Key Influencing Factors Affecting the Student Academic Performance and Student Satisfactions Ratings: Evidence from Undergraduate Students in China

This paper has developed a sound and practical method to evaluate the key teaching quality including the student academic performance and student satisfaction ratings. The method makes use of the existing data already readily available in a Chinese university, focusing on the identification of key influencing factors affecting the student academic performance and student satisfactions ratings. The data analyses have shown the university student academic performance is significantly affected student gender, age, previous academic performance, settlements and occupations of parents. There is significant difference in the student ratings for different genders and academic positions of teaching staff. The student performance and satisfaction ratings also significantly vary in different years of intakes and different Schools/programs. The student’s university academic performance can be accurately predicted using artificial neural networks with a prediction error of about 7%. This approach can help the university to improve the student academic performance and student satisfactions

Learning Dense UV Completion for Human Mesh Recovery

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW)

Visualization of lithium-ion transport and phase evolution within and between manganese oxide nanorods.

Multiple lithium-ion transport pathways and local phase changes upon lithiation in silver hollandite are revealed via in situ microscopy including electron diffraction, imaging and spectroscopy, coupled with density functional theory and phase field calculations. We report unexpected inter-nanorod lithium-ion transport, where the reaction fronts and kinetics are maintained within the neighbouring nanorod. Notably, this is the first time-resolved visualization of lithium-ion transport within and between individual nanorods, where the impact of oxygen deficiencies is delineated. Initially, fast lithium-ion transport is observed along the long axis with small net volume change, resulting in two lithiated silver hollandite phases distinguishable by orthorhombic distortion. Subsequently, a slower reaction front is observed, with formation of polyphase lithiated silver hollandite and face-centred-cubic silver metal with substantial volume expansion. These results indicate lithium-ion transport is not confined within a single nanorod and may provide a paradigm shift for one-dimensional tunnelled materials, particularly towards achieving high-rate capability

Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction

As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close to the human body. The naive focal length assumptions can harm this task with the incorrectly formulated projection matrices. To solve this, we propose Zolly, the first 3DHMR method focusing on perspective-distorted images. Our approach begins with analysing the reason for perspective distortion, which we find is mainly caused by the relative location of the human body to the camera center. We propose a new camera model and a novel 2D representation, termed distortion image, which describes the 2D dense distortion scale of the human body. We then estimate the distance from distortion scale features rather than environment context features. Afterwards, we integrate the distortion feature with image features to reconstruct the body mesh. To formulate the correct projection matrix and locate the human body position, we simultaneously use perspective and weak-perspective projection loss. Since existing datasets could not handle this task, we propose the first synthetic dataset PDHuman and extend two real-world datasets tailored for this task, all containing perspective-distorted human images. Extensive experiments show that Zolly outperforms existing state-of-the-art methods on both perspective-distorted datasets and the standard benchmark (3DPW)

Porous pyroelectric ceramic with carbon nanotubes for high-performance thermal to electrical energy conversion

The recycling of low-grade thermal energy from our surroundings is an environmental-friendly approach to contribute to sustainability, which remains a grand challenge. Herein, a high-performance porous pyroelectric ceramic formed using carbon nanotubes (CNT) is designed and fabricated using a modified solid-state reaction technique. Localized characterization of PMN-PMS-PZT and PMN-PMS-PZT with 0.3 wt% CNT additions by piezoelectric force microscopy suggests that the presence of porosity and defects in grains can restrict the reversal of domains and weaken the local piezoresponse; that is due to the influence of porosity on the electric field, domain morphology, or screening effects induced by defects at the pore surface. More importantly, the porous ceramics showed enhanced figure of merits, including voltage responsibility and energy harvesting figure of merit, compared to the dense ceramic. The harvested energy increased by 208% when the 0.3 wt% of CNT was added to produce porosity, which has a potential application in thermal energy harvesting and sensing system.</p

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X

Neuroprotective effect of arctigenin via upregulation of P-CREB in mouse primary neurons and human SH-SY5Y neuroblastoma cells.

Arctigenin (Arc) has been shown to act on scopolamine-induced memory deficit mice and to provide a neuroprotective effect on cultured cortical neurons from glutamate-induced neurodegeneration through mechanisms not completely defined. Here, we investigated the neuroprotective effect of Arc on H89-induced cell damage and its potential mechanisms in mouse cortical neurons and human SH-SY5Y neuroblastoma cells. We found that Arc prevented cell viability loss induced by H89 in human SH-SY5Y cells. Moreover, Arc reduced intracellular beta amyloid (Aβ) production induced by H89 in neurons and human SH-SY5Y cells, and Arc also inhibited the presenilin 1(PS1) protein level in neurons. In addition, neural apoptosis in both types of cells, inhibition of neurite outgrowth in human SH-SY5Y cells and reduction of synaptic marker synaptophysin (SYN) expression in neurons were also observed after H89 exposure. All these effects induced by H89 were markedly reversed by Arc treatment. Arc also significantly attenuated downregulation of the phosphorylation of CREB (p-CREB) induced by H89, which may contribute to the neuroprotective effects of Arc. These results demonstrated that Arc exerted the ability to protect neurons and SH-SY5Y cells against H89-induced cell injury via upregulation of p-CREB