Microencapsulation of omega-3 fatty acid rich oil via complex coacervation

The overall goal of this thesis was to use plant-based materials to encapsulate omega-3 oils to produce microencapsulated powders with improved stability against oxidative stresses. This research includes four studies (Chapter 3, 4, 5 & 6). Chapter 3 examined the complexation behaviour of lentil protein isolate (LPI) and carboxymethyl cellulose (CMC) with different degree of substitution (DS; 0.7, 0.9, and 1.2) and molar mass (MM; 90 and 250 kDa), and their thermodynamic properties. For complexation behaviour, max optical density was conducted at 4:1 LPI: CMC mixing ratio. MM and DS had no significant effect on critical pH values but impacted the size and number of complexes formed. The complexation reactions of all LPI-CMC mixtures at pH 3 was energetically favorable. Chapter 4 and 5 compared the complexation behaviour of LPI with various polysaccharides, including CMC, gum Arabic (GA), alginate (AL), ι-carrageenan (ι-C), and κ-carrageenan (κ-C), and the resulting emulsifying properties. For chapter 4, at 4:1 LPI-polysaccharide mixing ratio, LPI-GA and LPI-CMC mixtures formed coacervate-type of complexes, while precipitate-type of complexes were formed with LPI-AL and LPI-ι-C systems. Their resulting complexes at pHopt were used to make emulsions. LPI-ι-C emulsion displayed the highest emulsion stability (ES) due to its high emulsion viscosity, lower mean droplet sizes, and highly negative-charged droplets. For chapter 5, incorporating ι-C and κ-C into the LPI solution led to suppression of complexes formed. Emulsions prepared with the resulting soluble complexes at pH 6 showed significantly higher stability than those made with insoluble complexes at pH 3.5 for each sample. The greatest ES was attributed to 4:1 LPI-κ-C and LPI-ι-C emulsions at pH 6. Chapter 6 developed the LPI-polysaccharide based microcapsules to encapsulate flaxseed oil. LPI-κ-C and LPI-ι-C emulsions with maltodextrin at pH 6 were prepared, followed by spray-drying or freeze-drying to yield the dried capsules. Spray-dried capsules showed higher oil encapsulation efficiency, but the encapsulated oil was oxidized significantly due to heat effect during drying and lower water activity of the capsules. Flaxseed oil was stable in all freeze-dried capsules during 8 weeks of storage. For in vitro oil release profile, more oil was released from LPI-κ-C powders under simulated gastric fluid, but more oil was released from LPI-ι-C powders under subsequent simulated gastric fluid and simulated intestinal fluid regardless of drying method and oil content. This research suggested that there is great potential to use the resulting emulsions to make plant-based microcapsules to deliver omega-3 oils

Comparative study of machine learning and deep learning methods on ASD classification

The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of the two groups are analyzed, and networks of connections between brain regions were created. Several classification frameworks are developed to distinguish the connectivity patterns between the groups. The best models for statistical inference and precision were compared, and the tradeoff between precision and model interpretability was analyzed. Finally, the classification accuracy measures were reported to justify the performance of our framework. Our best model can classify autistic and healthy patients on the multisite ABIDE I data with 71% accuracy

Joint Detection Algorithm for Multiple Cognitive Users in Spectrum Sensing

Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.Comment: https://aei.ewapublishing.org/article.html?pk=e24c40d220434209ae2fe2e984bcf2c

Fabrication of three-dimensional microdisk resonators in calcium fluoride by femtosecond laser micromachining

We report on fabrication of on-chip calcium fluoride (CaF2) microdisk resonators using water-assisted femtosecond laser micromachining. Focused ion beam (FIB) milling is used to create ultra-smooth sidewalls. The quality (Q)-factors of the fabricated microresonators are measured to be 4.2x10^4 at wavelengths near 1550 nm. The Q factor is mainly limited by the scattering from the bottom surface of the disk whose roughness remains high due to the femtosecond laser micromachining process. This technique facilitates formation of on-chip microresonators on various kinds of bulk crystalline materials, which can benefit a wide range of applications such as nonlinear optics, quantum optics, and chip-level integration of photonic devices.Comment: 7 pages, 3 figure

On-chip electro-optic tuning of a lithium niobate microresonator with integrated in-plane microelectrodes

We demonstrate electro-optic tuning of an on-chip lithium niobate microresonator with integrated in-plane microelectrodes. First two metallic microelectrodes on the substrate were formed via femtosecond laser process. Then a high-Q lithium niobate microresonator located between the microelectrodes was fabricated by femtosecond laser direct writing accompanied by focused ion beam milling. Due to the efficient structure designing, high electro-optical tuning coefficient of 3.41 pm/V was observed.Comment: 6 pages, 3 figure

Subsidence monitoring of offshore platforms

AbstractThe normal subsidence monitoring technologies, used in civil engineering, are hard to apply in ocean engineering. Because it is hard to find a fixed reference for subsidence monitoring. A new method, which is suitable for subsidence monitoring of offshore platforms, is proposed in this paper. Firstly, the compression characteristic of the soil was analyzed and the harms of subsidence are discussed. Based on the analysis, the subsidence monitoring method was given. Finally, an real application is shown. Some advanced measurement technologies, such as the FBG strain measurement techniques and so on, were used in this application. The real application indicates that the new method is suitable for the subsidence monitoring of offshore platforms

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI