Search Results (81)

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development of advanced control techniques to ensure stability and power quality. The proposed algorithm combines droop control, synchronverter dynamics, and virtual impedance to achieve a robust and efficient control strategy. Simulations were conducted to validate the algorithm’s performance, demonstrating its capability to maintain voltage within acceptable limits and improve the inertial response of the microgrid. The results contribute to the advancement of intelligent and resilient microgrid development, which is essential for the transition towards a more sustainable energy system. Full article

Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited accuracy of traditional data-driven models, instability in sequence prediction, and poor adaptability to diverse operational environments. To address these issues, we propose a novel prognostics approach integrating three key components: time-intrinsic mode functions-derived feature representation (TIR) sequences, a one-dimensional temporal feature convolution–reallocation network (TFCR) with a flexible configuration scheme, and a physics-based Kalman filtering method. The approach first converts denoised signals into TIR-sequences using windowed empirical mode decomposition (EMD). The TFCR network then extracts hidden high-dimensional features from these sequences and maps them to the initial RUL. Finally, physics-based Kalman filtering is applied to enhance prediction stability and enforce physical constraints, producing refined RUL estimates. The experimental results based on the XJTU-SY dataset show the superiority of the proposed approach and further prove the feasibility of this method in bearing RUL estimation. Full article

Foredeep basins often host significant natural gas reservoirs within siliciclastic successions, as exemplified by the Po Plain (Northern Italy), one of Europe’s largest foredeep basins. Here, numerous depleted gas reservoirs have been successfully converted into underground gas storage (UGS) facilities. For safe and efficient storage operations, detailed reservoir characterization and continuous monitoring of surface and subsurface effects are crucial. This study investigates the Cornegliano Laudense reservoir during its first 5–7 years as a UGS facility, employing an integrated monitoring approach that combines traditional methods (InSAR for surface deformation, microseismic monitoring) with innovative techniques (Pulsed Neutron Log-PNL). The results clearly illustrate and quantify the significant increase in storage capacity over a relatively short operational period, primarily driven by the progressive displacement of formation water by injected gas. Despite increased stored gas volumes, monitoring revealed no adverse effects on surface stability or subsurface seismicity. This integrated methodology demonstrates substantial potential for refining predictive models, optimizing storage efficiency, and enhancing sustainable management practices for underground gas storage operations. Full article

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial disease that incurs significant healthcare costs due to diagnostic and treatment needs. This study aimed to estimate healthcare expenses related to IPF diagnosis, treatment, and follow-up, including factors affecting overall expenditure. Methods: This retrospective cohort study included 276 IPF patients from a tertiary hospital (2013–2022). Diagnostic and treatment costs were analyzed, including antifibrotic medications (pirfenidone and nintedanib), diagnostic tests (pulmonary function tests and performance evaluation tests), and interventions (fiberoptic bronchoscopy, imaging modalities). Costs in Turkish Lira were converted to United States dollars. Statistical analysis was performed using non-parametric tests to evaluate expenditure correlations with demographic, clinical, and treatment parameters, which included the Mann–Whitney and Spearman Rank Correlation tests when appropriate. Results: The median healthcare expenditure was USD 429.1 (9.13–21,024.57). Inpatient costs (USD 582.67; USD 250.22 to USD 1751, 25th and 75th percentile, respectively) were higher than outpatient costs (USD 192.36; USD 85.75 to USD 407.47, 25th and 75th percentile, respectively). Antifibrotic regimens did not differ significantly in cost or duration (Z = 0.657; p = 0.511) (mean pirfenidone duration: 1.1 ± 1.0 years; mean nintedanib duration: 0.6 ± 0.9 years). Diagnostic tests, particularly pulmonary function tests (PFT) (p: 0.001, Rho: 0.337), diffusing capacity of the lungs for carbon monoxide (DLCO) (p: 0.001, Rho: 0.516), and high-resolution computed tomography (HRCT) (p: 0.001, Rho: 0.327), were the primary drivers of costs. Longer treatment duration was positively correlated with expenditure (Rho: 0.264, p: 0.001 and Rho: 0.247, p: 0.006 for pirfenidone and nintedanib, respectively) while age showed a weak negative correlation (Rho = −0.184, p = 0.002). Gender and type of antifibrotic regimen did not show any significant effect on costs. Discussion: Diagnostic and follow-up testing were the main contributors to costs, driven by reimbursement requirements and the progressive nature of IPF. Antifibrotic medications, although expensive, provided clinical stability, potentially reducing hospitalization needs but increasing long-term care expenses. Variations in healthcare systems affect expenditures, with Turkey’s universal coverage lowering costs compared to Western countries. The study’s main limitations include being a single-center, retrospective study and its inability to include comorbidities and disease severity in the statistical analysis. Conclusions: IPF management is resource-intensive, with diagnostic tests and follow-up driving costs independent of demographics and treatment modality. Anticipating higher expenditures with prolonged survival and evolving treatment options is crucial for healthcare budget planning. Preparation of healthcare policies accordingly to these observations, which must include an overall increase in cost due to treatment duration and survival, remains a crucial aspect of budget control. Full article

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response. Full article

Driven by the environmental benefits associated with reduced greenhouse gas emissions, oil companies have intensified research efforts into reassessing the strategies used to meet the electrical demands of offshore production platforms. Among the various alternatives available, the deployment of onshore–offshore interconnections via High-Voltage Direct Current (HVDC) transmission systems has emerged as a promising solution, offering both economic and operational advantages. In addition to reliably meeting the electrical demand of offshore facilities, this approach enables enhanced operational flexibility due to the advanced control and regulation capabilities inherent to HVDC converter stations. Based on the use of interconnection through an HVDC link, aiming to evaluate the operation of the electrical system as a whole, this study focuses on evaluating dynamic events using the PSCAD software version 5.0.2 to analyze the direct online starting of a large induction motor and the sudden loss of a local synchronous generating unit. The simulation results are then analyzed to assess the effectiveness of both Grid-Following (GFL) and Grid-Forming (GFM) control strategies for the converters, while the synchronous generators are evaluated under both voltage regulation and constant power factor control operation, with a particular focus on system stability and restoration of normal operating conditions in the sequence of events. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

The application of oxygen sensors based on Tunable Diode Laser Absorption Spectroscopy (TDLAS) in the industrial field has received extensive attention. However, most of the existing studies construct detection systems using discrete devices, making it difficult to apply them in the industrial field. In this work, through the optimization of the sensor circuit, the size of the core components of the sensor is reduced to 7.8 × 7.8 × 11.8 cm³, integrating the laser, photodetector, and system control circuit. A novel integrated optical path design is proposed for the optical mechanical structure, which enhances the structural integration and long-term optical path stability while reducing the system assembly complexity. The interlocking design of the laser-driven digital-to-analog converter (DAC) and photocurrent acquisition analog-to-digital converter (ADC) reduces the requirements of the harmonic signal extraction for the system hardware. By adopting a high-precision ADC and a high-resolution pulse-width modulation (PWM), the peak-to-peak value of the laser temperature control noise is reduced to 2 m°C, thereby reducing the detection noise of the sensor. This oxygen detection system has a minimum response time of 0.1 s. Under the condition of a 0.5 m detection optical path, the Allan variance shows that when the integration time is 5.6 s, the detection limit reaches 53.4 ppm, which is ahead of the detection accuracy of similar equipment under the very small system size. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts in terms of label generation for supervised methods. One way is to employ an inversion network to convert the seismic shot gathers into a velocity model. The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. To further improve the efficiency, we propose a two-stage training strategy for the self-supervised learning FWI. The first stage is to pretrain the inversion network using a simultaneous source for a large-scale velocity model with high efficiency. The second stage is switched to modeling the separate shot gathers for an accurate measurement of the seismic data to invert the velocity model details. The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. The time-domain 2D acoustic wave equation serves as the physical constraint in this self-supervised framework. Different loss functions are used for the two stages, that is, the waveform loss with time weighting for the first stage (simultaneous source) and the hybrid waveform with time weighting and logarithmic envelope loss for the second stage (separate source). Comparative experiments demonstrate that the proposed approach improves both inversion accuracy and efficiency on the Marmousi2 model, Overthrust model, and BP model tests. Moreover, the method exhibits excellent noise resistance and stability when low-frequency data component is missing. Full article

Soil salinization presents a significant challenge, driven by factors such as inadequate drainage, shallow aquifers, and high evaporation rates, threatening global food security. The sunflower emerges as a key cash crop in such areas, providing the opportunity to convert its straw into biochar, which offers additional agronomic and environmental benefits. This study investigates the effectiveness of biochar interlayers in enhancing salt leaching and suppressing upward salt migration through integrated laboratory and field experiments. The effectiveness of varying biochar interlayer application rates was assessed in promoting salt leaching, decreasing soil electrical conductivity (EC), and enhancing crop performance in saline soils through a systematic approach that combines laboratory and field experiments. The biochar treatments included a control (CK) and different applications of 20 (BL20), 40 (BL40), 60 (BL60), and 80 (BL80) tons of biochar per hectare, all applied below a depth of 20 cm, with each treatment replicated three times. The laboratory and field experimental setups maintained consistency in terms of biochar treatments and interlayer placement methodology. During the laboratory column experiments, the soil columns were treated with deionized water, and their leachates were analyzed for EC and major ionic components. The results showed that columns with biochar interlayers exhibited significantly higher efflux rates compared to those of the control and notably accelerated the time required for the effluent EC to decrease to 2 dS m⁻¹. The CK required 43 days for full discharge and 38 days for EC stabilization below 2 dS m⁻¹. In contrast, biochar treatments notably reduced these times, with BL80 achieving discharge in just 7 days and EC stabilization in 10 days. Elution events occurred 20–36 days earlier in the biochar-treated columns, confirming biochar’s effectiveness in enhancing leaching efficiency in saline soils. The field experiment results supported the laboratory findings, indicating that increased biochar application rates significantly reduced soil EC and ion concentrations at depths of 0–20 cm and 20–40 cm, lowering the EC from 7.12 to 2.25 dS m⁻¹ and from 6.30 to 2.41 dS m⁻¹ in their respective layers. The application of biochar interlayers resulted in significant reductions in Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻ concentrations across both soil layers. In the 0–20 cm layer, Na⁺ decreased from 3.44 to 2.75 mg·g⁻¹, K⁺ from 0.24 to 0.11 mg·g⁻¹, Ca²⁺ from 0.35 to 0.20 mg·g⁻¹, Mg²⁺ from 0.31 to 0.24 mg·g⁻¹, Cl⁻ from 1.22 to 0.88 mg·g⁻¹, SO₄²⁻ from 1.91 to 1.30 mg·g⁻¹ and HCO₃⁻ from 0.39 to 0.18 mg·g⁻¹, respectively. Similarly, in the 20–40 cm layer, Na⁺ declined from 3.62 to 3.05 mg·g⁻¹, K⁺ from 0.28 to 0.12 mg·g⁻¹, Ca²⁺ from 0.39 to 0.26 mg·g⁻¹, Mg²⁺ from 0.36 to 0.27 mg·g⁻¹, Cl⁻ from 1.18 to 0.80 mg·g⁻¹, SO₄²⁻ from 1.95 to 1.33 mg·g⁻¹ and HCO₃⁻ from 0.42 to 0.21 mg·g⁻¹ under increasing biochar rates. Moreover, the use of biochar interlayers significantly improved the physiological traits of sunflowers, including their photosynthesis rates, stomatal conductance, and transpiration efficiency, thereby boosting biomass and achene yield. These results highlight the potential of biochar interlayers as a sustainable strategy for soil desalination, water conservation, and enhanced crop productivity. This approach is especially promising for managing salt-affected soils in regions like California, where soil salinization represents a considerable threat to agricultural sustainability. Full article

The transition toward sustainable and decentralized energy solutions necessitates the development of innovative bioelectronic systems capable of harvesting and converting renewable energy. Here, we present a novel photo-bioelectrochemical fuel cell architecture based on a biohybrid anode integrating laser-induced graphene (LIG), poly(3,4-ethylenedioxythiophene) (PEDOT), and isolated thylakoid membranes. LIG provided a porous, conductive scaffold, while PEDOT enhanced electrode compatibility, electrical conductivity, and operational stability. Compared to MXene-based systems that involve complex, multi-step synthesis, PEDOT offers a cost-effective and scalable alternative for bioelectrode fabrication. Thylakoid membranes were immobilized onto the PEDOT-modified LIG surface to enable light-driven electron generation. Electrochemical characterization revealed enhanced redox activity following PEDOT modification and stable photocurrent generation under light illumination, achieving a photocurrent density of approximately 18 µA cm⁻². The assembled photo-bioelectrochemical fuel cell employing a gas diffusion platinum cathode demonstrated an open-circuit voltage of 0.57 V and a peak power density of 36 µW cm⁻² in 0.1 M citrate buffer (pH 5.5) under light conditions. Furthermore, the integration of a charge pump circuit successfully boosted the harvested voltage to drive a low-power light-emitting diode, showcasing the practical viability of the system. This work highlights the potential of combining biological photosystems with conductive nanomaterials for the development of self-powered, light-driven bioelectronic devices. Full article

While extensive research has addressed electromechanical systems interacting with power electronic converters, most studies lack a unified modeling framework that simultaneously captures converter switching behavior, nonlinear dynamics, and linearized control-oriented representations. In particular, the dynamic interaction between two-level full-bridge converters and angular-motion electromechanical switching devices (EMDs) is often simplified or abstracted, thereby limiting control system design and frequency-domain analysis. This work presents a comprehensive dynamic modeling methodology for an angular-motion EMD driven by a full-bridge dc-dc converter. The modeling framework includes (i) a detailed nonlinear switching model, (ii) an averaged nonlinear model suitable for control design, and (iii) a small-signal linearized model for deriving transfer functions and evaluating system stability. The proposed models are rigorously validated through time-domain simulations and Bode frequency analysis, confirming both theoretical equilibrium points and dynamic characteristics such as resonant frequencies and phase margins. The results demonstrate strong consistency across the modeling hierarchy and reveal critical features—such as ripple-induced resonance and low-frequency coupling—that are essential for robust controller design. This framework established a foundational tool for advancing the control and optimization of electromechanical switching systems in high-performance applications. Full article

Virtual inertia is a measure of the capability of distributed sources and loads within power supply units to resist system frequency variations through additional control strategies applied to converters. The reasonable allocation of virtual inertia is beneficial for enhancing system stability. In response to the insufficient consideration of multi-regional coordination and difficulties in balancing frequency change rates in existing virtual inertia allocation methods, this paper proposes a neural network-based coordinated virtual inertia allocation method for multiple regions. First, a data-driven model is constructed based on the RBFNN neural networks to map the feasible region boundaries of virtual inertia for distributed resources under different disturbance scenarios. Second, a multi-area virtual inertia optimization allocation model is established, aiming to minimize both the inter-area frequency change rates and the differences between them, while considering the regulation capabilities of grid-forming PV systems and ESS. Following this, a genetic algorithm-based solving strategy is designed to achieve the global optimal allocation of virtual inertia. Finally, simulations verify the effectiveness of the coordinated allocation strategy in enhancing frequency stability across multiple autonomous regions. This optimization method reduces the frequency variation rate in both regions and maintains relative stability between the regions, thereby enhancing the system’s disturbance rejection capability. The results showed that after optimizing the virtual inertia allocation using the method proposed in this paper, the frequencies of the two regions increased by 0.11 Hz and 0.14 Hz, respectively, and the dynamic rate of frequency change decreased by 50.2% and 52.1%. Therefore, this study provides a foundational method and a feasible approach to multi-area virtual inertia optimization allocation in the new distribution system, contributing to frequency support via virtual inertia in distribution network optimization operation. Full article