Determination of urotropine using cucurbit[7]uril-palmatine complex as a highly sensitive fluorescent probe

A new method for sensitive and selective determination of urotropine has been developed using the cucurbit[7]uril-palmatine complex as a fluorescent probe. The complex exhibits high fluorescence in aqueous solution, which is quenched in the presence of urotropine. The fluorescence quenching value, DF, is directly proportional to the concentration of urotropine in the range of 0.004–1.26 μg mL−1, with detection limit as sensitive as 0.0013 μg mL−1. The proposed method has been successfully applied to determine urotropine in food samples with good precision and accuracy. The stoichiometry and binding affinity as well as the nature of the binding behavior are determined using spectrofluorimetry, 1H NMR and molecular modeling theoretical calculations

Utilising Task-Patterns in Organisational Process Knowledge Sharing

Pattern based task management has been proposed as a promising approach to work experience reuse in knowledge intensive work environments. This paper inspects the need of organisational work experience sharing and reuse in the context of a real-life scenario based on use case studies. We developed a task pattern management system that supports process knowledge externalisation-internalisation. The system brings together task management related concepts and semantic technologies that materialise the former through a variety of semantic enhanced measures. Case studies were carried out for evaluating the proposed approach and also for drawing inspiration for future development

An improve RCB method based on microwave induced thermo acoustic tomography

Thermoacoustic imaging (TAI) is a non-ionizing and non-invasive imaging method, which combines the merits of high ultrasound imaging resolution with high microwave imaging contrast. In TAI, a short non-ionizing microwave pulse irradiates tissues to induce a small temperature rise, which consequently causes thermoelastic expansion to generate TA signals. By using an image reconstruction algorithm, TAI can then recover the microwave absorption distribution inside the tissue and further distinguish abnormal areas from background normal tissues. TAI for breast cancer detection is the main purpose of this study, the basic theory of TAI was introduced at first, especially the RCB reconstruction algorithm for TAI. After that, in this dissertation, two sets of thermoacoustic imaging systems were developed, which named thermoacoustic tomography and ultra-short pulse based high resolution TAI. According to experiments and theoretical studies carried out in this dissertation, the feasibility of thermoacoustic imaging method by Robust Capon Beam-former (RCB) for breast cancer detection before its clinical investigation is fully validated. Due to high imaging performance needs for the early detection of breast cancer. Based on the advantages of TAI, the potential applications of TAI for finger joints and brain diseases diagnosing are explored, which is opening up a new field for TAI

Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?

Recent breakthroughs in pre-trained code models, such as CodeBERT and Codex, have shown their superior performance in various downstream tasks. The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically. Recent studies reveal that even state-of-the-art pre-trained code models struggle with suggesting the correct APIs during code generation. However, the reasons for such poor API usage performance are barely investigated. To address this challenge, we propose using knowledge probing as a means of interpreting code models, which uses cloze-style tests to measure the knowledge stored in models. Our comprehensive study examines a code model's capability of understanding API fully qualified names from two different perspectives: API call and API import. Specifically, we reveal that current code models struggle with understanding API names, with pre-training strategies significantly affecting the quality of API name learning. We demonstrate that natural language context can assist code models in locating Python API names and generalize Python API name knowledge to unseen data. Our findings provide insights into the limitations and capabilities of current pre-trained code models, and suggest that incorporating API structure into the pre-training process can improve automated API usage and code representations. This work provides significance for advancing code intelligence practices and direction for future studies. All experiment results, data and source code used in this work are available at \url{https://doi.org/10.5281/zenodo.7902072}

Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech.Comment: Accepted to ICASSP 201