On Competition for Undergraduate Co-op Placement: A Graph Approach

The objective of this thesis is to improve the co-operative (co-op) education process by analyzing the relationships among academic programs in the context of the co-op job market. To do this, we propose and apply a novel graph-mining methodology. The input to our problem consists of student-job interview pairs, with each student labelled with his or her academic program. From this input, we build a weighted directed graph, which we refer to as a program graph, in which nodes correspond to academic programs and edge weights denote the percentage of jobs that interviewed at least one student from both programs. For example, a directed edge from the Computer Engineering program to the Electrical Engineering program with weight 0.36 means that of all the jobs that interviewed at least one Computer Engineering student, 36 percent of those jobs also interviewed at least one Electrical Engineering student. Thus, the larger the edge weight, the stronger the relationship and competition between particular programs. The output consists of various graph properties and analyses, particularly those which find nodes forming clusters or communities, nodes that are connected to few or many clusters, and nodes that are strongly connected to their immediate neighbours. As we will show, these properties have natural interpretations in terms of the relationships among academic programs and competition for co-op jobs. We applied the proposed methodology on one term of co-op interview data from a large Canadian university. We obtained interesting new insights that have not been reported in prior work. These insights can be beneficial to students, employers and academic institutions. Characterizing closely connected programs can help employers broaden their search for qualified students and can help students select programs of study that better correspond to their desired career. Students seeking a multi-disciplinary education can choose programs that are connected to other programs from many different clusters. Additionally, institutions can attend to programs that are strongly connected to (and face competition from) other programs by attracting more employers offering jobs in this area

Comprehensive Analysis, Discussion and Suggestion on the Current Situation of Cardiopulmonary Resuscitation and Automatic External Defibrillator in General Public in China

The number of sudden cardiac death (SCD) has increased year by year, which has become one of the main causes of death in China. Timely cardiopulmonary resuscitation (CPR) and timely and accurate use of automatic external defibrillator (AED) can greatly improve the survival rate of patients with sudden cardiac death. Because the large probability of sudden cardiac death occurs outside the hospital, it is very important for the general public to master first aid skills. This paper will mine all kinds of data from multi-dimensional and multi-angle, analyze the mastery of public first aid skills in China, and provide practical suggestions and ideas for popularizing first aid skills in the future

Molecular oxygen-assisted in defect-rich ZnO for catalytic depolymerization of polyethylene terephthalate

Polyethylene terephthalate (PET) is the most produced polyester plastic; its waste has a disruptive impact on the environment and ecosystem. Here, we report a catalytic depolymerization of PET into bis(2-hydroxyethyl) terephthalate (BHET) using molecule oxygen (O2)−assisted in defect-rich ZnO. At air, the PET conversion rate, the BHET yield, and the space-time yield are 3.5, 10.6, and 10.6 times higher than those in nitrogen, respectively. Combining structural characterization with the results of DFT calculations, we conclude that the (100) facet of defect-rich ZnO nanosheets conducive to the formation of reactive oxygen species (∗O2−) and Zn defect, promotes the PET breakage of the ester bond and thus complete the depolymerization processed. This approach demonstrates a sustainable route for PET depolymerization by molecule-assisted defect engineering.publishedVersio

Insights into the Roles of Midazolam in Cancer Therapy

With its high worldwide mortality and morbidity, cancer has gained increasing attention and novel anticancer drugs have become the focus for cancer research. Recently, studies have shown that most anesthetic agents can influence the activity of tumor cells. Midazolam is a γ-aminobutyric acid A (GABAA) receptor agonist, used widely for preoperative sedation and as an adjuvant during neuraxial blockade. Some studies have indicated the potential for midazolam as a novel therapeutic cancer drug; however, the mechanism by which midazolam affects cancer cells needs to be clarified. This systematic review aims to summarize the progress in assessing the molecular mechanism of midazolam as an anticancer agent

HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet, efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper, we present HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking, achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors, with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then, we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach, which significantly outperforms existing approaches in terms of optimization speed, rendering quality, and storage overhead

Causal associations between human gut microbiota and osteomyelitis: a Mendelian randomization study

BackgroundRecent studies have emphasized the role of gut microbiota in the onset and progression of osteomyelitis. However, the exact types of gut microbiota and their mechanisms of action remain unclear. Additionally, there is a lack of theoretical support for treatments that improve osteomyelitis by altering the gut microbiota.MethodsIn our study, we utilized the largest genome-wide association study (GWAS) meta-analysis to date from the MiBioGen consortium, involving 13,400 participants. The GWAS data for osteomyelitis were sourced from the UK Biobank, which included 4,836 osteomyelitis cases and 486,484 controls. We employed a two-sample Mendelian randomization framework for a detailed investigation into the causal relationship between gut microbiota and osteomyelitis. Our methods included inverse variance weighting, MR-Egger, weighted median, and weighted mode approaches. Additionally, we applied Cochran’s Q statistic to assess the heterogeneity of the instrumental variable.ResultsAt the class level, Bacilli and Bacteroidia were positively correlated with the risk of osteomyelitis. At the order level, only Bacteroidales showed a positive association with osteomyelitis. At the genus level, an increased abundance of Butyricimonas, Coprococcus3, and Tyzzerella3 was positively associated with the risk of osteomyelitis, whereas Lachnospira was negatively associated. Sensitivity analyses showed no evidence of heterogeneity or pleiotropy.ConclusionThis study reveals that classes Bacilli and Bacteroidia, order Bacteroidales, and genera Butyricimonas, Coprococcus3, and Tyzzerella3 are implicated in increasing the risk of osteomyelitis, while the genus Lachnospira is associated with a reduced risk. Future investigations are warranted to elucidate the precise mechanisms through which these specific bacterial groups influence the pathophysiology of osteomyeliti

Inhibition effect of solid products and DC breakdown characteristics of the HFO1234Ze(E)-N2-O2 ternary gas mixture

HFO1234ze(E) is an environmentally friendly SF6 substitute gas with prominent application potential. To suppress the generation of the HFO1234ze(E) solid decomposition products, which may cause great hazards to the gas–solid insulation strength, a gas mixing scheme screening method based on the reactive force field (ReaxFF) molecular dynamics (MD) simulation was innovatively proposed. The simulation results show that the inhibitory effect of O2 on the formation of HFO1234ze(E) solid products is better than those of CO2 and CF4. Further study shows that when O2 accounts for 3.33% of the gas mixture, the solid precipitate content is reduced by 48%. The experimental study shows that an O2 content of 3.33% can inhibit the generation of solid products by more than 50%. Besides, compared with HFO1234ze(E)-N2, the DC breakdown voltage of HFO1234ze(E)-N2-O2 is slightly increased, and the breakdown voltage dispersion degree and continuous breakdown voltage drop rate are decreased. This work gives a feasible solution for the suppression of HFO1234ze(E) solid decomposition products and provides an efficient method for solving similar problems of environmentally friendly insulating gas in C/F/O/N systems

Human Performance Modeling and Rendering via Neural Animated Mesh

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.Comment: 18 pages, 17 figure