An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

Topical Application of a Vitamin A Derivative and Its Combination With Non-ablative Fractional Laser Potentiates Cutaneous Influenza Vaccination

Skin contains a large number of antigen presenting cells, making intradermal (ID) injection one of the most effective ways for vaccine administration. However, although current adjuvants may cause severe local reactions and inflammations in the skin, no adjuvant has been approved for ID vaccination so far. Here, we report that topical application of all-trans retinoic acid (ATRA), a vitamin A derivative produced in the human body, augmented cutaneous influenza vaccination. The adjuvant effects were evaluated in a murine vaccination/challenge model by using A/California/07/2009 pandemic vaccine (09V) or a seasonal influenza vaccine (SIV). ATRA drove a Th2-biased immune response, as demonstrated by profoundly elevated IgG1 titer rather than IgG2 titer. Combining ATRA with a non-ablative fractional laser (NAFL), which represents a new category of vaccine adjuvant utilizing physical stimuli to induce self-immune stimulators, further enhanced the efficacy of influenza vaccines with a more balanced Th1/Th2 immune response. The dual adjuvant strengthened cross-reactive immune responses against both homogenous and heterogeneous influenza viral strains. Analysis of gene expression profile showed that ATRA/NAFL stimulated upregulation of cytosolic nucleic acid sensors and their downstream factors, leading to a synergistic elevation of type I interferon expression. Consistent with this finding, knocking out IRF3 or IRF7, two key downstream regulatory factors in most nucleic acid sensing pathways, resulted in a significant decrease in the adjuvant effect of ATRA/NAFL. Thus, our study demonstrates that the self molecule ATRA could boost cutaneous influenza vaccination either alone or ideally in combination with NAFL

Helix grounding electrode with good grounding performance

Grounding electrode is used for discharging current and ensure the safe and stable operation of electrical equipment. Grounding performance of grounding electrode will degradation with limited installation space. This paper proposes helix grounding electrode and builds theoretically and simulating model on the helix grounding electrode. Analyzing grounding surface potential and step voltage distribution, grounding resistance and surface current density distribution. Results show helix grounding electrode can reduce grounding resistance, improve grounding surface potential distribution and the discharging current distribution

The Antihistamine Drugs Carbinoxamine Maleate and Chlorpheniramine Maleate Exhibit Potent Antiviral Activity Against a Broad Spectrum of Influenza Viruses

Influenza A viruses (IAV) comprise some of the most common infectious pathogens in humans, and they cause significant mortality and morbidity in immunocompromised people as well as children and the elderly. After screening an FDA-approved drug library containing 1280 compounds by cytopathic effect (CPE) reduction assay using the Cell Counting Kit-8, we found two antihistamines, carbinoxamine maleate (CAM) and S-(+)-chlorpheniramine maleate (SCM) with potent antiviral activity against A/Shanghai/4664T/2013(H7N9) infection with IC50 (half-maximal inhibitory concentration) of 3.56 and 11.84 μM, respectively. Further studies showed that CAM and SCM could also inhibit infection by other influenza A viruses, including A/Shanghai/37T/2009(H1N1), A/Puerto Rico/8/1934(H1N1), A/Guizhou/54/1989(H3N2), and one influenza B virus, B/Shanghai/2017(BY). Mice were challenged intranasally with A/H7N9/4664T/2013 (H7N9) virus and intraperitoneally injected with CAM (10 mg/kg per day) or SCM (1 mg/kg per day) for 5 days. CAM or SCM (10 mg/kg per day) were fully protected against challenge with A/Shanghai/4664T/2013(H7N9). The results from mechanistic studies indicate that both could inhibit influenza virus infection by blocking viral entry into the target cell, the early stage of virus life cycle. However, CAM and SCM neither blocked virus attachment, characteristic of HA activity, nor virus release, characteristic of NA activity. Such data suggest that these two compounds may interfere with the endocytosis process. Thus, we have identified two FDA-approved antihistamine drugs, CAM and SCM, which can be repurposed for inhibiting infection by divergent influenza A strains and one influenza B strain with potential to be used for treatment and prevention of influenza virus infection

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

Structure and Photoluminescent Properties of ZnO Encapsulated in Mesoporous Silica SBA-15 Fabricated by Two-Solvent Strategy

The two-solvent method was employed to prepare ZnO encapsulated in mesoporous silica (ZnO/SBA-15). The prepared ZnO/SBA-15 samples have been studied by X-ray diffraction, transmission electron microscope, X-ray photoelectron spectroscopy, nitrogen adsorption–desorption isotherm, and photoluminescence spectroscopy. The ZnO/SBA-15 nanocomposite has the ordered hexagonal mesostructure of SBA-15. ZnO clusters of a high loading are distributed in the channels of SBA-15. Photoluminescence spectra show the UV emission band around 368 nm, the violet emission around 420 nm, and the blue emission around 457 nm. The UV emission is attributed to band-edge emission of ZnO. The violet emission results from the oxygen vacancies on the ZnO–SiO2interface traps. The blue emission is from the oxygen vacancies or interstitial zinc ions of ZnO. The UV emission and blue emission show a blue-shift phenomenon due to quantum-confinement-induced energy gap enhancement of ZnO clusters. The ZnO clusters encapsulated in SBA-15 can be used as light-emitting diodes and ultraviolet nanolasers