Collaborative driving mode of sustainable marketing and supply chain management supported by metaverse technology

In this article, we aim to explore the relationship between sustainable marketing and supply chain management (SCM) under the background of metaverse technology to realize the sustainable development of enterprises. First, this study deeply studies the influence of metaverse technology on sustainable marketing strategy from the theoretical level. Second, it deeply discusses the integration of digital transformation and sustainable development in SCM. Finally, this study implements a collaborative driving model of sustainable marketing and SCM supported by metaverse. By designing and analyzing the questionnaire on the sustainable performance of enterprises, it is found that SCM, cooperation with customers, investment recovery, sustainable marketing, R&D and design, production, and manufacturing have a significant positive influence on the sustainable performance of enterprises (p<0.01). In addition, the distribution and retail in sustainable marketing negatively impact the sustainable performance of enterprises, and the standardization coefficient is −0.225 (p<0.05). These research results emphasize the importance of sustainable marketing and SCM, which jointly promote enterprises to achieve sustainable performance, and ultimately provide valuable practical guidance for building a sustainable digital economy and contribute to collaborative optimization in enterprise engineering

Two free boundary problems in optimal investment

Master'sMASTER OF SCIENC

Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

The mushroom, Cordyceps cicadae, ameliorates renal interstitial fibrosis via TLR2-mediated pathways

Purpose: To evaluate the mushroom, Cordyceps cicadae, for its ability to suppress tissue fibrosis and Toll-like receptors 2 (TLR 2) pathway activation in a mouse model of renal interstitial fibrosis (RIF).Methods: Cordyceps cicadae powder was obtained from BioAsia Group (Shanghai, China). RIF was induced via unilateral ureteral obstruction (UUO) in male C57Bl/6 mice. Animals were treated via the intragastric administration of Cordyceps cicadae powder (0.1g, 0.3 g/ml/100 g/day), beginning 24 h prior to UUO, and the treatment was continued for the following 14 days. Changes in tissue histology were then assessed via hematoxylin and eosin, and Sirius red stainings. Tissue macrophages were characterized based upon their expression of inducible nitric oxide synthase (iNOS) and interleukin-10 (IL-10), while Western blotting technique was used to measure the levels of TLR2, Myeloid differentiation factor 88 (MyD88), and nuclear factor-κB (NF-κB)/p-NF-κB in samples from these animals.Results: Treatment with Cordyceps cicadae powder is associated with a shift in macrophage phenotype that in turn decreased the production of extracellular matrix and alleviated RIF occurrence in mice model.Conclusion: This mechanistic study highlights the novel potential approach for treating and preventing RIF using Cordyceps cicadae powder. Keywords: Renal interstitial fibrosis, TLR2-mediated pathway, Cordyceps cicada

Investigating the Role of Coil Designs and Anatomical Variations in Cerebellar TMS

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that is used for treating various neurological disorders such as major depressive disorder. TMS has been gaining popularity in the field of neurostimulation of the cerebellum, since the cerebellum is a complex structure connected with almost the entire central nervous system and TMS has promise for non-invasively probing cerebellar function. Recent studies have discovered that the cerebellum plays an important role not only in motor planning and behavior but also in the cognitive domain. However, few studies have explored how different coil designs and anatomical variations affect the effectiveness of cerebellar TMS. Therefore, in this paper, we investigated the effects of cerebellar TMS with different coil designs positioning on several locations. Finite-element modeling was conducted with Figure-8 coil and D-B80 coil. Each coil was positioned in the center, 1 and 3 cm to the left of center of the cerebellum, and all the locations were tangential to the scalp at a distance of 5 mm. Furthermore, 50 MRI derived head models were used in the computer modeling to examine how anatomical variations affect the distribution and intensity of electric field in cerebellar TMS

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings

Despite the great success of neural visual generative models in recent years, integrating them with strong symbolic knowledge reasoning systems remains a challenging task. The main challenges are two-fold: one is symbol assignment, i.e. bonding latent factors of neural visual generators with meaningful symbols from knowledge reasoning systems. Another is rule learning, i.e. learning new rules, which govern the generative process of the data, to augment the knowledge reasoning systems. To deal with these symbol grounding problems, we propose a neural-symbolic learning approach, Abductive Visual Generation (AbdGen), for integrating logic programming systems with neural visual generative models based on the abductive learning framework. To achieve reliable and efficient symbol assignment, the quantized abduction method is introduced for generating abduction proposals by the nearest-neighbor lookups within semantic codebooks. To achieve precise rule learning, the contrastive meta-abduction method is proposed to eliminate wrong rules with positive cases and avoid less-informative rules with negative cases simultaneously. Experimental results on various benchmark datasets show that compared to the baselines, AbdGen requires significantly fewer instance-level labeling information for symbol assignment. Furthermore, our approach can effectively learn underlying logical generative rules from data, which is out of the capability of existing approaches

SIRT1 Is a Potential Drug Target for Treatment of Diabetic Kidney Disease

Multiple studies have demonstrated a critical role of Sirtuin-1 (SIRT1) deacetylase in protecting kidney cells from cellular stresses. A protective role of SIRT1 has been reported in both podocytes and renal tubular cells in multiple kidney disease settings, including diabetic kidney disease (DKD). We and others have shown that SIRT1 exerts renoprotective effects in DKD in part through the deacetylation of transcription factors involved in the disease pathogenesis, such as p53, FOXO, RelA/p65NF-κB, STAT3, and PGC1α/PPARγ. Recently we showed that the podocyte-specific overexpression of SIRT1 attenuated proteinuria and kidney injury in an experimental model of DKD, further confirming SIRT1 as a potential target to treat kidney disease. Known agonists of SIRT1 such as resveratrol diminished diabetic kidney injury in several animal models. Similarly, we also showed that puerarin, a Chinese herbal medicine compound, activates SIRT1 to provide renoprotection in mouse models of DKD. However, as these are non-specific SIRT1 agonists, we recently developed a more specific and potent SIRT1 agonist (BF175) that significantly attenuated diabetic kidney injury in type 1 diabetic OVE26 mice. We also previously reported that MS417, a bromodomain inhibitor that disrupts the interaction between the acetyl-residues of NF-κB and bromodomain-containing protein 4 (BRD4) also attenuates DKD. These results suggest that SIRT1 agonists and bromodomain inhibitors could be potential new therapuetic treatments against DKD progression

Open charm production in sNN\sqrt{s_{NN}}=200 GeV Au+Au collisions

We report on the measurement of D meson production from the analysis of their hadronic ($D^0\to K\pi$) and semileptonic ($D\to \mu+X$, $D\to e+X$) decays in $\sqrt{s_{NN}}$=200 GeV Au+Au collisions. The transverse momentum ($p_T$) spectra and the nuclear modification factors for $D^0$ and for electron/muon from charm semileptonic decays will be presented. The differential cross section $d\sigma/dy$ is found to be consistent with the number of binary scaling. The blast-wave fit suggests that the charm hadron freeze out earlier than other light flavor hadrons.Comment: 4 pages, 2 figures, presentation at Strangeness in Quark Matter 2006, accepted for publication by Journal of Physics