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Enhanced Stability and Bioavailability of Defatted Cricket Protein Hydrolysates Encapsulated in Alginate-Coated Liposomes
(2026-04-01)
Chotphruethipong, Lalita
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Benjakul, Soottawat
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Aluko, Rotimi E.
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Senphan, Theeraphol
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Hutamekalin, Pilaiwanwadee
The practical application of protein hydrolysates as functional food ingredients is frequently obstructed by their inherent structural instability. To circumvent this limitation, liposomal encapsulation has emerged as a sophisticated strategy to bolster the bioactivity and integrity of cricket-derived proteins. In this study, varying concentrations (1–4% w/v) of defatted cricket protein hydrolysate (DCPH) were integrated into vesicles composed of soy lecithin and cholesterol. The highest encapsulation efficiency (EE) was observed at a 2% DCPH loading capacity, yielding a significant result of 88.18% (p < 0.05). Subsequent coating with sodium alginate (SA) at 0.1–0.3% (w/v) resulted in an increase in particle size and a more pronounced negative surface charge. When maintained at 4 °C over a 24-day duration, the SA-coated liposome (SA-L-2%DCPH) exhibited superior stability compared to its uncoated (L-2%DCPH) counterpart. Also, the digest derived from the SA-L-2%DCPH exhibited significantly enhanced transepithelial permeability across the Caco-2 cell monolayer, indicated by the higher protein content and ABTS radical scavenging activity. Thus, sodium alginate-coated liposomes serve as a promising delivery system for encapsulating DCPH both during storage stability and in the gastrointestinal digestion system.
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Intelligence-Driven Grid-Forming Converter Control for Islanding Microgrids
(2025-01-01)
Ngamroo, Issarachai
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Surinkaew, Tossaporn
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Mitani, Yasunori
In modern microgrids (MGs) with high penetration of distributed energy resources (DERs), system reconfiguration occurs more frequently and becomes a significant issue. Fixed-parameter controllers may not handle these tasks effectively, as they lack the ability to adapt to the dynamic conditions in such environments. This paper proposes an intelligence-driven grid-forming (GFM) converter control method for islanding MGs using a robustness-guided neural network (RNN). To enhance the adaptability of the proposed method, traditional proportional-integral controllers in the GFM primary control loops are entirely replaced by the RNN. The RNN is trained by a robustness-guided strategy to replicate their robust behaviors. All the training stages are purely data-driven methods, which means that no system parameters are required for the controller design. Consequently, the proposed method is an intelligence-driven modelless GFM converter control. Compared with traditional methods, the simulation results in all testing scenarios show the clear benefits of the proposed method. The proposed method reduces overshoots by more than 71.24%, which keeps all damping ratios within the stable region and provides faster stabilization. In comparison to traditional methods, at the highest probability, the proposed method improves damping by over 14.7% and reduces the rates of change of frequency and voltage by over 59.97%. Additionally, the proposed method effectively suppresses the interactions between state variables caused by inverter-based resources, with frequencies ranging from 1.0 Hz to 1.422 Hz. Consequently, these frequencies contribute less than 19.79% To the observed transient responses.
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Formulation and application of clove essential oil-based nanoemulsion against Ruellia tuberosa L.
(2023-02-01)
Somala, N.
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Laosinwattana, C.
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Dimak, J.
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Teerarak, M.
Clove essential oil, a volatile compound, plays a role in herbicidal action on broadleaf weeds. Synthesis and technology nanoemulsion-based herbicide help protect the effectiveness of oils. The present study focused on the formulation, characteristics of clove essential oil-based nanoemulsion, and their herbicidal activity against Ruellia tuberosa L. The nanoemulsion containing clove essential oil was produced using the high-energy emulsification method with Tween 85 (HLB 11) and Span 85 (HLB 1.8) as an emulsifier agent (non-ionic surfactant), enabling the performance of clove oil nanoparticle size. The characteristic of nanoemulsion after being prepared 1 day showed that the droplet size, polydispersity index (PI) and zeta potential value was 152.5 nm, 0.162 and -32.68 mV, respectively. Even after 28 days, these characteristics have changed slightly. To confirm the droplet size, transmission electron microscope (TEM) images verified the clove oil-based nanoparticle size. Pre-emergence herbicidal activity under laboratory tests showed that the nanoemulsion at a concentration of 100, 200, 400 and 800 µL L-1 of oil reduced the germination and seedling growth of the tested weed. Inhibitory effect was absolutely at 800 µL L-1 of clove oil. Post-emergence application of nanoemulsion at a various concentration of 2000, 4000, 8000 and 10000 µL L-1 of oil was sprayed on 2-4 true leaves stage of tested weed. After foliar-applied nanoemulsion 24 h, the results showed that chlorophyll and carotenoid content decreased. Moreover, an increase in relative electrolyte leakage (REL) and malondialdehyde (MDA) indicated membrane disruption. This research supported that the clove oil-based nanoformulation may have a strong phytotoxic effect on R. tuberosa L. The nanoemulsion from clove oil is friendly to the environment and promote natural herbicide production for sustainable weed control.
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Dynamical model of rabies disease in human and dog
(2022-04-28)
Pongsumpun, Puntani
Rabies causes inflammation of the brain in humans and other mammals. This is a viral disease. Every year, there are about 59,000 people worldwide die from rabies. About 99 percent of them have been bitten by dogs. This study, we formulated the dynamical model consider the transmission of rabies disease. The most important animals which transmit this disease are dogs, cats and possibly other animals. We consider the transmission of rabies virus between human and dog populations. The dynamical model is separated into human and dog populations. The standard dynamical analysis is used to analyze this model. The local and global stabilities are analyzed.
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Impact of a Spun-Cast MoOxLayer on the Enhanced Moisture Stability and Performance-Limiting Behaviors of Perovskite Solar Cells
(2021-04-26)
Rosungnern, Unyamanee
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Kumnorkaew, Pisist
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Kayunkid, Navaphun
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Chanlek, Narong
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Li, Youyong
Organic-inorganic perovskite solar cells (PSCs), which have good environmental durability, are of great interest for practical applications. In this work, we show that a solution-processed MoOx layer acts as a buffer layer against high moisture stress to suppress defects in the perovskite and as a hole transport layer. The inversion of the photoinduced charge migration behaviors, that is, the electron preferentially moving toward the surface when MoOx is directly deposited onto the perovskite, is found to cause a significant loss in device functionality. The deposition of MoOx onto spiro-OMeTAD results in a lower photocurrent density-voltage (J-V) hysteresis behavior, a greatly enhanced electrical conductivity, and a significantly stabilized power conversion efficiency (PCE) when compared with those of devices without the MoOx layer. More importantly, the PCEs of the MoOx-based devices are retained at over 85% of their initial value, while only 75% is retained for a reference cell. This work highlights the facial fabrication approach of the solution-based MoOx layer and provides experimental evidence of the photogenerated charge migration behaviors on the perovskite/MoOx interface. This information would be beneficial for the further design and development of PSC technology.
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A system of functional equations satisfied by components of a quadratic function and its stability
(2019-10-01)
Ponpetch, Kanet
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Laohakosol, Vichian
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Mavecha, Sukrawan
A system of functional equations satisfied by the components of a quadratic function is derived via their corresponding circulant matrix. Given such a system of functional equations, general solutions are determined and a stability result for such a system is established.
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Parallel Active Power Filter Based on Source Current Detection for Antiparallel Resonance with Robustness to Parameter Variations in Power Systems
(2019-02-01)
Yeetum, Wanno
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Kinnares, Vijit
In previous research, the variable conductance of a parallel active power filter has been used for antiparallel resonance with parameter variations in power systems. Its disadvantages are many sensors, complicated harmonic detection, and complexity of the algorithm for successful accomplishment in antiparallel resonance. In order to eliminate such drawbacks, this paper presents the parallel active power filter based on source current detection for antiparallel resonance with robustness to parameter variations for power factor correction using capacitor banks. The proposed method employs only single harmonic detector and an open-loop shaping design for robustness to variations of source impedance and a sudden change of capacitor bank values for power factor correction. The proposed antiparallel resonance controller can be readily applied to both large-scale and laboratory-scale power systems without any change. The large-scale simulation and the laboratory-scale experiment results confirm the validity, the accuracy, and good performance for antiparallel resonance, harmonic suppression, and robustness to parameter variations in power systems.
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Kosambi-Cartan-Chern (KCC) theory for higher-order dynamical systems
(2016-02-01)
Harko, Tiberiu
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Pantaragphong, Praiboon
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Sabau, Sorin V.
The Kosambi-Cartan-Chern (KCC) theory represents a powerful mathematical method for the investigation of the properties of dynamical systems. The KCC theory introduces a geometric description of the time evolution of a dynamical system, with the solution curves of the dynamical system described by methods inspired by the theory of geodesics in a Finsler spaces. The evolution of a dynamical system is geometrized by introducing a nonlinear connection, which allows the construction of the KCC covariant derivative, and of the deviation curvature tensor. In the KCC theory, the properties of any dynamical system are described in terms of five geometrical invariants, with the second one giving the Jacobi stability of the system. Usually, the KCC theory is formulated by reducing the dynamical evolution equations to a set of second-order differential equations. In this paper, we introduce and develop the KCC approach for dynamical systems described by systems of arbitrary n-dimensional first-order differential equations. We investigate in detail the properties of the n-dimensional autonomous dynamical systems, as well as the relationship between the linear stability and the Jacobi stability. As a main result we find that only even-dimensional dynamical systems can exhibit both Jacobi stability and instability behaviors, while odd-dimensional dynamical systems are always Jacobi unstable, no matter their Lyapunov stability. As applications of the developed formalism we consider the geometrization and the study of the Jacobi stability of the complex dynamical networks, and of the Cold Dark Matter (CDM) cosmological models, respectively.

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