Learning-related Soft Skills Among Online Business Students in Higher Education: Grade Level and Managerial Role Difference in Self-Regulation, Motivation, and Social Skill

The purpose of the study was to investigate how undergraduate and graduate business management students, as well as those who had a managerial role in their career and who did not, differ on levels of soft skills (SRL strategies, motivation, and social skills) after gender was controlled. Moreover, we intended to investigate how well soft skills factors influence business students’ successes in an online learning environment after students’ individual characteristics and learning characteristics were controlled. To serve this purpose, this study conducted MANCOVA and hierarchical multiple regression analyses on data collected from 162 students in fully online business courses. First, the results of the study indicated that graduate students had higher level of soft skills than undergraduate students, especially in self-regulation and motivation. Likewise, students with managerial experiences demonstrated a higher level of soft skills. Next, hierarchical regression analysis revealed that the final regression model with all soft skills factors included could predict approximately 34% of the variance in students learning outcomes to a statistically significant level. In addition, goal setting, self-efficacy, and social skills were found to be significant predictors. We suggest that instructors and instructional designers should realize that soft skills are important contributor to the learning outcomes. Therefore, mechanisms to enhance student soft skills should be embedded into the online course in order to improve student learning outcomes. This should be especially a priority for undergraduate online courses because undergraduate students do not demonstrate higher soft skills compared to graduate students

Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202

A novel glue attachment approach for precise anchoring of hydrophilic EGCG to enhance the separation performance and antifouling properties of PVDF membranes

A novel glue attachment approach was proposed to form a durable hydration layer on a hydrophobic PVDF hollow fiber membrane (PVDF HFM) surface to improve its hydrophilicity and antifouling ability during wastewater filtration. The functional glue was synthesized from reclaimed styrene butadiene rubber (SBR) and a hydroxyl group was created with an epoxidation reaction (ESBR). The hydrophilic epigallocatechin-s-gallate (EGCG) was then precisely anchored via hydrogen bonding with multiple phenolic hydroxyl groups in the ESBR without penetrating into the inner matrix of the PVDF to prevent flux decline. The hydrophilicity of the PVDF membrane increased drastically and the water contact angle decreased from 62.7° to 45.1° with only a 25% decline in the pure water flux. Furthermore, due to precise anchoring of the EGCG, the modified EGCG-ESBR/PVDF membrane showed a higher pure water flux (110.6 L m−2h−1) and much higher BSA and oil (kerosene) rejection rates (approximately 94.5% and 99.5%, respectively) compared to membranes directly coated with EGCG (EGCG-PVDF). Moreover, the modified membrane also showed higher water flux recovery after multiple filtration cycles. This promising and efficient hydrophilic modification suggests great potential for application of the eco-friendly material in wastewater treatment.</p

Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/1/mp13497.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/2/mp13497_am.pd

Hypolipidemic Effects of Three Purgative Decoctions

In traditional Chinese medicine (TCM), purgation is indicated when a person suffers an illness due to the accumulation of evil internal heat. Obese individuals with a large belly, red face, thick and yellow tongue fur, constipation, and avoidance of heat are thought accumulates of evil internal heat, and they are also treated with purgatives such as Ta-Cheng-Chi-Tang (TCCT), Xiao-Chen-Chi-Tang (XCCT), and Tiao-Wei-Chen-Chi-Tang (TWCCT) by TCM doctors. In previous studies, our group found that TCCT has potent anti-inflammatory activity, and that XCCT is an effective antioxidant. Since rhubarb is the principle herb in these three prescriptions, we will first present a thorough review of the literature on the demonstrated effect (or lack of effect) of rhubarb and rhubarb-containing polyherbal preparations on lipid and weight control. We will then continue our research with an investigation of the anti-obesity and lipid-lowering effect of TCCT, XCCT, TWCCT, and rhubarb extracts using two animal models. TWCCT lowered the serum triglyceride concentration as much as fenofibrate in Triton WR-1339-treated mice. Daily supplementation with XCCT and TWCCT significantly attenuated the high-fat-diet-induced hypercholesterolemia in rats. In addition, TWCCT also significantly lowered the high-fat-diet-induced hypertriglycemia. Although feeding high-fat diet rats with these extracts did not cause loose stools or diarrhea or other deleterious effects on renal or hepatic function. None of these extracts lowered the body weight of rats fed on high-fat diet. In conclusion, the results suggest that XCCT and TWCCT might exert beneficial effects in the treatment of hyperlipidemia

Deep reinforcement learning for automated radiation adaptation in lung cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/1/mp12625.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/2/mp12625_am.pd

Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Image Translation

An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner. While the initial solutions to the I2I problem were provided by the generative adversarial neural networks (GANs), currently, diffusion models (DM) hold the state-of-the-art status on the I2I translation benchmarks in terms of FID. Yet, they suffer from some limitations, such as not using data from the source domain during the training, or maintaining consistency of the source and translated images only via simple pixel-wise errors. This work revisits the classic CycleGAN model and equips it with recent advancements in model architectures and model training procedures. The revised model is shown to significantly outperform other advanced GAN- and DM-based competitors on a variety of benchmarks. In the case of Male2Female translation of CelebA, the model achieves over 40% improvement in FID score compared to the state-of-the-art results. This work also demonstrates the ineffectiveness of the pixel-wise I2I translation faithfulness metrics and suggests their revision. The code and trained models are available at https://github.com/LS4GAN/uvcgan

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients’ clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health & Medicin

Gradient static-strain stimulation in a microfluidic chip for 3D cellular alignment

This is the published version. Copyright 2014 Royal Society of ChemistryCell alignment is a critical factor to govern cellular behavior and function for various tissue engineering applications ranging from cardiac to neural regeneration. In addition to physical geometry, strain is a crucial parameter to manipulate cellular alignment for functional tissue formation. In this paper, we introduce a simple approach to generate a range of gradient static strains without external mechanical control for the stimulation of cellular behavior within 3D biomimetic hydrogel microenvironments. A glass-supported microfluidic chip with a convex flexible polydimethylsiloxane (PDMS) membrane on the top was employed for loading the cells suspended in a prepolymer solution. Following UV crosslinking through a photomask with a concentric circular pattern, the cell-laden hydrogels were formed in a height gradient from the center (maximum) to the boundary (minimum). When the convex PDMS membrane retracted back to a flat surface, it applied compressive gradient forces on the cell-laden hydrogels. The concentric circular hydrogel patterns confined the direction of hydrogel elongation, and the compressive strain on the hydrogel therefore resulted in elongation stretch in the radial direction to guide cell alignment. NIH3T3 cells were cultured in the chip for 3 days with compressive strains that varied from ~65% (center) to ~15% (boundary) on hydrogels. We found that the hydrogel geometry dominated the cell alignment near the outside boundary, where cells aligned along the circular direction, and the compressive strain dominated the cell alignment near the center, where cells aligned radially. This study developed a new and simple approach to facilitate cellular alignment based on hydrogel geometry and strain stimulation for tissue engineering applications. This platform offers unique advantages and is significantly different from the existing approaches owing to the fact that gradient generation was accomplished in a miniature device without using an external mechanical source