Online Cooperative Promotion and Cost Sharing Policy under Supply Chain Competition

This paper studies online cooperative promotion and cost sharing decisions in competing supply chains. We consider a model of one B2C e-commerce platform and two supply chains each consisting of a supplier and an online retailer. The problem is studied using a multistage game. Firstly, the e-commerce platform carries out the cooperative promotion and sets the magnitude of markdown (the value of e-coupon). Secondly, each retailer and his supplier determine the fraction of promotional cost sharing when they have different bargaining power. Lastly, the retailers decide whether to participate in the cooperative promotion campaign. We show that the retailers are likely to participate in the promotion if consumers become more price-sensitive. However, it does not imply that the retailers can benefit from the price promotion; the promotion decision game resembles the classical prisoner’s dilemma game. The retailers and suppliers can benefit from the cooperative promotion by designing an appropriate cost sharing contract. For a supply chain, the bargaining power between supplier and retailer, consumer price sensitivity, and competition intensity affect the fraction of the promotional cost sharing. We also find that equilibrium value of e-coupon set by the e-commerce platform is not optimal for all the parties

Prevalence of Low Bone Mass and Osteoporosis in Ireland: the Dual‐Energy X‐Ray Absorptiometry (DXA) Health Informatics Prediction (HIP) Project

Osteoporosis is a common disease that has a significant impact on patients, healthcare systems, and society. World Health Organization (WHO) diagnostic criteria for postmenopausal women were established in 1994 to diagnose low bone mass (osteopenia) and osteoporosis using dual‐energy X‐ray absorptiometry (DXA)‐measured bone mineral density (BMD) to help understand the epidemiology of osteoporosis, and identify those at risk for fracture. These criteria may also apply to men ≥50 years, perimenopausal women, and people of different ethnicity. The DXA Health Informatics Prediction (HIP) project is an established convenience cohort of more than 36,000 patients who had a DXA scan to explore the epidemiology of osteoporosis and its management in the Republic of Ireland where the prevalence of osteoporosis remains unknown. In this article we compare the prevalence of a DXA classification low bone mass (T‐score < −1.0) and of osteoporosis (T‐score ≤ −2.5) among adults aged ≥40 years without major risk factors or fractures, with one or more major risk factors, and with one or more major osteoporotic fractures. A total of 33,344 subjects met our study inclusion criteria, including 28,933 (86.8%) women; 9362 had no fractures or major risk factors, 14,932 had one or more major clinical risk factors, and 9050 had one or more major osteoporotic fractures. The prevalence of low bone mass and osteoporosis increased significantly with age overall. The prevalence of low bone mass and osteoporosis was significantly greater among men and women with major osteoporotic fractures than healthy controls or those with clinical risk factors. Applying our results to the national population census figure of 5,123,536 in 2022 we estimate between 1,039,348 and 1,240,807 men and women aged ≥50 years have low bone mass, whereas between 308,474 and 498,104 have osteoporosis. These data are important for the diagnosis of osteoporosis in clinical practice, and national policy to reduce the illness burden of osteoporosis. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. Abstract Osteoporosis prevalence in Republic of Irelan

Leukotriene B4 receptor knockdown affects PI3K/AKT/mTOR signaling and apoptotic responses in colorectal cancer

Colorectal cancer (CRC) presents a landscape of intricate molecular dynamics. In this study, we focused on the role of the leukotriene B4 receptor (LTB4R) in CRC, exploring its significance in the disease's progression and potential therapeutic approaches. Using bioinformatics analysis of the GSE164191 and the Cancer Genome Atlas-colorectal adenocarcinoma (TCGA-COAD) datasets, we identified LTB4R as a hub gene influencing CRC prognosis. Subsequently, we examined the relationship between LTB4R expression, apoptosis, and the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway through cellular and mice experiments. Our findings revealed that LTB4R is highly expressed in CRC samples and is pivotal for determining prognosis. In vitro experiments demonstrated that silencing LTB4R significantly impeded CRC cell viability, migration, invasion, and colony formation. Correspondingly, in vivo tests indicated that LTB4R knockdown led to markedly slower tumor growth in mice models. Further in-depth investigation revealed that LTB4R knockdown significantly amplified the apoptosis in CRC cells and upregulated the expression of apoptosis-related proteins, such as caspase-3 and caspase-9, while diminishing p53 expression. Interestingly, silencing LTB4R also resulted in a significant downregulation of the PI3K/AKT/mTOR signaling pathway. Moreover, pretreatment with the PI3K activator 740Y-P only partially attenuated the effects of LTB4R knockdown on CRC cell behavior, emphasizing LTB4R's dominant influence in CRC cell dynamics and signaling pathways. LTB4R stands out as a critical factor in CRC progression, profoundly affecting cellular behavior, apoptotic responses, and the PI3K/AKT/mTOR signaling pathway. These findings not only shed light on LTB4R's role in CRC but also establish it as a potential diagnostic biomarker and a promising target for therapeutic intervention

Branch-and-reduce algorithm for convex programs with additional multiplicative constraints

This article presents a branch-and-reduce algorithm for globally solving for the first time a convex minimization problem (P) with p[greater-or-equal, slanted]1 additional multiplicative constraints. In each of these p additional constraints, the product of two convex functions is constrained to be less than or equal to a positive number. The algorithm works by globally solving a 2p-dimensional master problem (MP) equivalent to problem (P). During a typical stage k of the algorithm, a point is found that minimizes the objective function of problem (MP) over a nonconvex set Fk that contains the portion of the boundary of the feasible region of the problem where a global optimal solution lies. If this point is feasible in problem (MP), the algorithm terminates. Otherwise, the algorithm continues by branching and creating a new, reduced nonconvex set Fk+1 that is a strict subset of Fk. To implement the algorithm, all that is required is the ability to solve standard convex programming problems and to implement simple algebraic steps. Convergence properties of the algorithm are given, and results of some computational experiments are reported.Global optimization Multiplicative programming Product of convex functions Branch-and-reduce

Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting

Many forecasting techniques have been applied to sales forecasts in the retail industry. However, no one prediction model is applicable to all cases. For demand forecasting of the same item, the different results of prediction models often confuse retailers. For large retail companies with a wide variety of products, it is difficult to find a suitable prediction model for each item. This study aims to propose a dynamic model selection approach that combines individual selection and combination forecasts based on both the demand patterns and the out-of-sample performance for each item. Firstly, based on both metrics of the squared coefficient of variation (CV2) and the average inter-demand interval (ADI), we divide the demand patterns of items into four types: smooth, intermittent, erratic, and lumpy. Secondly, we select nine classical forecasting methods in the M-Competitions to build a pool of models. Thirdly, we design two dynamic weighting strategies to determine the final prediction, namely DWS-A and DWS-B. Finally, we verify the effectiveness of this approach by using two large datasets from an offline retailer and an online retailer in China. The empirical results show that these two strategies can effectively improve the accuracy of demand forecasting. The DWS-A method is suitable for items with the demand patterns of intermittent and lumpy, while the DWS-B method is suitable for items with the demand patterns of smooth and erratic