Network Structure Research Articles

Three-dimensional porous SiC/BDD electrode with long-term robustness and enhanced electrochemical degradation performance for refractory organic pollutants

Boron-doped diamond (BDD) electrodes with a three-dimensional (3D) porous network structure can overcome the mass transfer limitations and increase substantially the utilization rate of active oxygen species. Nonetheless, the 3D porous network structure was invariably built using metallic substrates, which inherently have inferior stability because of their non-corrosion resistance and the severe thermal mismatch between BDD films and substrates. Herein, we reported, for the first time, the use of a commercially porous silicon carbide (SiC) as the substrate to construct a 3D SiC/BDD electrode to simultaneously alleviate the problematics of substrate corrosion and thermal mismatch. This novel electrode exhibited a long-term service lifetime of 800 h in 3 M H2SO4 under the current density of 50 mA cm−2, over 500 times longer than the conventional metallic substrate-based BDD electrodes. The optimum p-SiC/BDD electrode exhibited a high oxygen evolution potential of 2.04 V vs. SHE, a low charge transfer resistance of 18.4 Ω, a high mass transfer coefficient of 2.78 × 10−5 m s−1, and a thin diffusion layer thickness of 25.2 μm. The proposed electrode had a threefold larger pseudo-first-order reaction rate constant than a flat BDD electrode, although it consumed just a fourth of the latter’s electric energy. The superior performance was attributable to an increased mass transfer rate confirmed by computational fluid dynamics (CFD) simulations as well as a high yield of reactive oxygen species verified by electron spin resonance (ESR). Furthermore, the proposed SiC/BDD electrode could work effectively under different water matrices in the presence of diverse salt electrolytes, thus paving a new avenue for the structure design of robust non-active anode materials.

Effect of starch categories and mass ratio of TA/starch on the emulsifying performance and stability of emulsions stabilized by tannic acid-starch complexes

This study compounded natural corn starch (CS), mung bean starch (MBS) and potato starch (PS) with tannic acid (TA) to stabilize O/W Pickering emulsion. The effect of TA/starch mass ratio (0–0.25) and three starch categories on particle properties, emulsifying properties, lipid oxidation, freeze-thaw stability, emulsion powder and digestive properties were comprehensibly investigated. In detail, the TA/starch complexes size increased gradually (91.14 nm–200.87 nm) and the hydrophobicity first increased and then decreased (TA/CS > TA/MBS > TA/PS) with increasing TA/starch mass ratio. In addition, the emulsifying ability of TA/starch complexes also increased first and then decreased with increasing mass ratio, especially TA/CS system was the best, which was the same as the hydrophobicity conclusion (θow = 80.46°). Moreover, four starch-based emulsion application characteristics were further evaluated to reveal interface structure. Compared to CS and PS system, TA/MBS emulsion had stronger ability to resist the oil oxidation (TBA = 2.54 μg/mL), destruction of ice crystal (whiter emulsion powder) and digestive enzymes (FFAs = 75.33 %). It mainly attributed to the crosslinking network structure and the highest surface load of TA/MBS complexes. This study would provide new ideas for the design and application of emulsifying properties and emulsion stability.

Neural relational and dynamics inference for complex systems

Many complex processes in the real world can be viewed as complex systems and their evolution is governed by underlying nonlinear dynamics. However, one can only access the trajectories of the system without knowing the underlying system structure and dynamics in most cases. To address this challenge, this paper proposes a model called Neural Relational and Dynamics Inference (NRDI) that combines graph neural networks (GNNs) and ordinary differential equation systems (ODEs) to handle both continuous-time dynamics prediction and network topology inference for complex systems. Our model contains two modules: (1) the network inference module, which infers system structure from input system trajectories using GNNs, and (2) the dynamics learning module, which employs GNNs to fit the differential equation system for predicting future trajectories. We tested NRDI’s performance on system trajectory prediction and graph reconstruction separately. Experimental results show that the proposed NRDI outperforms many baseline models on continuous-time complex network dynamics prediction, and can explicitly infer network structures with high accuracy.

Lightweight algorithm based on you only look once version 5 for multiple class defect detection on wind turbine blade surfaces

A lightweight wind turbine blade surface multiple class defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed to address the issues of low accuracy, poor efficiency, and high power consumption associated with manual and traditional automation algorithms used for detecting surface defects on wind turbine blades. Firstly, by replacing the Concentrated-Comprehensive Convolution Block module in YOLOv5 with the GhostBottleneckv2 module, the overall parameter count of the model is significantly reduced. Additionally, advocating the inclusion of the Convolutional Block Attention Module (CBAM) into the YOLOv5 backbone network to address the issue of small targets and significant scale variations in surface defects found on wind turbine blades. The YOLOv5 feature fusion module replaces the Feature Pyramid Network (FPN) structure with the Bidirectional Feature Pyramid Network (BiFPN) structure to enhance the fusion capability of multi-scale weighted features. Finally, the optimized model files underwent structured pruning through the application of pruning strategies, retraining fine-tuning, and knowledge distillation techniques. The experimental results on the self-made dataset of surface defects on wind turbine blades indicate that the optimized model has a parameter count that is only 48.79% of the small version of the YOLOv5 model. However, its mean Average Precision (mAP) and frame rate have increased by 5.05% and 8.1 frames/s, respectively, reaching 94.56% and 43.7 frames/s. The lightweight algorithm proposed in this study meets the accuracy and real-time requirements for surface defect detection on wind turbine blades.

A Longitudinal Network Analysis of Emotion Regulation, Interpersonal Problems, and Eating Disorder Psychopathology in Chinese Adolescents.

The present longitudinal study examined sex-specific, symptom-level relationships among emotion regulation (ER), interpersonal problems (IP), and eating disorder (ED) psychopathology in a large sample of Chinese adolescents. Data were from a project with four waves of data collection (N = 1540; 710 boys and 830 girls) at 6-month intervals over 18 months. Questionnaires assessed ED psychopathology, ER, and IP at each wave of data collection. Longitudinal network analyses were conducted separately for boys and girls. Sex differences in the network structures were also examined. The results revealed pronounced heterogeneity in the presentation of ED psychopathology, ER, and IP across Chinese adolescent boys and girls longitudinally and intra-individually. For example, weight/shape preoccupation in ED psychopathology and awareness in ER emerged as important nodes in the temporal network for boys. However, weight/shape preoccupation and dissatisfaction in ED psychopathology were identified as the most important nodes in the temporal network for girls. Regarding bridge strength, awareness in ER emerged as the node with the highest connectivity in the temporal network for boys. At the same time, weight/shape dissatisfaction in ED psychopathology was the node with the highest connectivity for girls. The current study extended network theory to better understand the longitudinal interplay among ER, IP, and ED psychopathology in Chinese adolescents and their sex differences in the importance of symptoms. Such insights may pave the way for developing targeted prevention and treatment strategies for adolescent boys and girls in China.

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

The effects of tropical cyclone on the container shipping network: A case study of typhoon Ma-on (2022)

As climate change intensifies, tropical cyclones pose increasing challenges to container shipping networks (CSN). However, research related in this area remains insufficient. In this study, we analyzed the effects of Typhoon Ma-on on the CSN using over 776 million Automatic Identification System (AIS) trajectory signals. Our findings reveal that the typhoon not only altered the network structure of the CSN but also affected the importance of container ports, vessel flow, and travel distance fluctuations along shipping routes. After the typhoon, the recovery of the network’s connectivity and stability, as well as travel time fluctuations, was less robust compared to other aspects. Additionally, the impact ranges for port importance, vessel flow, and travel distance fluctuations were 1,750 km, 500 km, and 2,250 km, respectively, following an inverse distance decay pattern. These findings offer new insights for stakeholders to enhance the effectiveness of disaster prevention and mitigation policies.

A Method for Quantifying Global Network Topology Based on a Mathematical Model

To facilitate a direct comparison of the differences in network resources among countries worldwide, this paper proposes a method for quantifying the relationship between autonomous systems and territorial networks from the perspective of network topology. Using global router-level network topology data as the foundational data for the network resources of various countries, we abstract the dual mapping information of router geographic distribution and operational ownership into a matrix-form mathematical model. By employing relevant indicators from both network scale and border connectivity, we compare matrix model data from different periods to quantitatively assess changes in the network structures of countries globally. The study results show that internet resources are concentrated in the United States, which owns 38.04% of the global routers, distributed across 87.88% of the countries, significantly impacting the global network. Compared to the average quantitative indicators of each country, 67.00% of the countries exhibit higher deployment consistency, 37.30% show higher border connection consistency, 23.81% perform prominently in terms of impact, and 46.20% have outstanding border node degrees. From a continental perspective, the analysis indicates that Asian and African countries have a closer relationship between AS and territorial networks, while Europe’s connections are relatively sparse. Over time, we observe a slight decline in deployment consistency in Asia, Africa, and Europe, a slight increase in border connection consistency in Asia, Africa, and North America, and enhanced impact in Asia, Africa, Europe, and South America. These trends suggest that the integration between AS and territorial networks is intensifying in most countries.

Double cross-linked biomass aerogels with enhanced mechanical strength and flame retardancy for construction thermal insulation

Biomass aerogels are expected to be a popular material in construction field due to their sustainability, eco-friendliness and excellent thermal insulation properties. In this paper, high modulus biomass aerogels based on the principle of double cross-linking of physics and chemistry were synthesized by freeze-drying technique using gelatin, sodium carboxymethyl cellulose, glutaraldehyde, phytic acid and diatomite as raw materials. The presence of a double cross-linked network structure endowed the prepared aerogels with a low thermal conductivity (0.021–0.029 W·m−1·k−1). The density of only 0.0865 g·cm−3 biomass aerogel exhibited an extrastrong compression modulus of 31.5 MPa, which was superior to common biomass aerogels. Biomass intumescent flame retardant system composed of sodium carboxymethyl cellulose (carbon source), gelatin (gas source) and phytic acid (acid source) was used in combination with diatomite to enhance flame retardancy (limit oxygen index value up to 36.5 %, UL-94 V-0 rating and total smoke production reduced from 0.31 m2 to 0.18 m2) and thermal stability (the residue up to 48.91 %). Remarkably, the modified aerogel showed incredible hydrophobicity (hydrophobic angle of 137°). In summary, the results indicated this study proposed a novel green idea for the preparation of construction thermal insulation materials with a combination of high modulus, flame retardancy and hydrophobicity.

Reviving collapsed plant-pollinator networks from a single species.

Mutualistic ecological networks can suddenly transition to undesirable states due to small changes in environmental conditions. Recovering from such a collapse can be difficult as restoring the original environmental conditions may be infeasible. Additionally, such networks can also exhibit a phenomenon known as hysteresis, whereby the system could exhibit multiple states under the same environmental conditions, implying that ecological networks may not recover. Here, we attempted to revive collapsed mutualistic networks to a high-functioning state from a single species, using concepts from signal propagation theory and an eco-evolutionary model based on network structures of 115 empirical plant-pollinator networks. We found that restoring the environmental conditions rarely aided in recovery of collapsed networks, but a positive relationship between recovering pollinator density and network nestedness emerged, which was qualitatively supported by empirical plant-pollinator restoration data. In contrast, network resurrection from a collapsed state in undesirable environmental conditions where restoration has minimal impacts could be readily achieved by perturbing a single species or a few species that controls the response of the dynamical networks. Additionally, nestedness in networks and a moderate amount of trait variation could aid in the revival of networks even in undesirable environmental conditions. Our work suggests that focus should be applied to a few species whose dynamics could be steered to resurrect entire networks from a collapsed state and that network architecture could play a crucial role in reviving collapsed plant-pollinator networks.

A model-based deep learning approach to interpretable impact force localization and reconstruction

Model-based and deep learning-based methods have been widely studied for force identification. However, model-based methods usually have high computational complexity and face challenges in parameter setting, while the generic network structures in deep learning methods are mostly designed empirically. In this paper, a model-based deep learning network is proposed to simultaneously localize and reconstruct impact f[orces. The proposed network, dubbed NSC-Net, is obtained by unrolling the iterative shrinkage-thresholding algorithm (ISTA) for the non-negative sparse coding (NSC) model. NSC-Net combines the interpretability of the model-based ISTA with the powerful parameter learning capability of deep learning. The transfer matrix is embedded into the network as physical information through learnable scaling. The structure of NSC-Net is based on explicit theoretical analysis, which enhances its interpretability in terms of structural design. In contrast to ISTA, the parameters in NSC-Net can be trained end-to-end from the training data without manual setting. Simulations and experiments on composite panels are conducted to validate the performance of the proposed method. The results demonstrate that NSC-Net accurately localizes and reconstructs impact forces, outperforming both ISTA and learned iterative shrinkage-thresholding algorithm (LISTA) network. Additionally, NSC-Net exhibits excellent noise immunity. Furthermore, the systematic approach of constructing an algorithm unrolling network for impact force identification is summarized, aiming to facilitate further related research.

Influence of climate, weather and floral associations on pollinator community composition across an elevational gradient

Insect pollinators, which are ectothermic, are especially sensitive to abiotic conditions, which often drive predictable patterns of pollinator species turnover along environmental gradients. However, pollinator activity is also reliant on suitable biotic conditions, such as the presence of host plants. High‐elevation environments provide a useful setting to examine the relative contribution of abiotic and biotic factors in shaping species interactions as they are often characterised by strong environmental gradients over short geographic distances. Here, we examined pollination interaction networks across an elevational gradient from 930–2000 m a.s.l. in southern Australia, to determine the underlying patterns of pollinator activity and their interactions with flowers. Interaction frequency of Diptera increased at high elevations, while interaction frequency of Hymenoptera and Coleoptera decreased. We provide evidence that this elevational pattern of activity is partly driven by floral associations, with interactions dominated by Hymenoptera‐attracting plant families at lower elevations (Proteaceae, Fabaceae) and a Diptera‐attracting family at high elevation (Asteraceae). Pollinator activity was also influenced by weather conditions, with reduced activity for all three orders at lower temperatures, and Diptera active across the broadest range of temperature, humidity and wind conditions. We suggest that changes across elevation gradients in pollinator community composition are driven by both direct responses to abiotic conditions such as temperature, as well as the elevational distribution patterns of associated flowering plants. Despite these distinct shifts in composition of the pollinator assemblage with elevation, pollination network structure was stable across the elevational gradient, with moderate levels of specialisation and low levels of connectance and nestedness present across the gradient. By considering both abiotic conditions and biotic processes, our results provide insight for predicting the impacts of upslope vegetation shifts on pollinator communities in the face of climate change.

Physics-informed neural networks for efficient aeroacoustic computational based on two time sequence prediction models

Physics-informed neural networks (PINNs) have been proven powerful for solving partial differential equations (PDEs). However, conventional PINNs fail handling time/space variables in the training data distinctly and overlook the temporal relationships among data points, which is critical for accurately modeling dynamic systems. To address this issue, this work proposes two novel PINN models based on the transformer and the convolutional long short-term memory (ConvLSTM) architectures, respectively. An encoder-decoder neural network structure is adopted to enhance handling of time-space variables and a high-order finite-difference method is used to compute partial derivatives. These computations are implemented using the convolution within the PyTorch framework that incorporates both physical and mathematical information into the neural networks. The efficacy of the proposed methods has been evaluated for the wave equation and the Reynolds-Averaged Navier-Stokes equations. Results suggest that the transformer-based PINN outperforms both the conventional and ConvLSTM-based PINN in terms of accuracy, efficiency and generalizability.

The impact of operating lease reporting rules on firm efficiency: the moderating role of supply chain network structure

Effective December 16, 2018, Accounting Standards Update (ASU) No. 2016–02 requires lessees to record most leases on their balance sheets as right-of-use (ROU) assets with corresponding lease liabilities. Drawing on resource dependence theory, this study examines the impact of ASU 2016–02 on firm efficiency and the moderating role of supply chain network properties (eigenvector centrality, betweenness centrality, and interconnectedness). Using a sample of 9,571 firms and 139,041 firm-year observations from 1971 to 2022, fixed-effects regression analyses reveal that ASU 2016–02 is positively associated with firm efficiency. Eigenvector centrality strengthens this relationship, while interconnectedness weakens it. However, betweenness centrality does not significantly moderate the relationship. These findings contribute to the literature by highlighting the role of accounting standards and supply chain network structure in shaping firm efficiency. The study offers practical insights for managers and stakeholders on navigating the impact of lease accounting changes and leveraging supply chain network properties to enhance operational efficiency.

Multi-scale spiking network model of human cerebral cortex.

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

Effect of ultrasound combined with highland barley dietary fiber on gel properties of reduced-salt chicken breast myofibrillar protein.

This study aimed to investigate the effect of ultrasound combined with highland barley dietary fiber (HBDF) on the quality of reduced-salt chicken breast myofibrillar protein (MP) gel. The molecular forces maintaining gel structure, the gelling formation process, and gel microstructure of different groups, two control groups (2% sodium chloride [NaCl] group, 1% NaCl group), and four treatment groups (0.3% HBDF+U5, 0.3% HBDF+U10, 0.5% HBDF+U5, and 0.5% HBDF+U10) were examined. Results indicated significant improvements (p<0.05) in gel properties such as water-holding capacity, textural characteristics, and color of the MP gel of the four treatment groups compared to Control 2 (1% NaCl) group. Furthermore, the second structural alterations were characterized by increase β-sheet, β-turn, and random coil structure contents in treatment groups, especially in 0.3% HBDF+U5 and 0.5% HBDF+U5 groups; in addition, the exposure of more hydrophobic groups and the formation of disulfide bonds and hydrogen bonds were promoted in treatment groups, thus enhancing protein aggregation and gel quality. Finally, compared to Control 2 (1% NaCl) group, more compact and uniform gel network structures and pores inside the composite gels were observed in treatment groups. In conclusion, the findings demonstrated that the application of ultrasound in combination with HBDF improved the gelling characteristics of reduced-salt chicken breast MP gel, especially 0.3% HBDF+U5 and 0.5% HBDF+U5 groups.

The fabrication, microstructure, rheological properties and interactions of soft solid oleogels of hazelnut oil body

Trans and saturated fatty acids in dietary solid fats have negative effects on human health, such as chronic diseases, making the development of a fat substitute an urgent matter. The plant derived oil body-hazelnut oil body (HOB) as the oil droplets, and the hydrogel formed by xanthan gum (XG) and type B gelatin (GEL) as the outer layer were used to encapsulate HOB and construct the oleogel. The OBC, microstructure, and rheological results indicated that the XG-GEL formed a sponge-like, tightly packed three-dimensional network structure on the surface of HOB, effectively preventing oil leakage. The hazelnut oil body oleogel exhibited 100% OBC, excellent deformation resistance, and thixotropic recovery ability. FTIR spectroscopy results revealed that changes in the amide I, II, III, and A bands indicated the formation of hydrogen bonds, with pronounced hydrogen bonding occurring between the amino/hydroxyl groups of HOB and the hydroxyl/carboxyl groups of XG-GEL. Raman spectroscopy results suggested that HOB-XG-GEL likely exhibited stronger hydrophobic interactions, while XG-GEL had no significant impact on the unsaturation level, membrane composition, or lipid acyl chain disorder of HOB. XRD results showed a single broad diffraction peak at 2θ = 19.6°, corresponding to π-π stacking interactions between HOB and XG-GEL. Interfacial characterization and zeta potential results demonstrated that HOB-XG-GEL had stronger surface activity and electrostatic repulsion. XRD and DSC results confirmed that the hazelnut oil body oleogel displayed an amorphous structure and good thermal stability. This study provides a new strategy for fabricating oil bodies to produce edible oleogels instead of solid fats.

Effect of salt and alkali on the viscoelastic behavior of noodle dough sheet with different wheat starch granule sizes

The underlying mechanisms of salt and alkali on the viscoelastic behaviors of noodle dough sheets with varied B/A-type starch ratios were investigated from water state, protein polymerization and conformation, and microstructure. The viscoelastic behaviors of dough sheets increased with increasing B/A-type starch ratio, regardless of the presence of salt and alkali, indicating the addition of salt and alkali did not change the effect law of starch granule size on dough viscoelasticity. The viscoelastic moduli of dough decreased in the presence of salt and alkali, and the effect was more obvious as the ratio of B/A-type starch decreased, which was mainly determined by the interactions of protein-protein, protein-starch, and starch-starch in dough systems. In the low ratio of B/A-type starch dough sheets, NaCl enhanced the non-covalent interactions and β-sheet structure, while alkali promoted the covalent cross-linking of protein. In the high ratio B/A-type starch dough samples, the big starch surface area provided by a high phase volume of starch granules led to the domination of starch-starch interactions over the protein phase, thus determining the viscoelastic behavior of dough. SEM images showed that NaCl caused the gluten to form a fibrous structure, while alkali induced a membrane-like and more closed structure. NaCl and alkali showed different influences on water distribution, molecular conformation, and network structure of dough sheets with varied B/A-type starch ratios and thus contributed to the different viscoelastic behaviors.

Enhanced emulsification properties of microalgae protein through gellan gum conjugation: Mechanistic insights and applications in curcumin encapsulation and delivery

The emulsification properties of microalgae protein (MP) are poor, especially under acidic and neutral conditions, which may limit the broad applications of MP in food processing. This study aims to explore the effects of gellan gum (GG) on the emulsification properties of MP. Firstly, MP-GG complexes were prepared and their structures characterized. Subsequently, MP-GG complexes stabilized emulsions were prepared and their stability evaluated. Finally, these emulsions were employed for the encapsulation and delivery of curcumin to evaluate their potential as an efficient nutrient delivery medium. Results indicated that MP-GG complexes were formed under various pH conditions, with pH 6 identified as optimal for complexes stability (zeta-potential value was −31 mV). UV–vis and fluorescence spectroscopy demonstrated that GG did not significantly alter the MP's structure but induced slight conformational changes, leading to the burial of some amino acid residues. Zeta potential measurements confirmed that MP-GG complexes were stabilized by strong electrostatic repulsions. The increase of GG content was conducive to providing more negative charge and promoting the dissolution and dispersion of the MP-GG complexes (MP: GG = 1: 1). Emulsions stabilized by MP-GG complexes exhibited smaller droplet sizes and improved stability compared to those stabilized by MP alone, especially at oil phase volume fractions of 60 % and 70 %. Rheological analysis indicated that GG enhanced emulsion stability by increasing viscosity, and higher oil phase volume fractions facilitated better MP-GG complexes adsorption on oil droplets, strengthening network structures of emulsions. During in vitro simulated gastrointestinal digestion, emulsions with a 70 % oil phase exhibited higher curcumin retention rate (31.09 %) and lower curcumin bioaccessibility (13.23 %) compared to those with a 60 % oil phase. This suggests that emulsions with higher oil phase volume fractions may be more suitable for colon-targeted curcumin delivery, with potential applications in promoting colon health. These findings confirm that the complexation of MP and GG was an effective way to improve the emulsification properties of MP. Emulsions stabilized by MP-GG complexes can serve as stable nutritional delivery systems for fat-soluble bioactive compounds.

An artificial neural network based approach for harmonic component prediction in a distribution line

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.