An Empirical Study of Convenience, Usefulness, Customer Trust and Customer Loyalty in the Live Streaming Platforms

Purpose: This study examines Live Streaming Platforms in China for convenience, usefulness, customer trust, and customer loyalty. This study was created, analyzed, and summarized using secondary data analysis using an archival study approach to establish a research framework. Research design, data, and methodology: 25 Chinese respondents employed purposive sample techniques and used an online questionnaire to collect the data, then used zoom meetings to ask respondents' opinions. After collecting data, descriptive statistics were used to explain crucial aspects. Results: The results of convenience, usefulness, trust, and loyalty can explain Livestream shopping systems' potential. Live streaming shopping connects factories, suppliers, and consumers directly. Customers can deal with suppliers, influencers, steamers, or net-idols on pricing, quality, custom needs, promotions, discounts, etc. Influencers, steamers, or net-idols entertain and give ideas for selling things while live streaming. Conclusions: The research findings have met the research objectives. This study paper's weaknesses include its China-centric focus. The research findings may only apply to China and no other countries

Simulation and Analysis of Fluid-Solid Coupling of Wave Impact Sandcastle Based on COMSOL

It is a complex problem to study the interaction between sand castle and flowing water, which needs to consider the complexity of seawater flow and the stress of sand castle structure. The authors use the fluid-solid coupling model to establish the connection between the fluid field and the structural mechanical field, and use the finite element analysis to complete the simulation modeling of the transient process of wave impact and sandcastle foundation deformation. This paper analyzes the stress and the first principal strain of the sand castle foundation in the direction of flow velocity when the sand castle foundation is hit by waves, as a method to judge the strength of the sand castle.The best shape: the boundary value of sand castle collapse caused by strain have been determined, so as to obtain the maximum stress that a sand castle foundation can bear before collapse, which makes it possible to use the fatigue strength calculation theory of sand castle solid to carry out the quantitative calculation of sand castle durability. At the same time, the impact of waves is abstracted as wave motion equation. Finally, the finite element analysis technology is adopted to calculate the main strain of sandcastles of different shapes under the impact of the same wave, and through the comparison of the main strain, the authors get the sandcastle shape with the strongest anti-wave impact ability, which is the eccentric circular platform body.Affected by rain: the authors considered the effect of rainwater infiltration on the sandcastle's stress, and simplified the process of rain as a continuous and uniform infiltration of rain into the sandcastle's surface. The rain changes the gravity of the sand on the castle's surface. Simulation analysis is adopted to calculate the surface stress of sand castle with different degree of water seepage and different geometry. By comparison, it has been found that the smooth cone is more able to withstand the infiltration of rain without collapse.

MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning

Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.Comment: 21 pages, 9 figures, 10 table

Flames: Benchmarking Value Alignment of Chinese Large Language Models

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames

Comparison of single cell sequencing data between two whole genome amplification methods on two sequencing platforms

Abstract Research based on a strategy of single-cell low-coverage whole genome sequencing (SLWGS) has enabled better reproducibility and accuracy for detection of copy number variations (CNVs). The whole genome amplification (WGA) method and sequencing platform are critical factors for successful SLWGS (<0.1 × coverage). In this study, we compared single cell and multiple cells sequencing data produced by the HiSeq2000 and Ion Proton platforms using two WGA kits and then comprehensively evaluated the GC-bias, reproducibility, uniformity and CNV detection among different experimental combinations. Our analysis demonstrated that the PicoPLEX WGA Kit resulted in higher reproducibility, lower sequencing error frequency but more GC-bias than the GenomePlex Single Cell WGA Kit (WGA4 kit) independent of the cell number on the HiSeq2000 platform. While on the Ion Proton platform, the WGA4 kit (both single cell and multiple cells) had higher uniformity and less GC-bias but lower reproducibility than those of the PicoPLEX WGA Kit. Moreover, on these two sequencing platforms, depending on cell number, the performance of the two WGA kits was different for both sensitivity and specificity on CNV detection. The results can help researchers who plan to use SLWGS on single or multiple cells to select appropriate experimental conditions for their applications

Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 220,000 questions and accompanied by Xiezhi-Specialty and Xiezhi-Interdiscipline, both with 15k questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. We anticipate Xiezhi will help analyze important strengths and shortcomings of LLMs, and the benchmark is released in https://github.com/MikeGu721/XiezhiBenchmark .Comment: Under review of NeurIPS 202

High expression of DOCK2 indicates good prognosis in acute myeloid leukemia

DOCK family proteins are evolutionarily conserved guanine nucleotide exchange factors for Rho GTPase with different cellular functions. It has been demonstrated that DOCK1 had adverse prognostic effect in acute myeloid leukemia (AML). We first analyzed data of 85 AML patients who were treated with chemotherapy and had available DOCK1 to DOCK11 expression information and found that DOCK1 and DOCK2 had prognostic significance in AML. In view of the known prognosis of DOCK1 in AML, we then explored the prognostic role of DOCK2. One hundred fifty-six AML patients with DOCK2 expression data were extracted from The Cancer Genome Atlas (TCGA) database and enrolled in this study. Patients were divided based on treatment modality into the chemotherapy group and the allogeneic hematopoietic stem cell transplant (allo-HSCT) group. Each group was divided into two groups by the median expression levels of DOCK2. In the chemotherapy group, high DOCK2 expression was associated with longer event-free survival (EFS, P=0.001) and overall survival (OS, P=0.007). In the allo-HSCT group, EFS and OS were not significantly different between high and low DOCK2 expression groups. Multivariate analysis showed that high DOCK2 expression was an independent favorable prognostic factor for both EFS and OS in all patients (all

Expression and Role of the Calcium-Sensing Receptor in Rat Peripheral Blood Polymorphonuclear Neutrophils

The calcium-sensing receptors (CaSRs) play an important role in many tissues and organs that are involved in inflammatory reactions. Peripheral blood polymorphonuclear neutrophils (PMNs) are important inflammatory cells. However, the expression and functions of CaSR in peripheral blood PMNs are still not reported. In this study, we collected rat peripheral blood PMNs to observe the relationship between CaSR and PMNs. From the results, we found first that the CaSR protein was expressed in PMNs, and it increased after PMNs were activated with fMLP. In addition, CaSR activator cincalcet promoted the expression of CaSR and P-p65 (NF-κB signaling pathway protein) and Bcl-xl (antiapoptosis protein), and it increased the secretion of interleukin-6 (IL-6) and myeloperoxidase (MPO); meanwhile, it decreased proapoptosis protein Bax expression and the production of IL-10 and reactive oxygen species (ROS). At the same time, cincalcet also decreased the PMN apoptosis rate analyzed by flow cytometry. However, CaSR inhibitor NPS-2143 and NF-κB signaling pathway inhibitor PDTC reverse the results cited earlier. All of these results indicated that CaSR can regulate PMN functions and status to play a role in inflammation, which is probably through the NF-κB signaling pathway

Gestational Folic Acid Administration Alleviated Maternal Postpartum Emotional and Cognitive Dysfunction in Mice

Gestational folic acid (FA) supplementation has been widely recognized for its benefits in preventing offspring defects, but its effect on postpartum females has not yet been adequately assessed. The occurrence of emotional and cognitive dysfunction is common in postpartum women, and its treatment remains limited. Considering the promising results of FA in various psychiatric disorders both in human and redents, we tested the effect of gestational FA administration on postpartum psychiatric behavioral phenotypes and the implicated brain-related mechanisms in a murine model. FA was administered orally in both the hormone-stimulated-pregnancy (HSP) model and pregnant mice at doses of 1 and 5 mg/kg. Postpartum behavioral results showed that the disorders of cognitive performance, depressive, and anxiety-related behaviors were all alleviated in the 5 mg/kg FA group. However, the general development of their offspring remained unaffected. Immunofluorescence and immunoblot results revealed that FA pretreatment significantly activated the maternal hippocampal BDNF-related pathway. Morphological studies have confirmed that FA promotes hippocampal neurogenesis. Moreover, synaptic plasticity and synaptic transmission are enhanced. All of these hippocampal changes play critical roles in rescuing neuronal function and behaviors. Thus, our data suggest that gestational FA administration has a therapeutic effect that improves cognition and reduces depression and anxiety in a murine postpartum model. This may be developed as a preventive and adjuvant therapeutic option for pregnant women