Fusion Bonding Recipes for Glass-Glass Nanofluidic Devices

Engineering (The Ohio State University Denman Undergraduate Research Forum)Glass is a desired material for microfluidic and nanofluidic chips, due to chemical inertness, temperature stability, and optical clarity. Given the desire to maintain chemical uniformity for flow conduits, it is well known that thermal fusion bonding is the preferred method to bond two distinct glass substrates. Furthermore, thermal fusion bonding is known to achieve higher bond strength compared to other bonding techniques (anodic bonding, etc.). The purpose of this research is to develop a reliable recipe for successful fusion bonding of all-glass nanochannels with the aspect ratios described later. In this research, an inverted Y-channel is used as a model system with nanochannels at depths varying from 80 nm to 450 nm fabricated via standard lithography and wet etching. The width of the channel is 100 µm at the entry of the channel and reduces to 50 µm at each bifurcated leg. The channel is etched on a glass slide which is subsequently capped with microscope slide containing drilled holes (acting as reservoirs), to seal the channel from the ambient environment. In this research, we have investigated thermal bonding parameters including the variation of temperature, the use of weights to apply a constant pressure on glass slides, and the use of surface activation processes. Our results indicate that sealed glass-glass channels can be bonded at a temperature of 600℃ over 10 hours along with simultaneous application of weight over the bonding area (a load of 1.14 kg corresponds to a pressure of 9.3 kPa applied over the entire area of the channel containing cover glass). The devices will be used for subsequent electrical manipulation of ions and molecules.College of EngineeringAcademic Major: Mechanical Engineerin

Factors influencing consumer intention to electric car sharing program in China

Time-shared rental has been developed as an emerging model in the area of sharing economy in recent years in China. Since the technology of Internet of Things (IoT) and new energy automobile evolved, there are more and more people paying attention to innovative transportation such as eco-car sharing. As attitude towards eco-car, which may profoundly influence the industry, is on the turning point, the purpose of this dissertation is to examine what influence consumers` intention on e-car sharing and to understand how to better increase consumer intention to use time-shared rental. A 14-item survey was cited and developed to collect data from China delivered through Internet. Responses were collected from 183 participants. The results show that PEOU, GSI and CID have positive impact on intention to use time-shared rental while PE doesn`t associate with it. At the same time, the analysis of moderating roles indicates that neither age nor gender has moderating effect on the relationships between intention and the predicted variables. However, the income level of participants was identified to be the moderator of PE to intention, which is slightly positive.O aluguel de time-shared foi desenvolvido como um modelo emergente na área de economia compartilhada nos últimos anos na China. Com a evolução prosseguida da tecnologia da Internet of Things (IoT) e da nova energia automóvel, há cada vez mais pessoas a prestar atenção ao transporte inovador, tal como a eco-car sharing. Como a atitude em relação ao eco-car poderá ter grande impacto na indústria, este novo transporte ainda está num ponto de viragem. O objetivo desta dissertação é examinar o que é que influencia a intenção do consumidor em e-car sharing e entender como melhorar a intenção do consumidor em uso o aluguel de time-shared. Uma pesquisa de 14 itens foi citada e desenvolvida para recolher os dados que são transmitidas pela Internet. As respostas foram obtidas de 183 participantes. Os resultados mostram que PEOU, GSI e CID têm um impacto positivo na intenção de uso do aluguel time-shared enquanto o PE não se associa a ele. Ao mesmo tempo, a análise dos papéis moderadores indica que nem a idade nem o gênero têm efeito moderador nas relações entre a intenção e as variáveis previstas. No entanto, o nível de rendimento dos participantes foi identificado como sendo o moderador da PE para intenção, o que é ligeiramente positivo

Prefix-Tuning Based Unsupervised Text Style Transfer

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In this paper, we employ powerful pre-trained large language models and present a new prefix-tuning-based method for unsupervised text style transfer. We construct three different kinds of prefixes, i.e., \textit{shared prefix, style prefix}, and \textit{content prefix}, to encode task-specific information, target style, and the content information of the input sentence, respectively. Compared to embeddings used by previous works, the proposed prefixes can provide richer information for the model. Furthermore, we adopt a recursive way of using language models in the process of style transfer. This strategy provides a more effective way for the interactions between the input sentence and GPT-2, helps the model construct more informative prefixes, and thus, helps improve the performance. Evaluations on the well-known datasets show that our method outperforms the state-of-the-art baselines. Results, analysis of ablation studies, and subjective evaluations from humans are also provided for a deeper understanding of the proposed method

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Sense: Model Hardware Co-design for Accelerating Sparse CNN on Systolic Array

Sparsity is an intrinsic property of convolutional neural network(CNN) and worth exploiting for CNN accelerators, but extra processing comes with hardware overhead, causing many architectures suffering from only minor profit. Meanwhile, systolic array has been increasingly competitive on CNNs acceleration for its high spatiotemporal locality and low hardware overhead. However, the irregularity of sparsity induces imbalanced workload under the rigid systolic dataflow, causing performance degradation. Thus, this paper proposed a systolicarray-based architecture, called Sense, for sparse CNN acceleration by model-hardware co-design, achieving large performance improvement. To balance input feature map(IFM) and weight loads across Processing Element(PE) array, we applied channel clustering to gather IFMs with approximate sparsity for array computation, and co-designed a load-balancing weight pruning method to keep the sparsity ratio of each kernel at a certain value with little accuracy loss, improving PE utilization and overall performance. Additionally, Adaptive Dataflow Configuration is applied to determine the computing strategy based on the storage ratio of IFMs and weights, lowering 1.17x-1.8x DRAM access compared with Swallow and further reducing system energy consumption. The whole design is implemented on ZynqZCU102 with 200MHz and performs at 471-, 34-, 53- and 191-image/s for AlexNet, VGG-16, ResNet-50 and GoogleNet respectively. Compared against sparse systolic-array-based accelerators, Swallow, FESA and SPOTS, Sense achieves 1x-2.25x, 1.95x-2.5x and 1.17x-2.37x performance improvement on these CNNs respectively with reasonable overhead.Comment: 14 pages, 29 figures, 6 tables, IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEM

MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response

Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains relatively unexplored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT with the frozen LLaMA language model, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q&A datasets, we created the MusicInstruct (MI) dataset from MusicCaps, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs. Our introduced dataset enables notable advancements beyond previous ones

Perindopril and a Galectin-3 Inhibitor Improve Ischemic Heart Failure in Rabbits by Reducing Gal-3 Expression and Myocardial Fibrosis

Objective: Ventricular remodeling is considered the basis of heart failure and is involved in myocardial fibrosis. This study aimed to assess perindopril and a galectin-3 inhibitor (modified citrus pectin, MCP) for their effects on ventricular remodeling and myocardial fibrosis in rabbits with ischemic heart failure.Methods: Rabbits were divided into sham, heart failure (model), MCP, and perindopril groups, respectively. A rabbit model of ischemic heart failure was established by ligating the anterior descending coronary artery. Then, the rabbits were orally administered MCP, perindopril, or saline (all at 2 ml/kg/d) for 4 weeks. Sham animals only underwent open heart surgery without further treatment. After 4 weeks, cardiac function was examined by ultrasound, and myocardial Gal-3, collagen type I, and collagen type III expression was assessed, at the gene and protein levels, by real-time PCR and Western-Blot, respectively; serum Gal-3 was detected by ELISA, and fibrosis in the infarct zone was evaluated by H&E and Masson staining.Results: In model animals, myocardial Gal-3, collagen type I, and collagen type III gene and protein expression levels were increased compared with control values, as well as serum Gal-3 amounts. Treatment with perindopril and MCP significantly alleviated the above effects, with no significant differences between the treatment groups. Pathological analyses showed that compared with model animals, treatment with MCP or perindopril resulted in relatively neatly arranged myocardial cells in the infarct zone, with significantly decreased fibrosis.Conclusion: Perindopril and the galectin-3 inhibitor MCP comparably improve ischemic heart failure in rabbits, by downregulating Gal-3 and reducing myocardial fibrosis