Key Determinants Of Student Satisfaction When Undertaking Group Work

The increasing popularity of team structures in business environment coupled with the common practice of including group projects/assignments in university curricula means that business schools should direct efforts towards maximizing team as well as personal results. Yet, most frameworks for studying teams center exclusively on team level outcomes to address organizational needs. Far fewer studies have examined effectiveness at individual team member level in an educational context. The quantitative study on which this paper is based investigated the impact of team process on the effectiveness of individual satisfaction in group work amongst business students in Hong Kong with work group effectiveness and management educational literature providing the theoretical background. The study surveyed 489 university business students and revealed that all three team process factors, namely workload sharing, mutual support and communication play a positive and significant role in individual satisfaction in team settings

Cross-cultural Differences in Using Nonverbal Behaviors to Identify Indirect Replies

Open Access via the SpringerNature Agreement We thank Duan Wang, Xiran Zhou, Maria Sadlowska, and Eadie Elizabeth Cook for their help with data collection.Peer reviewe

New insights into the molecular mechanism of methanol-induced inactivation of Thermomyces lanuginosus lipase: A molecular dynamics simulation study

Methanol intolerance of lipase is a major limitation in lipase-catalyzed methanolysis reactions. In this study, to understand the molecular mechanism of methanol-induced inactivation of lipases, we performed molecular dynamics (MD) simulations of Thermomyces lanuginosus lipase (TLL) in water and methanol and compared the observed structural and dynamic properties. The solvent accessibility analysis showed that in methanol, polar residues tended to be buried away from the solvent while non-polar residues tended to be more solvent-exposed in comparison to those in water. Moreover, we observed that in methanol, the van der Waals packing of the core residues in two hydrophobic regions of TLL became weak. Additionally, the catalytically relevant hydrogen bond between Asp201 OD2 and His258 ND1 in the active site was broken when the enzyme was solvated in methanol. This may affect the stability of the tetrahedral intermediates in the catalytic cycle of TLL. Furthermore, compared to those in water, some enzyme surface residues displayed enhanced movement in methanol with higher Cα root-mean-square atomic positional fluctuation values. One of such methanol-affecting surface residues (Ile241) was chosen for mutation, and MD simulation of the I241E mutant in methanol was conducted. The structural analysis of the mutant showed that replacing a non-polar surface residue with an acidic one at position 241 contributed to the stabilization of enzyme structure in methanol. Ultimately, these results, while providing molecular-level insights into the destabilizing effect of methanol on TLL, highlight the importance of surface residue redesign to improve the stability of lipases in methanol environments

Particle Filtering Approach for GNSS RAIM and FPGA Implementation

The integrity monitoring system, as an integral part of aviation navigation system for global navigation satellite system (GNSS), should detect and isolated Failures or faults caused by system failures to maintain the integrity of the GNSS. The pseudorange residual noise of navigation satellites does not completely follow the Gaussian distribution, the performance of traditional filtering algorithms (such as the Kalman filtering) may be reduced due to non-Gaussian noise. The particle filter algorithm has great advantage to dealing with the nonlinear and non-Gaussian system. in this paper, the particle filter algorithm is applied to GNSS receiver autonomous integrity monitoring(RAIM) to detect the fault of navigation satellite. Firstly, Log likelihood ratio (LLR) testing is established; and then, the consistency between the state estimation of the main particle filter and the auxiliary particle filter is checked to determine whether the navigation satellite has failed; finally, the novel RAIM algorithm is undertaken by field programmable gate array (FPGA), the modules of the proposed RAIM algorithm is implemented. The effectiveness of the proposed approach is illustrated in a problem of GPS (Global Positioning System) autonomous integrity monitoring system, the algorithm and its implementation can be embeded in GNSS receiver

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.Comment: This work is submitted to IEEE International Conference on Data Engineering (ICDE) 202

Effective and Efficient Structural Inference with Reservoir Computing

peer reviewedU-AGR-6052 - IAS-AUDACITY Generic (01/07/2022 - 30/06/2026) - PANG Ju

On Efficient Reinforcement Learning for Full-length Game of StarCraft II

StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach involving extracted macro-actions and a hierarchical architecture of neural networks. We investigate a curriculum transfer training procedure and train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64 map and using restrictive units, we achieve a win rate of 99% against the level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat models, we achieve a 93% win rate against the most difficult non-cheating level built-in AI (level-7). In this extended version of the paper, we improve our architecture to train the agent against the cheating level AIs and achieve the win rate against the level-8, level-9, and level-10 AIs as 96%, 97%, and 94%, respectively. Our codes are at https://github.com/liuruoze/HierNet-SC2. To provide a baseline referring the AlphaStar for our work as well as the research and open-source community, we reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version of mAS is 1.07, which can be trained on the raw action space which has 564 actions. It is designed to run training on a single common machine, by making the hyper-parameters adjustable. We then compare our work with mAS using the same resources and show that our method is more effective. The codes of mini-AlphaStar are at https://github.com/liuruoze/mini-AlphaStar. We hope our study could shed some light on the future research of efficient reinforcement learning on SC2 and other large-scale games.Comment: 48 pages,21 figure