‘Meet New VCD’ Immersive Art Gallery

Students of the Visual Communication Design (VCD) program may have a relatively distressing question about how to explain some details of this program to others. Someone will always ask how to define and explain the VCD. Therefore, in the spirit of redefining the VCD program and creating a platform to more intuitively demonstrate the current success of VCD students in visual design. The curriculum of this program and related student works are mainly user-centered design as the primary starting point. Innovation and forward-looking are necessary. To better communicate the VCD project and expand its influence. According to the questionnaire and other forms of research, it is trendy to use the media lab area to design a new user-brand experiential project that integrates graphics, interaction, and motion design. This project explores the combination of spatial visual effects and interactive experience in the media lab corridor area to create a digital art gallery that combines video images, lighting, and music. Three different content and visual experience are displayed through three different modes in different periods. It allows visitors to understand us more intuitively, understand the meaning of current interdisciplinary design disciplines, and more intuitively feel the beauty of art created by this digital gallery. Let people be connected with design, which brings unlimited fun to people. It can be a way of decompression, a game, a dream, or reality. It removes the distance between art and people, making people part of the artwork. Let the interactive installation not be just a simple video show, but a connection between people, art, and technology

A probabilistic linguistic thermodynamic method based on the water-filling algorithm and regret theory for emergency decision making

Since thermodynamics can describe the energy of matter and its form of storage or transformation in the system, it is introduced to resolve the uncertain decision-making problems. The paper proposes the thermodynamic decision-making method which considers both the quantity and quality of the probabilistic linguistic decision information. The analogies for thermodynamical indicators: energy, exergy and entropy are developed under the probabilistic linguistic circumstance. The probabilistic linguistic thermodynamic method combines the regret theory which captures decision makers’ regret-aversion and the objective weight of criterion obtained by the water-filling algorithm. The proposed method is applied to select the optimal solution to respond to the floods in Chongqing, China. The self-comparison is conducted to verify the effectiveness of the objective weight obtained by the water-filling algorithm and regret theory in the probabilistic linguistic thermodynamic method. The reliability and feasibility of the proposed method are verified by comparative analysis with other decision-making methods by some simulation experiments and non-parametric tests

A Study of L2 English L1 Chinese Native Speakers’ Acquisition of Chinese Topic-comment Constructions

The study conducted a research on L1 Chinese and L2 English speakers’ acquisition of Chinese topic-comment constructions. Several results were found. First, the type of the topic, the position of the topic, and the English proficiency did exert influence on Chinese native speakers’ perception of Chinese topic-comment constructions. To analyze Chinese native speakers’ perception of Chinese topic-comment constructions, three aspects need to be considered. Second, backward transfer from English to Chinese seemed to occur in high English proficiency group when they comprehended the Chinese topic-comment constructions. For the high English proficiency group, because of the backward transfer from English to Chinese, they seemed to have got used to subject-prominence feature of English and unlearnt the topic-prominence feature of Chinese. Therefore, when they encountered sentence that topic was placed in complement clause, they still felt acceptable. Another explanation is that they appeared to transfer the strategy used in processing English garden path sentences into Chinese, which facilitated their understanding of Chinese garden path sentences (in this study, it is the construction whose topic is in complement clause). Third, when participants, dealt with the constructions that moved-topics are in complement clause (Chinese garden path sentences), they tended to adopt the late closure strategy and minimal attachment strategy, which undermined their acceptability of this kind of sentences

Dynamic reference point method with probabilistic linguistic information based on the regret theory for public health emergency decision-making

Group emergency decision-making is an uncertain and dynamic process, in which the decision makers may be bounded rational and have a risk appetite. To depict the vague qualitative assessments, the probabilistic linguistic term sets are employed to express the perceptions of decision makers. First, considering the regret-aversion of the decision makers’ psychological characteristic, the value function and the regret-rejoice function in the regret theory are modified to adapt the probabilistic linguistic information. Second, the definition and aggregation operators of the probabilistic linguistic time variable are proposed to describe and aggregate the dynamic decision information. Third, the probabilistic linguistic models based on the dynamic reference point method and the regret theory are studied to maximise the expectation-levels of alternatives at the relative time point. The proposed method is applied to select the optimal response strategy for the outbreak of COVID-19 in China. Finally, the comparative analysis is designed to verify the applicability and reasonability of the proposed method

Study on the Inhibitory Effects of Ephedra Aconite Asarum

Dendritic cells (DCs) can secrete cytokines stimulated by lipopolysaccharide (LPS), which leads to not just acute inflammatory responses but also Th1 polarization. Furtherly, chronic inflammation or autoimmune diseases could be triggered. As a classic Traditional Chinese Medicine formula, Ephedra Aconite Asarum Decoction with the main ingredients of ephedrine and hypaconitine can show effect on anti-inflammation and immunoregulation. But it remains unclear whether Ephedra Aconite Asarum Decoction controls DCs. In this study, we investigated the effects of Ephedra Aconite Asarum Decoction on LPS-induced bone marrow-derived DCs (BMDCs) in vitro. We found that Ephedra Aconite Asarum Decoction lowered surface costimulators on DCs and reduced the expression of Th1 type cytokines. Yet it is slightly beneficial for shifting to Th2. Our work reveals that the Ephedra Aconite Asarum Decoction can regulate Th1 inflammation through intervening DCs

Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling

Word embedding has become ubiquitous and is widely used in various text mining and natural language processing (NLP) tasks, such as information retrieval, semantic analysis, and machine translation, among many others. Unfortunately, it is prohibitively expensive to train the word embedding in a relatively large corpus. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the stable vocabulary, relative idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Skip-Gram by four-five times in terms of efficiency, while the error generated by the random walk sampling is small

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species

miRNA expression differentiation induced by polyploidization in newly formed triploids of black poplar

During whole genomic duplication (WGD) events, micro RNAs (miRNAs) are involved in stabilization of chromatin and genome and epigenetic regulation of gene expression. In this study, a newly induced triploid group of hybrids between sect. Tacamahaca and sect. Aigeiros in Populus, was characterized for genome-wide miRNA expression after WGD. Seven miRNA libraries (male parent, female parent, group of triploid offspring’s, group of diploid offspring, and three triploid individuals) were constructed and variation of miRNA expression from diploid parents to triploid offspring’s as well as distinction between triploid and diploid offspring were analyzed. The results showed that a total of 240 miRNAs were predicted including 187 known miRNAs and 53 novel miRNAs. 81.25% of miRNAs in triploid offspring were non-additively expressed in which 52.31% were down-regulated. A novel miRNA with 24nt in length choosing adenine as its first base was found in triploid offspring group suggesting its potential role in regulation of DNA methylation after WGD. A total of 18 novel miRNAs were specifically expressed in the library of triploid group. Targeted genes of different expressed miRNAs in three comparison sets (triploid offspring group vs female parent, male parent, and diploid offspring group) were all enriched in ADP binding (GO: 0043531; FDR < 0.05). KEGG enrichment pathway of all three comparison sets was plant-pathogen interaction. This study revealed an essential role of miRNAs involving in epigenetic regulation after WGD in poplar and provided a good model for further studies of polyploidization advantages in woody plant

Optimization and evaluation of multi-bed adsorbent tube method in collection of volatile organic compounds

The feasibility of using adsorbent tubes to collect volatile organic compounds (VOCs) has been demonstrated since the 1990&#39;s and standardized as Compendium Method TO-17 by the U.S. Environmental Protection Agency (U.S EPA). This paper investigates sampling and analytical variables on concentrations of 57 ozone (O-3) precursors (C-2-C-12 aliphatic and aromatic VOCs) specified for the Photochemical Assessment Monitoring Station (PAMS). Laboratory and field tests examined multi-bed adsorbent tubes containing a sorbate combination of Tenax TA, Carbograph 1 TD, and Carboxen 1003. Analyte stabilities were influenced by both collection tube temperature and ambient O-3 concentrations. Analytes degraded during storage, while blank levels were elevated by passive adsorption. Adsorbent tube storage under cold temperatures (- 10 degrees C) in a preservation container filled with solid silica gel and anhydrous calcium sulfate (CaSO4) ensured sample integrity. A high efficiency (&gt; 99%) O-3 scrubber (i.e., copper coil tube filled with saturated potassium iodide [KM removed O-3 (i.e., &lt; 200 ppbv) from the air stream with a sampling capacity of 30 h. Water vapor scrubbers interfered with VOC measurements. The optimal thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) desorption time of 8 min was found at 330 degrees C. Good linearity (R-2 &gt; 0.995) was achieved for individual analyte calibrations (with the exception of acetylene) for mixing ratios of 0.08-1.96 ppbv. The method detection limits (MDLs) were below 0.055 ppbv for a 3 L sample volume. Replicate analyses showed relative standard deviations (RSDs) of &lt; 10%, with the majority of the analytes within &lt; 5%