Study Island

This thesis project is in providing a better distance learning experience for students. Distance learning can be considered a tangible and interactive learning experience. The disadvantage of distance learning is the lack of learning environment and interactivity, which may lead to problems such as inattentive students learning inefficiently. It follows the use of the Internet to create an interactive and interesting distance learning platform. Students experience a more real sense of effective learning efficiency, enhance communication between students, and better complete their learning plans with mutual help. Students also provide good contact information when they go home, enhancing communication between users and providing a better experience for distance learning

Rapid Diagnosis by Microfluidic Techniques

Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields

A DenseNet-based method for decoding auditory spatial attention with EEG

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNe

FHPM: Fine-grained Huge Page Management For Virtualization

As more data-intensive tasks with large footprints are deployed in virtual machines (VMs), huge pages are widely used to eliminate the increasing address translation overhead. However, once the huge page mapping is established, all the base page regions in the huge page share a single extended page table (EPT) entry, so that the hypervisor loses awareness of accesses to base page regions. None of the state-of-the-art solutions can obtain access information at base page granularity for huge pages. We observe that this can lead to incorrect decisions by the hypervisor, such as incorrect data placement in a tiered memory system and unshared base page regions when sharing pages. This paper proposes FHPM, a fine-grained huge page management for virtualization without hardware and guest OS modification. FHPM can identify access information at base page granularity, and dynamically promote and demote pages. A key insight of FHPM is to redirect the EPT huge page directory entries (PDEs) to new companion pages so that the MMU can track access information within huge pages. Then, FHPM can promote and demote pages according to the current hot page pressure to balance address translation overhead and memory usage. At the same time, FHPM proposes a VM-friendly page splitting and collapsing mechanism to avoid extra VM-exits. In combination, FHPM minimizes the monitoring and management overhead and ensures that the hypervisor gets fine-grained VM memory accesses to make the proper decision. We apply FHPM to improve tiered memory management (FHPM-TMM) and to promote page sharing (FHPM-Share). FHPM-TMM achieves a performance improvement of up to 33% and 61% over the pure huge page and base page management. FHPM-Share can save 41% more memory than Ingens, a state-of-the-art page sharing solution, with comparable performance

Semantic reconstruction of continuous language from MEG signals

Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language from Magnetoencephalography (MEG) signals recorded while subjects were listening to continuous speech. First, a multi-subject decoding model was trained using contrastive learning to reconstruct continuous word embeddings from MEG data. Subsequently, a beam search algorithm was adopted to generate text sequences based on the reconstructed word embeddings. Given a candidate sentence in the beam, a language model was used to predict the subsequent words. The word embeddings of the subsequent words were correlated with the reconstructed word embedding. These correlations were then used as a measure of the probability for the next word. The results showed that the proposed continuous word embedding model can effectively leverage both subject-specific and subject-shared information. Additionally, the decoded text exhibited significant similarity to the target text, with an average BERTScore of 0.816, a score comparable to that in the previous fMRI study

Occupational exposure in swine farm defines human skin and nasal microbiota

Anthropogenic environments take an active part in shaping the human microbiome. Herein, we studied skin and nasal microbiota dynamics in response to the exposure in confined and controlled swine farms to decipher the impact of occupational exposure on microbiome formation. The microbiota of volunteers was longitudinally profiled in a 9-months survey, in which the volunteers underwent occupational exposure during 3-month internships in swine farms. By high-throughput sequencing, we showed that occupational exposure compositionally and functionally reshaped the volunteers’ skin and nasal microbiota. The exposure in farm A reduced the microbial diversity of skin and nasal microbiota, whereas the microbiota of skin and nose increased after exposure in farm B. The exposure in different farms resulted in compositionally different microbial patterns, as the abundance of Actinobacteria sharply increased at expense of Firmicutes after exposure in farm A, yet Proteobacteria became the most predominant in the volunteers in farm B. The remodeled microbiota composition due to exposure in farm A appeared to stall and persist, whereas the microbiota of volunteers in farm B showed better resilience to revert to the pre-exposure state within 9 months after the exposure. Several metabolic pathways, for example, the styrene, aminobenzoate, and N-glycan biosynthesis, were significantly altered through our PICRUSt analysis, and notably, the function of beta-lactam resistance was predicted to enrich after exposure in farm A yet decrease in farm B. We proposed that the differently modified microbiota patterns might be coordinated by microbial and non-microbial factors in different swine farms, which were always environment-specific. This study highlights the active role of occupational exposure in defining the skin and nasal microbiota and sheds light on the dynamics of microbial patterns in response to environmental conversion

Mapping of Cu and Pb Contaminations in Soil Using Combined Geochemistry, Topography, and Remote Sensing: A Case Study in the Le’an River Floodplain, China

Heavy metal pollution in soil is becoming a widely concerning environmental problem in China. The aim of this study is to integrate multiple sources of data, namely total Cu and Pb contents, digital elevation model (DEM) data, remote sensing image and interpreted land-use data, for mapping the spatial distribution of total Cu and Pb contamination in top soil along the Le’an River and its branches. Combined with geographical analyses and watershed delineation, the source and transportation route of pollutants are identified. Regions at high risk of Cu or Pb pollution are suggested. Results reveal that topography is the major factor that controls the spatial distribution of Cu and Pb. Watershed delineation shows evidence that the streamflow resulting from rainfall is the major carrier of metal pollutants