Search Results (33,448)

In this paper, a better power optimizer architecture has been presented for PV strings, using a buck converter for each PV module to address partial shading conditions. The buck converter, though rarely used, is a natural converter for partial shading effects, as it converts the lower current of the shaded module to a higher output current. Usually, the advanced architecture activates the isolated converters (complex) of only shaded modules to draw extra current from the inverter’s DC-link node to maintain the string current ($I_{string}$). On the other hand, the conventional architecture activates converters (basic) of all modules regardless of their shading status. The proposed architecture contains a unique design with a new schematic layout, where it activates the buck converters of only shaded modules without drawing extra current from the DC-link. Thus, it combines the benefits of both architectures—selective converter operation, basic topology, high efficiency, low voltage stress, and low control complexity—while eliminating their drawbacks. The designing philosophy, control mechanism, and fundamental operation of the proposed architecture have been comprehensively explained and validated through simulation experiments. Three levels of shading are used to test the proposed architecture for string containing three PV modules: (1) a single module moderate (15%) shading level, (2) a single module strong (50%) shading level, and (3) a double module extreme (75%) and moderate (25%) shading levels. The results show a successful operation of the proposed architecture as it maintains a common $I_{string}$ for an inverter, where all the shaded modules remain active. The architecture exhibits an average efficiency over 97% under normal conditions. A comparative analysis of architectures has been presented to indicate the enhanced features of the proposed architecture. Full article

Overfeeding, currently defined as providing excessive energy and nutrients beyond metabolic requirements, is a common yet often overlooked issue in the intensive care unit (ICU) setting. Understanding the factors contributing to overfeeding and implementing strategies to prevent it is essential for optimizing patient care in the ICU. Several factors contribute to overfeeding in the ICU, including inaccurate estimation of energy requirements, formulaic feeding protocols, and failure to adjust nutritional support based on individual patient needs. Prolonged overfeeding can lead to insulin resistance and hepatic dysfunction, exacerbating glycemic control, increasing the risk of infectious complications, and worsening clinical outcomes. Clinically, overfeeding has been linked to delayed weaning from mechanical ventilation, prolonged ICU stay, and increased mortality rates. Regular review and adjustment of feeding protocols, incorporating advances in enteral and parenteral nutrition strategies, are essential for improving patient outcomes. Clinicians must be proficient in interpreting metabolic data, understanding the principles of energy balance, and implementing appropriate feeding algorithms. Interdisciplinary collaboration among critical care teams, including dieticians, physicians, and nurses, is crucial for ensuring consistent and effective nutritional management. Overfeeding remains a significant concern in the ICU after discharge as well, implying further complications for patient safety and integrity. By understanding the causes, consequences, and strategies for the prevention of overfeeding, healthcare providers can optimize nutrition therapy and mitigate the risk of metabolic complications. Through ongoing education, interdisciplinary collaboration, and evidence-based practice, the ICU community can strive to deliver personalized and precise nutritional support to critically ill patients. Full article

Highly over-mature marine shales are distributed worldwide with substantial resource potential, yet their pore structure characteristics and controlling mechanisms remain poorly understood, hindering accurate shale gas resource prediction and efficient development. This study focuses on the Cambrian Niutitang Formation shales in the Upper Yangtze region of South China. To decipher the multiscale pore network architecture and its genetic constraints, we employ scanning electron microscopy (SEM) pore extraction and fluid intrusion methods (CO₂ and N₂ adsorption, and high-pressure mercury intrusion porosimetry) to systematically characterize pore structures in these reservoirs. The results demonstrate that the shales exhibit high TOC contents (average 4.78%) and high thermal maturity (average Ro 3.64%). Three dominant pore types were identified: organic pores, intragranular pores, and intergranular pores. Organic pores are sparsely developed with diameters predominantly below 50 nm, displaying honeycomb, slit-like, or linear morphologies. Intragranular pores are primarily feldspar dissolution voids, while intergranular pores exhibit triangular or polygonal shapes with larger particle sizes. CO₂ adsorption isotherms (Type I) and low-temperature N₂ adsorption curves (H3-H4 hysteresis) indicate wedge-shaped and slit-like pores, with pore size distributions concentrated in the 0.5–50 nm range, showing strong heterogeneity. Pore structure shows weak correlations with TOC and quartz content but a strong correlation with feldspar abundance. This pattern arises from hydrocarbon generation exhaustion and graphitization-enhanced organic pore collapse under high compaction stress, which reduces pore preservation capacity. The aulacogen tectonic setting engenders proximal sediment provenance regimes that preferentially preserve labile minerals such as feldspars. This geological configuration establishes optimal diagenetic conditions for the subsequent development of meso- and macro-scale of dissolution pores. Our findings demonstrate that feldspar-rich shales, formed in a proximal depositional system with well-developed inorganic pores, serve as favorable reservoirs for the exploration of highly over-mature marine shale gas. Full article

Microservice deployment methods in edge-native computing environments hold great potential for minimizing user application response time. However, most existing studies overlook the communication overhead between microservices and controllers, as well as the impact of microservice pull time on user response time. To address these issues, this paper proposes a multi-controller service mesh architecture to reduce data transfer overhead between microservices and controllers. Furthermore, we formulate the microservice deployment problem as an optimization problem aimed at minimizing both system communication overhead and microservice deployment cost. To achieve this, we introduce a novel RIME optimization algorithm and enhanced Adaptive Crested Porcupine Optimizer (RIME-ACPO) algorithm that optimizes microservice placement decisions. Notably, this algorithm incorporates a real-time resource monitoring-based load balancing algorithm, dynamically adjusting microservice deployment according to edge server resource utilization to enhance the execution performance of user applications. Finally, extensive simulation experiments were conducted to validate the effectiveness of the proposed algorithm. The experimental results demonstrate that, compared with other algorithms, our algorithm significantly improves user response time, optimizes resource utilization, and reduces the total cost. Full article

The development of efficient bioremediation technologies for polycyclic aromatic hydrocarbons contamination is a hot research topic in the environmental field. In this study, we found that the Mycobacterium sp., TJFP1, has the function of degrading low molecular weight PAHs, and further investigated its degradation characteristics using the PAH model compound phenanthrene as a target pollutant. The optimal growth and degradation conditions were determined by single-factor experiments to be 37 °C, pH 9.0, and an initial concentration of 100 mg/L phenanthrene. Under this condition, the degradation efficiency of phenanthrene reached 100% after 106 h of incubation, and the average degradation rate could reach 24.48 mg/L/day. Combined with whole genome sequencing analysis, it was revealed that its genome carries a more complete phenanthrene degradation pathway, including functional gene clusters related to the metabolism of PAHs, such as phd and nid. Meanwhile, intermediates such as phthalic acid were detected; it was determined that TJFP1 metabolizes phenanthrene via the phthalic acid pathway. Simulated contaminated soil experiments were also conducted, and the results showed that the removal rate of phenanthrene from the soil after 20 days of inoculation with the bacterial strain was about 3.7 times higher than that of the control group (natural remediation). At the same time from the soil physical and chemical properties and soil microbial community structure of two levels to explore the changes in different means of remediation, indicating that it can be successfully colonized in the soil, and as a dominant group of bacteria to play the function of remediation, verifying the environmental remediation function of the strains, for the actual inter-soil remediation to provide theoretical evidence. This study provides efficient strain resources for the bioremediation of PAH contamination. Full article

The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart-rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5 min and 10 min segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB, v2.9.2), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5 min and 10 min segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5 min and 10 min data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest. Full article

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) schemes for optimal dispatch of active power, mitigating frequency deviations, aiding reserve capacity management, and improving energy balancing. Second, frequency stability and optimal dispatch are formulated in the N—MADRL framework using the physical constraints under a dynamic simulation environment. Third, a decentralised coordinated control scheme is implemented in the HPP environment using communication-resilient scenarios to address system vulnerabilities. Finally, the practicality of the N—MADRL approach is demonstrated in a Grid2Op dynamic simulation environment for optimal dispatch, energy reserve management, and frequency control. Results demonstrated on the IEEE 14 bus network show that compared to PPO and DDPG, N—MADRL achieves 42.10% and 61.40% higher efficiency for optimal dispatch, along with improvements of 68.30% and 74.48% in mitigating frequency deviations, respectively. The proposed approach outperforms existing methods under partially, fully, and randomly connected scenarios by effectively handling uncertainties, system intermittency, and communication resiliency. Full article

In this article, we propose a fuzzy proportional–integral–derivative (Fuzzy-PID) controller that integrates a system-identification-based control strategy. We aim to address the challenge of regulating electrical conductivity (EC) in a fertigation system to ensure precise nutrient delivery. During fertilization, the nutrient solution EC value increases gradually and nonlinearly as water and fertilizer are integrated. Precise fertilizer injection is essential to maintain stable EC levels, preventing crop undernutrition or overnutrition. The fertigation process is modeled using a particle swarm optimization (PSO)-based system identification method. A Fuzzy-PID method is then employed to regulate the nutrient solution EC value based on the pre-determined or real-time identified transfer model. The proposed control strategy is deployed within a programmable logic controller (PLC) environment and validated on a PLC-based fertilizer system. The results show that the identified transfer model accurately represents the fertilizer mixing process, achieving a standard Mean Absolute Percentage Error (MAPE) value of less than 5% within 2 s using the proposed PSO-based identification method. In the simulation tests, the proposed Fuzzy-PID control rule would converge the nutrient solution to target EC values 1000 and 1500 μs/cm within a deviation band ± 50 μs/cm, within 6 s from the recorded identified transfer models and within 25 s from the real-time identified transfer models. In the device’s test, the convergence time of the fertigation EC control is approximately 16 s from the history data and 42 s from the real-time collected data, with a deviation band ± 50 μs/cm. In contrast, it may take over 70 s for the EC regulation of the same fertilization, using the classic control methods including conventional PID and Fuzzy-PID. The proposed control strategy significantly improves EC regulation in terms of speed, stability, and precision, enhancing the performance of fertilizer mixing systems. Full article

Superabsorbent polymers (SAPs) are widely employed as an internal curing agent to enhance the durability and shrinkage–cracking resistance of concrete. However, while its macroscopic effects on concrete properties (e.g., strength reduction) have been documented, the nanoscale mechanisms governing the mechanical behavior of calcium silicate hydrate (C-S-H) gel in SAP-modified concrete remain poorly understood. This knowledge gap limits the optimization of SAP content for balancing durability and strength, a critical challenge in high-performance concrete design. In this paper, we address this scientific problem by combining experimental characterization and molecular dynamics (MD) simulations to systematically investigate how SAP-induced pore structure modifications dictate the mechanical performance of C-S-H gel. First, we analyzed the effects of SAP on concrete pore structure and compressive strength, revealing its role in refining capillary pores into gel pores. Next, MD simulations were employed to construct C-S-H gel models with controlled pore size distributions at three SAP contents (0.2%, 0.3%, and 0.5%), to establish a quantitative relationship between pore characteristics and material performance. The results reveal that pores of ~0.74 nm diameter, predominantly located in weak interfacial regions, critically govern the mechanical behavior of C-S-H gel. At 0.2% SAP content, the C-S-H gel exhibits the highest bulk modulus (10.61 GPa) and optimal mechanical properties, whereas 0.3% SAP leads to a dominant pore cluster at 1.12 nm, resulting in significant reductions in bulk modulus (30.8%), shear modulus (29%), and Young’s modulus (22.3%). These findings establish a quantitative pore-property relationship, providing a mechanistic basis for tailoring SAP content to enhance both durability and mechanical performance in concrete, ultimately advancing the design of longer-lasting infrastructure. Full article

In the domain of application of PN theory, the system deadlock problem of a flexible manufacturing system (FMS) is a thorny problem that needs to be solved urgently. All the research has the same objective of designing optimal controllers with maximal permissiveness and liveness. Plenty of the past literature used deadlock prevention as the main control strategy that is implemented by control places. However, these methods usually forbid undesirable system states from being reached, while reducing the system’s liveness. This study employed the resource flow graph (RFG)-based method to achieve a deadlock recovery policy that can maintain maximal permissiveness by adding control transitions (CTs). Also, we improved the current definition of RFG and developed a systematic approach for generating the corresponding RFG, which is based on flow mirroring pair (FMP) functions and the software Graphviz 12.2.1. Furthermore, this study proposed an automatic method that forms DOT script for generating Graphviz images, which is convincingly demonstrated in this study to enhance the execution efficiency and recognition of circular waiting situations. Full article

Proton exchange membrane fuel cells (PEMFC), distinguished by rapid refueling capability and zero tailpipe emissions, have emerged as a transformative energy conversion technology for automotive applications. Nevertheless, their widespread commercialization remains constrained by technical limitations mainly in operational longevity. Precise prognostics of performance degradation could enable real-time optimization of operation, thereby extending service life. This investigation proposes a hybrid prognostic framework integrating steady-state modeling with dynamic condition. First, a refined semi-empirical steady-state model was developed. Model parameters’ identification was achieved using grey wolf optimizer. Subsequently, dynamic durability testing data underwent systematic preprocessing through a correlation-based screening protocol. The processed dataset, comprising model-calculated reference outputs under dynamic conditions synchronized with filtered operational parameters, served as inputs for a recurrent neural network (RNN). Comparative analysis of multiple RNN variants revealed that the hybrid methodology achieved superior prediction fidelity, demonstrating a root mean square error of 0.6228%. Notably, the integration of steady-state physics could reduce the RNN structural complexity while maintaining equivalent prediction accuracy. This model-informed data fusion approach establishes a novel paradigm for PEMFC lifetime assessment. The proposed methodology provides automakers with a computationally efficient framework for durability prediction and control optimization in vehicular fuel cell systems. Full article

This study developed a solid-state fermentation system based on Trichoderma harzianum, which significantly enhanced the nutritional value of distiller’s grain (DG) feed through a multi-stage synergistic treatment process. During the cellulase production phase, rice husk was used as an auxiliary material, and specific degradation of DGs was effectively enhanced. Through optimization using response surface methodology, the optimal enzyme production conditions were determined. The filter paper enzyme activity reached a peak of 1.45 U/gds (enzyme activity per gram of dry substrate) when the moisture content was 53%, the fermentation time was 3 days, and the Tween-80 dosage was 0.015 mL/g (dry weight basis). Under these conditions, the crude enzyme solution was used to hydrolyze DGs. Compared to original DGs, the content of reducing sugars increased by 10.75%. In the stage of protein production, segmented hydrolysis fermentation (SHF) and simultaneous saccharification fermentation (SSF) processes were employed using yeast. The results showed that SSF pathway showed better performance, and the true protein content reached 15.16% after 11 days, an increase of 41.5% compared to the control. Finally, through secondary fermentation regulated by Lactobacillus fermentum, the flavor of the feed was significantly improved. This study innovatively integrated bio-enzymatic hydrolysis and multi-strain synergistic fermentation technologies, providing a novel strategy for the efficient and sustainable production of protein feed based on DGs. Full article

Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive. Full article

With the acceleration of urban renewal, directional blasting has become a common method for building demolition. Analyzing the time–frequency characteristics of blast-induced seismic waves allows for the assessment of risks to surrounding structures. However, the signals monitored are frequently tainted with noise, which undermines the precision of time–frequency analysis. To counteract the dangers posed by blast vibrations, effective signal denoising is crucial for accurate evaluation and safety management. To tackle this challenge, a dual denoising model is proposed. This model consists of two stages. Firstly, it applies endpoint processing (EP) to the signal, followed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to suppress low-frequency clutter. High-frequency noise is then handled by controlling the multi-scale permutation entropy (MPE) of the intrinsic mode functions (IMF) obtained from EP-CEEMDAN. The EP-CEEMDAN-MPE framework achieves the first stage of denoising while mitigating the influence of endpoint effects on the denoising performance. The second stage of denoising involves combining the IMF obtained from EP-CEEMDAN-MPE to generate multiple denoising models. An objective function is established considering both the smoothness of the denoising models and the standard deviation of the error between the denoised signal and the measured signal. The denoising model corresponding to the optimal solution of the objective function is identified as the dual denoising model for blasting seismic wave signals. To validate the denoising effectiveness of the denoising model, simulated blasting vibration signals with a given signal-to-noise ratio (SNR) are constructed. Finally, the model is applied to real engineering blasting seismic wave signals for denoising. The results demonstrate that the model successfully reduces noise interference in the signals, highlighting its practical significance for the prevention and control of blasting seismic wave hazards. Full article

Coherent anti-Stokes Raman scattering (CARS) is a powerful nonlinear spectroscopic technique widely used in biological imaging, chemical analysis, and combustion and flame diagnostics. The adoption of pulse shapers in CARS has emerged as a useful approach, offering precise control of optical waveforms. By tailoring the phase, amplitude, and polarization of laser pulses, the pulse shaping approach enables selective excitation, spectral resolution improvement, and non-resonant background suppression in CARS. This paper presents a comprehensive review of applying pulse shaping techniques in CARS spectroscopy for biophotonics. There are two different pulse shaping strategies: passive pulse shaping and active pulse shaping. Two passive pulse shaping techniques, hybrid CARS and spectral focusing CARS, are reviewed. Active pulse shaping using a programmable pulse shaper such as spatial light modulator (SLM) is discussed for CARS spectroscopy. Combining active pulse shaping and passive shaping, optimizing CARS with acousto-optic programmable dispersive filters (AOPDFs) is discussed and illustrated with experimental examples conducted in the authors’ laboratory. These results underscore pulse shapers in advancing CARS technology, enabling improved sensitivity, specificity, and broader applications across diverse scientific fields. Full article