THE PROSODY-SYNTAX INTERACTION IN THE "YI-BU-QI-BA" RULE: A MORPHOLOGICALLY CONDITIONED TONE CHANGE IN MANDARIN CHINESE

This thesis investigates the prosody-syntax interaction in morpheme specific tone change, presents a systematic comparison between tone Sandhi and tone change, and proposes a new rule-based analysis for the Mandarin "yi-bu-qi-ba" rule. The empirical data is the comparison between Mandarin 3rd Tone Sandhi (3TS) of numeral morpheme wu (`five') and yi (`one') tone change (YTC). Data shows that syntax and prosody do not function the same way in predicting 3TS and YTC. The basic domain of 3TS is a foot whereas the domain of YTC is a phonological word (pword). This thesis also contributes a new analysis for the "yi-bu-qi-ba" rule, with three major claims: (1) YTC only applies when yi is the first element of a pword except in the string of digits. (2) BTC only applies when bu is the first element of a pword. (3) No tone change rule applies to qi and ba any more.Master of Art

MULTI-VESSELS COLLISION AVOIDANCE STRATEGY FOR AUTONOMOUS SURFACE VEHICLES BASED ON GENETIC ALGORITHM IN CONGESTED PORT ENVIRONMENT

An improved genetic collision avoidance algorithm is proposed in this study to address the problem that Autonomous Surface Vehicles (ASV) need to comply with the collision avoidance rules at sea in congested sea areas. Firstly, a collision risk index model for ASV safe encounters is established taking into account the international rules for collision avoidance. The ASV collision risk index and the distance of safe encounters are taken as boundary values of the correlation membership function of the collision risk index model to calculate the optimal heading of ASV in real-time. Secondly, the genetic coding, fitness function, and basic parameters of the genetic algorithm are designed to construct the collision avoidance decision system. Finally, the simulation of collision avoidance between ASV and several obstacle vessels is performed, including the simulation of three collision avoidance states head-on situation, crossing situation, and overtaking situation. The results show that the proposed intelligent genetic algorithm considering the rules of collision avoidance at sea can effectively avoid multiple other vessels in different situations

Review of Energy Transition Policies in Singapore, London, and California

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively

STF-based diagnosis of AUV thruster faults

The diagnosis of thruster faults of autonomous underwater vehicles is studied in this paper. Based on the theory of strong tracking filter (STF), the AUV motion model and the thruster fault model are established. The STFs are designed for each thruster for the purpose of fault diagnosis. The AUV state and the fault deviation of the thruster are estimated online before the thruster faults are diagnosed based on residual analysis. The simulation experiments were conducted to verify the feasibility and effectiveness of the STF-based diagnosis of AUV thruster faults

LncRNA MALAT1 Promotes Cancer Metastasis in Osteosarcoma via Activation of the PI3K-Akt Signaling Pathway

Background/Aims: LncRNAs have been reported to be vital regulators of the progression of osteosarcoma, although the underlying mechanisms are not completely understood. Methods: The levels of MALAT1 and miR-129-5p expression were measured using qRT-PCR. Cell growth was determined using the CCK-8 and colony formation assays. Cell migration and invasion were detected using the wound healing and Transwell invasion assays, respectively. Tumor growth was determined with a xenograft model. Results: MALAT1 was significantly up-regulated in osteosarcoma tissues compared with adjacent non-tumor soft tissues. Overexpression of MALAT1 promoted osteosarcoma cell proliferation, migration, and invasion in vitro and enhanced tumor growth in a tumor xenograft mouse model. MALAT1 promoted osteosarcoma progression by modulating stem cell-like properties. Moreover, rescue experiment and luciferase reporter assay results indicated that MALAT1 modulates RET expression by sponging miR-129-5p in osteosarcomas. Furthermore, MALAT1 augmented the expression of downstream proteins of the RET-Akt pathway. MALAT1 was consistently significantly increased in osteosarcoma tissues and MALAT1 expression was positively correlated with tumor size and metastasis. High expression of MALAT1 was significantly associated with poor outcomes in patients with osteosarcomas. MALAT1 expression was positively related to RET and negatively related to miR-129-5p in osteosarcoma samples and xenograft tumors. MALAT1 functioned as an oncogenic lncRNA in osteosarcomas and was as an independent prognostic indicator. Conclusion: Our data revealed for the first time that MALAT1 increases stem cell-like properties by up-regulating RET via sponging miR-129-5p, and thus activates the PI3K-Akt signaling pathway and provides potential therapeutic targets for osteosarcoma treatment

Dual-emission single sensing element-assembled fluorescent sensor arrays for the rapid discrimination of multiple surfactants in environments

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal–organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent “fingerprints”, which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis