Optimization of Independent College Students Evaluation of Teaching Assessment System Based on the PDCA Model

Student evaluation of teaching is one of the important ways of independent college teaching quality evaluation, which has a positive practical significance to promote independent college teaching reform, and improve the teaching quality. In recent years, at the same time in the further carry out the work of student evaluation of teaching, more and more problem is highlighted. In this paper, from two aspects of roles and work itself, analysis of the present status of the independent college student evaluation of teaching. On this basis, student evaluation of teaching is introducing the theory of PDCA cycle, which is continuously correcting problems found in student work, optimizing the evaluation working process, , so to promote the communication between teachers and students cooperation, to achieve real actual effect of student appraisal evaluation of teaching Keywords: PDCA；Student evaluation of teaching；Independent college；Optimization DOI: 10.7176/JEP/11-32-11 Publication date: November 30th 202

On the talent training mode of “new engineering” in local undergraduate colleges

Funding  source: Research on talent training mode of "new engineering" in local undergraduate colleges and universities (project code: 2019SJA2197) Abstract: With the development of China's new economy and emerging industries, new engineering construction comes into being. Facing the construction of new engineering, local undergraduate colleges and universities should deeply understand their own development trend, clarify the training mode of new engineering talents, and take the adjustment of specialty setting guided by regional industrial demand, the innovation of talent training mode based on the consensus of coordinated development, and the improvement of talent training quality as the main measures for local undergraduate colleges and universities to cultivate new engineering talents Key words: local; New engineering; Talent training mode DOI: 10.7176/JEP/12-33-15 Publication date: November 30th 202

Specialized Courses Teaching Mode Innovation of the Independent College Based on MOOCS

Independent college is a new kind of school-running pattern, on the basis of independent college computer professional course teaching, based on the background of MOOCS, specialized course teaching mode principle, on the basis of design is given priority to, the class online course of classroom teaching mode. To a certain extent can motivate we will accelerate reform of the teaching mode of independent colleges, improve the teaching quality of education. Keywords: Moocs, Independent college, Specialized courses, Teaching mod

Exploration on the Training Mode of Computer Professionals Based on the Concept of “New Engineering”

With the national industrial upgrading and technological innovation in recent years, the construction industry is leading in the direction of informatization, industrialization, intelligence and international integration, which puts forward new requirements for the current traditional mode of computer talent training. The innovation of talent training mode, the improvement of education and teaching, the improvement of education resources and so on have become the urgent problems of new engineering computer professional talent training Absolutely. This paper analyzes the current situation of "new engineering" talent demand and training, points out the shortcomings of the current computer talent training in the teaching concept, teaching mode, teachers and so on, and explores the new engineering computer talent training mode. And take the practice of Henan University School of civil engineering and architecture in the new engineering personnel training as an example, hope to have a certain reference significance for the new engineering computer professional personnel training. Keywords: new engineering; computer; interdisciplinary training; subject integration DOI: 10.7176/JEP/12-8-03 Publication date:March 31st 202

AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion

The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broad images since different images favor different features captured by different explicit regularizations. As such, this paper proposes a new adaptive and implicit low-rank regularization that captures the low-rank prior dynamically from the training data. The core of our new adaptive and implicit low-rank regularization is parameterizing the Laplacian matrix in the Dirichlet energy-based regularization, which we call the regularization \textit{AIR}. Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training. We validate AIR's effectiveness on various benchmark tasks, indicating that the AIR is particularly favorable for the scenarios when the missing entries are non-uniform. The code can be found at https://github.com/lizhemin15/AIR-Ne

3,3′-[Biphenyl-4,4′-diylbis(­oxy)]diphthalic acid

In the title mol­ecule, C28H18O10, the two central benzene rings form a dihedral angle of 31.0 (1)°. In the phthalic acid fragments, the carb­oxy groups in the meta positions are approximately coplanar with the attached benzene rings, being inclined to their planes at 2.7 (1) and 10.3 (1)°, while the carb­oxy groups in the ortho positions are twisted from the benzene ring planes by 83.5 (1) and 75.4 (1)°. In the crystal, O—H⋯O hydrogen bonds link the mol­ecules into layers parallel to the bc plane. Weak C—H⋯O hydrogen bonds and π–π inter­actions between the aromatic rings [centroid–centroid distance = 3.7674 (3) Å] further consolidate the crystal packing

Making Pre-trained Language Models Great on Tabular Prediction

The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is still far from being well exploited on tabular data prediction (e.g., regression or classification tasks). Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables. In this paper, we present TP-BERTa, a specifically pre-trained LM for tabular data prediction. Concretely, a novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names. Comprehensive experiments demonstrate that our pre-trained TP-BERTa leads the performance among tabular DNNs and is competitive with Gradient Boosted Decision Tree models in typical tabular data regime.Comment: Accepted to ICLR 2024 as spotlight presentation (Notable Top 5%). OpenReview link is https://openreview.net/forum?id=anzIzGZuLi, codes will be available at https://github.com/jyansir/tp-bert

A novel folate-modified self-microemulsifying drug delivery system of curcumin for colon targeting

Lin Zhang1*, Weiwei Zhu2*, Chunfen Yang1, Hongxia Guo1, Aihua Yu1, Jianbo Ji3, Yan Gao1, Min Sun1, Guangxi Zhai11Department of Pharmaceutical Engineering, College of Pharmacy, Shandong University, Jinan; 2Department of Pharmacy, Yantai Yuhuangding Hospital, Yantai; 3Department of Pharmacology, College of Pharmacy, Shandong University, Jinan, China*These authors contributed equally to the workBackground: The objective of this study was to prepare, characterize, and evaluate a folate-modified self-microemulsifying drug delivery system (FSMEDDS) with the aim to improve the solubility of curcumin and its delivery to the colon, facilitating endocytosis of FSMEDDS mediated by folate receptors on colon cancer cells.Methods: Ternary phase diagrams were constructed in order to obtain the most efficient self-emulsification region, and the formulation of curcumin-loaded SMEDDS was optimized by a simplex lattice experiment design. Then, three lipophilic folate derivatives (folate-polyethylene glycol-distearoylphosphatidylethanolamine, folate-polyethylene glycol-cholesteryl hemisuccinate, and folate-polyethylene glycol-cholesterol) used as a surfactant were added to curcumin-loaded SMEDDS formulations. An in situ colon perfusion method in rats was used to optimize the formulation of FSMEDDS. Curcumin-loaded FSMEDDS was then filled into colon-targeted capsules and the in vitro release was investigated. Cytotoxicity studies and cellular uptake studies was used in this research.Results: The optimal formulation of FSMEDDS obtained with the established in situ colon perfusion method in rats was comprised of 57.5% Cremophor® EL, 32.5% Transcutol® HP, 10% Capryol™ 90, and a small amount of folate-polyethylene glycol-cholesteryl hemisuccinate (the weight ratio of folate materials to Cremophor EL was 1:100). The in vitro release results indicated that the obtained formulation of curcumin could reach the colon efficiently and release the drug immediately. Cellular uptake studies analyzed with fluorescence microscopy and flow cytometry indicated that the FSMEDDS formulation could efficiently bind with the folate receptors on the surface of positive folate receptors cell lines. In addition, FSMEDDS showed greater cytotoxicity than SMEDDS in the above two cells.Conclusion: FSMEDDS-filled colon-targeted capsules are a potential carrier for colon delivery of curcumin.Keywords: curcumin, SMEDDS, folate receptor, colon targetin

Au@h-Al2O3 Analogic Yolk–Shell Nanocatalyst for Highly Selective Synthesis of Biomass-Derived D-xylonic Acid via Regulation of Structure Effects

Selective oxidation of biomass-based monosaccharides into value-added sugar acids is highly desired, but limited success of producing D-xylonic acid has been achieved. Herein, we report an efficient catalyst system, viz., Au nanoparticles anchored on the inner walls of hollow Al2O3 nanospheres (Au@h- Al2O3), which could catalyze the selective oxidation of D-xylose into D-xylonic acid under base-free conditions. The mesoporous Al2O3 shell as the adsorbent first adsorbed D-xylose. Then, the interface of Au nanoparticles and Al2O3 as active sites spontaneously dissociated O2, and the exposed Au nanoparticle surface as the catalytic site drove the transformation. With this catalyst system, the valuable D-xylonic acid was produced with excellent yields in the aerobic oxidation of D-xylose. Extensive investigation showed that Au@h- Al2O3 is an efficient catalyst with high stability and recyclability