Non-Abelian Groups with Perfect Order Subsets

The purpose of this paper is to explore non-abelian finite groups with perfect order subsets. A finite group is said to have perfect order subsets (POS) if the number of elements of each given order can divide the order of the group. The study of such groups was initiated by Carrie E. Finch and Lenny Jones. In this paper, we construct POS-groups by considering semi-direct products of cyclic groups (and sometimes quaternions)

The Impact of the COVID-19 on Online Food Delivery Service: Evidence from China

The COVID-19 has had a profound effect on society as a whole. To examine the effect of the COVID-19 on online food delivery services, we collected sales data from a large online food delivery platform in 195 Chinese cities from November 2019 to July 2020. Interrupted time series analysis and time-varying difference-in-difference methods were used to estimate the impact of the COVID-19 and city lockdown policies on online food delivery services. The COVID-19 had a considerable negative effect on the online food delivery services. Lockdown policies caused further disruptions. As the pandemic and lockdown policies ended, the negative impacts dissipated. This finding reflected digital channels’ resilience to the catering industry during the pandemic and helped it withstand its impact. There were significant differences among urban characteristics. The government can formulate relevant policies to deal with potential public health risks in the future based on these findings

Judge’s Advice Utilization: Whose Advice is More Persuasive, AI or Human?

In recent years, especially with the development of Generative AI, more and more people seek advice from AI application when they make important decisions like career choice. The trend raises an important question: Do judges prefer to rely on human or AI advice in different advising scenarios? Although this topic has been discussed variously in research on algorithm appreciation and algorithm aversion, there are still some gaps need to be filled. Based on belief revision theory and the judge-advisor system, this study attempts to explore how advice strategy types (clinical vs. actuarial) and feedback inconsistency will affect judges’ perceived advice utilization when the advisor is different (Human vs. AI). To achieve this objective, a scenario-based online experiment will be carried out to collect data and test our research model

Exploring Users Motivations to Knowledge Contribution at the Creation Stage of Online Communities

The motivation of online community users’ contribution behavior has captured the attention of many scholars in various disciplines. But little empirical research has studied user behaviors according to the different stages of an online community. Based on Iriberri et al. (2009)’s life cycle model of online community, our study specifically focuses on the users’ contribution behavior at the creation stage of an online community. Some constructs of previous studies like trust and online-identity are not able to explain users’ behavior in our context, because identity and trust relationship are not established until growth and mature stage. Given the uniqueness of early participants and online community lifecycle, our study integrates three theoretical perspectives (need fulfillment theory, task-technology fit model and self-verification theory) to propose a research model to understand the participation motives. Furthermore, we introduced a moderator of group-level uniqueness to the self- verification theory

Experimental study and mass transfer modelling for extractive desulfurization of diesel with ionic liquid in microreactors

Conventional hydrodesulfurization technology was limited to treat aromatic heterocyclic sulfur compounds in ultralow-sulfur diesel. Extractive desulfurization (EDS) using ionic liquid (IL) exhibited good performance to address these issues, except for its long extraction time (15-40 min). To address this, microreactor was adopted to intensify the IL-based EDS, where dibenzothiophene was extracted from model diesel (MD) as the continuous phase to 1-butyl-3-methylimidazolium tetrafluoroborate as the dispersed phase under segmented flow (which appeared preferably at capillary numbers lower than 0.01). The effects of temperature, residence time and flow rate ratio on the desulfurization efficiency were investigated. The extraction equilibration time could be shortened from more than 15 min in conventional batch extractors to 120 s in microreactors. The extraction process was modeled according to the two-film model applied within a unit cell of the segmented flow, where the mass transfer resistance was considered primarily on the film side of the IL droplet. The mechanism for the improved EDS performance at higher temperatures or larger IL to MD flow ratios was investigated and validated, which was related to the significant increase in the diffusion coefficient or the specific interfacial area. These findings may shed important insights into the precise manipulation of IL-based EDS for a better process design and reactor optimization

Wounding triggers MIRO-1 dependent mitochondrial fragmentation that accelerates epidermal wound closure through oxidative signaling.

Organisms respond to tissue damage through the upregulation of protective responses which restore tissue structure and metabolic function. Mitochondria are key sources of intracellular oxidative metabolic signals that maintain cellular homeostasis. Here we report that tissue and cellular wounding triggers rapid and reversible mitochondrial fragmentation. Elevated mitochondrial fragmentation either in fzo-1 fusion-defective mutants or after acute drug treatment accelerates actin-based wound closure. Wounding triggered mitochondrial fragmentation is independent of the GTPase DRP-1 but acts via the mitochondrial Rho GTPase MIRO-1 and cytosolic Ca2+. The fragmented mitochondria and accelerated wound closure of fzo-1 mutants are dependent on MIRO-1 function. Genetic and transcriptomic analyzes show that enhanced mitochondrial fragmentation accelerates wound closure via the upregulation of mtROS and Cytochrome P450. Our results reveal how mitochondrial dynamics respond to cellular and tissue injury and promote tissue repair

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue

Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents. However, their performance lag behind general use cases in some expertise domains, such as Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue data. These models lack the ability for doctor-like proactive inquiry and multi-turn comprehension and cannot always align responses with safety and professionalism experts. In this work, we introduce Zhongjing, the first Chinese medical LLaMA-based LLM that implements an entire training pipeline from pre-training to reinforcement learning with human feedback (RLHF). Additionally, we introduce a Chinese multi-turn medical dialogue dataset of 70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly enhances the model's capability for complex dialogue and proactive inquiry initiation. We define a refined annotation rule and evaluation criteria given the biomedical domain's unique characteristics. Results show that our model outperforms baselines in various capacities and matches the performance of ChatGPT in a few abilities, despite having 50x training data with previous best model and 100x parameters with ChatGPT. RLHF further improves the model's instruction-following ability and safety.We also release our code, datasets and model for further research