks in the literature, this paper highlights the ongoing challenges and future directions in SoC estimation research. Unlike common review papers, this work also compares the performance of various existing methods via a comprehensive simulation study in MATLAB 2024b to quantify the difference and guide the users in selecting a suitable version for the applications. Full article

With global leadership as the new norm, discussion about followers’ preferred leader behaviors across cultures is growing in significance. This study proposes a comprehensive predictive model to explore significant preferred leadership factors, drawn from the Leader Behavior Description Questionnaire (LBDQXII), across cultures using automated machine learning (AML). We offer a robust empirical measurement of culturally contingent leader behavior and entrepreneurship behaviors and provide a tool for assessing the cultural predictors of preferred leader behavior to minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Hence, our approach fills a gap in the literature relating to applications of AML in leadership studies and contributes a novel empirical method to better predict leadership preferences. Cultural indicators from Global Leadership and Organizational Behavior (GLOBE) predict the likelihood of the preferred leader behaviors of “Role Assumption”, “Production Emphasis” and “Initiation of Structure”. Hofstede’s Long-Term/Short-Term Orientation is the most critical predictor of preferences for “Tolerance of Uncertainty” and “Initiation of Structure”, whereas the value of restraint impacts the likelihood of preferring leaders with skills in “Integration” and “Consideration”. Significant entrepreneurial values indicators have a significant impact on preferences for leaders focused on “Initiation of Structure”, “Production Emphasis” and “Predictive Accuracy”. Findings also support earlier studies that reveal age and gender significantly impact our preferences for specific leader behaviors. We discuss and offer conclusions to support our findings that foster development of global business managers and practitioners. Full article