Search Results (55)

Species characterized by undetermined clade affiliations, limited research coverage, and deficient systematic investigation serve as enigmatic entities in plant and animal taxonomy, yet hold critical significance for exploring phylogenetic relationships and evolutionary trajectories. Subgenus Cyathophora (Allium, Amayllidaceae), a small taxon comprising approximately five species distributed in the Qinghai–Tibet Plateau (QTP) and adjacent regions might contain an enigmatic species that has long remained unexplored. In this study, we collected data on species from subgenus Cyathophora and its close relatives in subgenus Rhizirideum, as well as the enigmatic species Allium siphonanthum. Combining phylogenomic datasets and morphological evidence, we investigated species relationships and the underlying mechanism of phylogenetic discordance. A total of 1662 single-copy genes (SCGs) and 150 plastid loci were filtered and used for phylogenetic analyses based on concatenated and coalescent-based methods. Furthermore, to systematically evaluate phylogenetic discordance and decipher its underlying drivers, we implemented integrative analyses using multiple approaches, such as coalescent simulation, Quartet Sampling (QS), and MSCquartets. Our phylogenetic analyses robustly resolve A. siphonanthum as a member of subg. Cyathophora, forming a sister clade with A. spicatum. This relationship was further corroborated by their shared morphological characteristics. Despite the robust phylogenies inferred, extensive phylogenetic conflicts were detected not only among gene trees but also between SCGs and plastid-derived species trees. These significant phylogenetic incongruences in subg. Cyathophora predominantly stem from incomplete lineage sorting (ILS) and reticulate evolutionary processes, with historical hybridization events likely correlated with the past orogenic dynamics and paleoclimatic oscillations in the QTP and adjacent regions. Our findings not only provide new insights into the phylogeny of subg. Cyathophora but also significantly enhance our understanding of the evolution of species in this subgenus. Full article

Context: Planting forests is an important strategy to combat biodiversity loss and ecosystem service degradation, but its effects on biodiversity and ecosystem services remain uncertain. Objectives: This study aimed to investigate the restoration of plants along successional and environmental gradients in planted forests by examining how understory plant diversity (species richness, composition, functional diversity), functional diversity—the range of species’ traits influencing ecosystem functions and services and their environmental drivers—evolve in temperate plantations over time. Methods: We examined a total of 36 plots with different stand ages in Chongli District, China, and compared the differences in species richness, biodiversity, composition, and functional diversity across different successional stages and over time. We also analyzed the response mechanisms of species richness and functional diversity to environmental factors at both the local and landscape scales. Results and Discussion: Our results showed species diversity, species richness, and functional diversity tended to increase with time in most plots and stabilized after 45 years. Although species richness was lower in mature plots (>100 years), functional diversity was higher, and species composition was significantly differentiated. This trade-off reflects environmental filtering selecting for competitively dominant species with distinct functional traits, while continuous species turnover prevents compositional convergence. The increase in functional diversity was not directly related to the rise in species richness, but it depended on the relative dominance of several species with different functional characteristics in the ecosystem. Simulation analysis confirmed this pattern aligns with a Simpson’s index-driven trait complementarity mechanism. At the local scale, stand age was the most significant positive factor influencing species richness and functional diversity. Soil total nitrogen and organic matter only negatively affected species richness in interactions. At the landscape scale, landscape heterogeneity plays an important role in restoring functional diversity. Historical afforestation since the 1950s restricted comparisons to secondary forests, lacking primary forest baselines. Conclusions: The results suggest that the effects of the successional stage and multiscale environmental factors should be comprehensively considered in the restoration strategy of restored forests. Full article

With the rapid development of smart grid technologies, communication networks have become the core infrastructure supporting control and energy optimization in microgrids. However, the excessive reliance of microgrid control on communication networks faces dual challenges: On one hand, the high-frequency information exchange under traditional periodic communication patterns causes severe waste of network resources; on the other hand, cyberattacks may cause information loss, abnormal delays, or data tampering, which can ultimately lead to system instability. To address these challenges, this paper investigates the secure dynamic integral event-triggered stabilization of photovoltaic microgrids under multimodal deception attacks. To address the communication resource constraints in photovoltaic (PV) microgrid systems, a dynamic integral-event-triggered scheme (DIETS) is proposed. This scheme employs average processing of historical state data to filter out redundant triggering events caused by noise or disturbances. Simultaneously, a time-varying triggering threshold function is designed by integrating real-time system states and historical information trends, enabling adaptive adjustment of dynamic triggering thresholds. In terms of cybersecurity, a secure control strategy against multi-modal deception attacks is incorporated to enhance system resilience. Subsequently, through the Lyapunov–Krasovskii functional and Bessel–Legendre inequality, collaborative design conditions for the controller gain and triggering matrix are formed as symmetric linear matrix inequalities to ensure system stability. The simulation results demonstrate that DIETS recorded only 99 triggering events, achieving a 55.2% reduction compared to the normal event-triggered scheme (ETS) and a 52.6% decrease relative to dynamic ETS, verifying the outstanding communication effectiveness of DIETS. Full article

To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article

In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability. Full article

State estimation of batteries is crucial in battery management systems (BMSs), particularly for accurately predicting the state of charge (SOC), which ensures safe and efficient battery operation. This paper proposes a joint SOC estimation method based on a fractional-order model, utilizing a multi-innovation full-tracking adaptive unscented Kalman filter (FOMIST-AUKF-EKF) combined with an extended Kalman filter (EKF) for online parameter updates. The fractional-order model more effectively represents the battery’s dynamic characteristics compared to traditional integer-order models, providing a more precise depiction of electrochemical processes and nonlinear behaviors. It offers superior modeling for long-memory effects, complex dynamics, and aging processes, enhancing adaptability to aging and nonlinear characteristics. Comparative results indicate a maximum end-voltage error reduction of 0.002 V with the fractional-order model compared to the integer-order model. The multi-innovation technology increases filter robustness against noise by incorporating multiple historical observations, while the full-tracking adaptive strategy dynamically adjusts the noise covariance matrix based on real-time data, thus enhancing estimation accuracy. Furthermore, EKF updates battery parameters (e.g., resistance and capacitance) in real time, correcting model errors and improving SOC prediction accuracy. Simulation and experimental validation show that the proposed method significantly outperforms traditional UKF-based SOC estimation techniques in accuracy, stability, and adaptability. Specifically, under varying conditions such as NEDC and DST, the method demonstrates excellent robustness and practicality, with maximum SOC estimation errors of 0.27% and 0.67%, respectively. Full article

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking. Full article

This paper proposes a state estimation method of linear discrete rectangular singular systems. The system is observable and regular, and the system matrix is rectangular without full column rank. To give the estimation of the state, the state is decomposed into two parts based on QR factorization, and the weighted least squares method is used to obtain the prediction of one part of the state. Then, the partial measurement equation is used to obtain the prediction of the other part of the state, and the projection theorem is used to obtain the state estimation value. Combined with the data-driven idea, a Kalman filtering algorithm based on historical data modeling is established. Finally, the feasibility and effectiveness of our approach is discussed and verified through performance analysis and numerical simulation perspectives. Full article

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades. Full article

In this study, the future snowmelt runoff in the chilly northeast region’s Tangwang River Basin was simulated and predicted using the SWAT model, which was built and used based on the NEX-GDDP-CMIP6 climate model. This study conducted a detailed analysis of the spatial and temporal distribution characteristics of snowmelt runoff using high-resolution DEM, land use, and soil data, along with data from historical and future climatic scenarios. Using box plots and the Bflow digital filtering approach, this study first determined the snowmelt runoff period before precisely defining the snowmelt periods. Sensitivity analysis and parameter rate determination ensured the simulation accuracy of the SWAT model, and the correlation coefficients of the total runoff validation period and rate period were 0.75 and 0.76, with Nashiness coefficients of 0.75 for both. The correlation coefficients of the snowmelt runoff were 0.73 and 0.74, with Nashiness coefficients of 0.7 and 0.68 for both, and the model was in good agreement with the measured data. It was discovered that while temperatures indicate an increasing tendency across all future climate scenarios, precipitation is predicted to increase under the SSP2-4.5 scenario. The SSP2-4.5 scenario predicted a decreasing trend regarding runoff, while the SSP1-2.6 and SSP5-8.5 scenarios showed an increasing trend with little overall change and the SSP5-8.5 scenario even showed a decrease of 6.35%. These differences were evident in the monthly runoff simulation projections. Overall, the findings point to the possibility that, despite future climate change having a negligible effect on the hydrological cycle of the Tangwang River Basin, it may intensify and increase the frequency of extreme weather events, creating difficulties for the management of water resources and the issuing of flood warnings. For the purpose of planning water resources and studying hydrological change in this basin and other basins in cold regions, this study offers a crucial scientific foundation. An in-depth study of snowmelt runoff is of great practical significance for optimizing water resource management, rational planning of water use, spring flood prevention, and disaster mitigation and prevention, and provides valuable data support for future research on snowmelt runoff. Full article

This study introduces a novel uninterruptible power supply (UPS) configuration that integrates active power filter (APF) capabilities within a single-phase matrix converter (SPMC) framework. Power disruptions, particularly affecting critical loads, can lead to substantial economic damages. Historically, conventional UPS systems utilized dual separate converters to function as a rectifier and an inverter, without incorporating any power factor correction (PFC) mechanisms. Such configurations suffered from diminished power density, compromised reliability, and spatial limitations. To address these issues, this research proposes an enhanced UPS design that incorporates APF features into the SPMC. The focus of this investigation is on the efficiency of alternating current (AC) to direct current (DC) conversion and the reverse process utilizing this advanced UPS model. The SPMC is selected to supplant the rectifier and inverter units traditionally employed in UPS architectures. A novel integrated switching strategy is formulated to facilitate the operation of the UPS in either rectifier (charging) or inverter (discharging) modes, contingent upon the operational state. The performance and efficacy of the devised circuit design and switching technique are substantiated through simulations conducted in MATLAB/Simulink 2019 and empirical evaluations using a test rig. The findings demonstrate that the voltage generated is sinusoidal and synchronized with the supply current, thereby minimizing the total harmonic distortion (THD) and enhancing both the power factor and the transition efficiency of the UPS system between its charging and discharging states. Full article

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. Full article

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based ${\mathcal{H}}_{-}/{\mathcal{H}}_{\infty }$ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional ${\mathcal{H}}_{\infty }$ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR). Full article

Concerning environmental safety and mitigating the risk of water pollution, the electroplating industry, historically reliant on the use of elevated concentrations of heavy metals to achieve high-quality products, faces a crucial challenge in monitoring wastewater enriched with these metals, notorious for their adverse effects on ecosystems and human health. Chromium, in both oxidation states Cr (III) and Cr (VI), emerges as a prominently employed metal, yielding noteworthy outcomes throughout the galvanisation process. This research showcases the prototype of an automatic in situ sensor tailored to industry sustainability efforts to facilitate real-time monitoring and efficient water management. This custom sensor, characterized by sensitivity, reliability, and user-friendliness, utilizes UV-Vis colorimetric principle to detect Cr in both oxidation forms ranging from grams per litre (g/L) to parts per million (ppm). This is made possible by the unique vibrant colours induced by chromium ions, enabling the precise measurement of analyte concentrations. Thanks to 3D printing, this sensor system interacts with customized parts, designed and validated through simulation processes, for filtering out particulate that may interfere with the analysis. The outcome represents a synergistic blend of technology and environmental responsibility, aligning industrial processes with the goal of safeguarding water resources and ecosystems. Full article

Federated learning (FL) is a highly promising collaborative machine learning method that preserves privacy by enabling model training on client nodes (e.g., mobile phones, Internet-of-Things devices) without sharing raw data. However, FL is vulnerable to Byzantine nodes, which can disrupt model performance, render training ineffective, or even manipulate the model by transmitting harmful gradients. In this paper, we propose a Byzantine fault-tolerant FL algorithm called federated learning with trustworthy data and historical information (FLTH). It utilizes a small trusted training dataset at the parameter server to filter out gradient updates from suspicious client nodes during model training, which provides both Byzantine resilience and convergence guarantee. It further introduces a historical information-based credibility assessment scheme such that the client nodes performing poorly over the long-term have a lower impact on the aggregation of gradients, thereby enhancing fault tolerance capability. Additionally, FLTH does not compromise the training efficiency of FL because of its low time complexity. Extensive simulation results show that FLTH achieves higher model accuracy compared to state-of-the-art methods under typical kinds of attack. Full article