Analytical applications of ionic liquids and determination of cell viability using capillary electrophoresis coupled with laser-induced fluorescence detection

Newly developed ionic liquids that are air and moisture stable have been subject to an increasing number of scientific investigations. Their recent applications include novel solvent systems and catalysts for organic synthesis, versatile electrolytes for electrochemical studies, and liquid-liquid extraction solvents. The potential usage of ionic liquids could be vast. The purpose of the first part of this dissertation is to address the novel applications of ionic liquids in the field of analytical chemistry. In this part, the author\u27s research can be divided into two directions: (a) examining the chromatographic performance of ionic liquids as gas chromatography (GC) stationary phases or solvents for GC stationary phases; (b) synthesizing new ionic liquids and testing their properties as matrices for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry.;In addition to multiple applications of ionic liquids, we also became interested in developing an effective instrumental method to assess the viability of microorganisms and mammalian cells. Since existing techniques, such as plate count methods, flow cytometry, etc., are either laborious or too expensive, highly efficient and more affordable methods are needed. Therefore, the second part of this dissertation is focused on the feasibility of using capillary electrophoresis (CE), in combination with fluorescent labeling technique, to determine cell viability. The author first adapted the recently developed highly efficient microbial CE method and viable fluorescence staining method to determine the viability of bacteria and yeast, and then carried out the potency study of animal sperm using a similar CE approach.;This dissertation is presented as two independent parts. Each part begins with a general introduction and literature review of recent progress in the specific research area. The following chapters are arranged in such a way that the related published papers or manuscripts are presented as separate chapters. All these chapters are presented in publication format. References for each chapter are independent and appear at the end of the chapter. The last chapter is general conclusions covering both parts of this dissertation

Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency in pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to define the inconsistency for the pseudo-labels and then minimize it, leading to pseudo-labels that maintain alignment across modalities and consistency within intra-modality structures. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the impact of noisy pseudo-labels while simultaneously aligning different modalities, coupled with a Modality-Invariant Representation Learning (MIRL) framework. Experiments demonstrate that our proposed method outperforms existing USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. The code will be available

Modelling the Influence of Geological Structures in Paleo Rock Avalanche Failures Using Field and Remote Sensing Data

This paper focuses on the back analysis of an ancient, catastrophic rock avalanche located in the small city of Lettopalena (Chieti, Italy). The integrated use of various investigation methods was employed for landslide analysis, including the use of traditional manual surveys and remote sensing (RS) mapping for the identification of geological structures. The outputs of the manual and RS surveys were then utilised to numerically model the landslide using a 2D distinct element method. A series of numerical simulations were undertaken to perform a sensitivity analysis to investigate the uncertainty of discontinuity properties on the slope stability analysis and provide further insight into the landslide failure mechanism. Both numerical modelling and field investigations indicate that the landslide was controlled by translational sliding along a folded bedding plane, with toe removal because of river erosion. This generated daylighting of the bedding plane, creating kinematic freedom for the landslide. The formation of lateral and rear release surfaces was influenced by the orientation of the discrete fracture network. Due to the presence of an anticline, the landslide region was constrained in the middle-lower section of the slope, where the higher inclination of the bedding plane was detected. The landslide is characterized by a step-path slip surface at the toe of the slope, which was observed both in the modelling and the field. This paper highlights the combined use of a geological model and numerical modelling to provide an improved understanding of the origin and development of rock avalanches under the influence of river erosion, anticline structures, and related faults and fractures

Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality data association problem for further heterogeneous joint learning. To address this issue, we propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations. Besides, we further propose a cross-modality neighbor consistency guided label refinement and regularization module, to eliminate the negative effects brought by the inaccurate supervised signals, under the assumption that the prediction or label distribution of each example should be similar to its nearest neighbors. Extensive experimental results on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art approach by a large margin of 7.76% mAP on average, which even surpasses some supervised VI-ReID methods

Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships between cross-modality clusters are not well explored. To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Specifically, we design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM) algorithm through optimizing the maximum matching problem in a bipartite graph. Then, the matched pairwise clusters utilize shared visible and infrared pseudo-labels during the model training. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Meanwhile, the cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the large modality discrepancy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art approaches by a large margin of 8.76% mAP on average

Comprehensive characterization of irradiation induced defects in ceria: Impact of point defects on vibrational and optical properties

Validation of multiscale microstructure evolution models can be improved when standard microstructure characterization tools are coupled with methods sensitive to individual point defects. We demonstrate how electronic and vibrational properties of defects revealed by optical absorption and Raman spectroscopies can be used to compliment transmission electron microscopy (TEM) and x-ray diffraction (XRD) in the characterization of microstructure evolution in ceria under non-equilibrium conditions. Experimental manifestation of non-equilibrium conditions was realized by exposing cerium dioxide (CeO2) to energetic protons at elevated temperature. Two sintered polycrystalline CeO2 samples were bombarded with protons accelerated to a few MeVs. These irradiation conditions produced a microstructure with resolvable extended defects and a significant concentration of point defects. A rate theory (RT) model was parametrized using the results of TEM, XRD, and thermal conductivity measurements to infer point defect concentrations. An abundance of cerium sublattice defects suggested by the RT model is supported by Raman spectroscopy measurements, which show peak shift and broadening of the intrinsic T2g peak and emergence of new defect peaks. Additionally, spectroscopic ellipsometry measurements performed in lieu of optical absorption reveals the presence of Ce3+ ions associated with oxygen vacancies. This work lays the foundation for a coupled approach that considers a multimodal characterization of microstructures to guide and validate complex defect evolution models

Evaluation of the Macrocyclic Antibiotic Avoparcin as a New Chiral Selector for HPLC

Avoparcin is a macrocyclic glycopeptide antibiotic structurally related to vancomycin, teicoplanin, and ristocetin A. When attached to 5-μm spherical silica gel, the avoparcin proved to be an effective chiral stationary phase (CSP) that could be used in the reversed-phase, normal- phase, and polar-organic modes. The avoparcin CSP was complimentary to the other macrocyclic glycopeptide CSPs in that it could resolve some racemates that the others could not, and vice versa. Some important compounds resolved on the avoparcin CSP include verapamil, thyroxine, mephenytoin, and 2- imidazolidone-4-carboxylic acid. The use of this CSP and the optimization of separations on it are discussed. Avoparcin appears to be a useful addition to this family of CSPs

How are the energy waves blocked on the way from hot to cold?

Representing the Center for Materials Science of Nuclear Fuel (CMSNF), this document is one of the entries in the Ten Hundred and One Word Challenge. As part of the challenge, the 46 Energy Frontier Research Centers were invited to represent their science in images, cartoons, photos, words and original paintings, but any descriptions or words could only use the 1000 most commonly used words in the English language, with the addition of one word important to each of the EFRCs and the mission of DOE energy. The mission of CMSNF to develop an experimentally validated multi-scale computational capability for the predictive understanding of the impact of microstructure on thermal transport in nuclear fuel under irradiation, with ultimate application to UO2 as a model syste

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level semantic watermarking algorithm based on locality-sensitive hashing (LSH), which partitions the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by an LLM, and conducts sentence-level rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. A margin-based constraint is used to enhance its robustness. To show the advantages of our algorithm, we propose a "bigram" paraphrase attack using the paraphrase that has the fewest bigram overlaps with the original sentence. This attack is shown to be effective against the existing token-level watermarking method. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on both common and bigram paraphrase attacks, but also is better at preserving the quality of generation