Monitoring the Supply of Products in a Supply Chain Environment: a Fuzzy Neural Approach

The fuzzy logic is applied to resolve the monitoring problem of products quality and products quantity increasingly varying as market requirement. A series of fuzzy rules are employed and the fuzzy system may generate suggested supply change rate. At the same time, the operation of supplier is also dynamically changing and the evaluation and selection for supplier are the basis of supply –chain co-operation. So whether it is scientific to select a supplier is crucial for sustaining and developing a company. Therefore, in this paper the neural network is introduced to dynamically assess suppliers and recommend to substituting for new ones when necessary, only supplementing fuzzy logic system with its advantages. This paper describes the methodology for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles

Optimal cutoffs of growth discordance for the risk of preeclampsia in twin pregnancies: A single-center retrospective cohort study

ObjectiveTo explore the optimal cutoffs of growth discordance for the risk of preeclampsia in twin pregnancies.MethodsA retrospective cohort study in a university hospital which included twins delivered from February 2013 to September 2020. Restrictive cubic spline (RCS) model was applied to the trend of intertwin birthweight difference (BWD) with the risk of preeclampsia. Logistic regression and subgroup analysis were performed to find the cut-off with statistical significance and clinical meaningfulness.ResultsA total of 2,631 women pregnant with twins were enrolled. RCS showed a nonlinear upward trend of preeclampsia with BWD, and the BWD of 15% was the initial rising point. With the confounders adjusted, only the group with BWD ≥ 25% was found to be significantly associated with an increased risk of preeclampsia (adjusted odds ratio [aOR], 2.44; 95% confidence interval [CI]: 1.74–3.42). Additionally, subgroup analysis showed that both monochorionic (MC) and small for gestational age (SGA) twins were more likely to complicate with preeclampsia.ConclusionThe growth discordance of 15% during pregnancy may be the preventive point of preeclampsia, and 25% may be the interventional point

Epigenotype-genotype-phenotype correlations in SETD1A and SETD2 chromatin disorders

Germline pathogenic variants in two genes encoding the lysine-specific histone methyltransferase genes SETD1A and SETD2 are associated with neurodevelopmental disorders (NDDs) characterised by developmental delay and congenital anomalies. The SETD1A and SETD2 gene products play a critical role in chromatin-mediated regulation of gene expression. Specific methylation episignatures have been detected for a range of chromatin gene-related NDDs and have impacted clinical practice by improving interpretation of variant pathogenicity. To investigate if SETD1A and/or SETD2-related NDDs are associated with a detectable episignature, we undertook targeted genome-wide methylation profiling of > 2 M CpGs using a next generation sequencing based assay. Comparison of methylation profiles in patients with SETD1A variants (n = 6) did not reveal evidence of a strong methylation episignature. Review of the clinical and genetic features of SETD2 patient group revealed that, as reported previously, there were phenotypic differences between patients with truncating mutations (n = 4, Luscan-Lumish syndrome; MIM:616831) and those with missense codon 1740 variants (p.Arg1740Trp (n = 4) and p.Arg1740Gln (n = 2)). Both SETD2 subgroups demonstrated a methylation episignature which was characterised by hypomethylation and hypermethylation events respectively. Within the codon 1740 subgroup, both the methylation changes and clinical phenotype were more severe in those with p.Arg1740Trp variants. We also noted that two of 10 cases with a SETD2-NDD had developed a neoplasm. These findings reveal novel epigenotype-genotype–phenotype correlations in SETD2-NDDs and predict a gain-of-function mechanism for SETD2 codon 1740 pathogenic variants

Consumers’ Purchase Intention of New Energy Vehicles: Do Product-Life-Cycle Policy Portfolios Matter?

New energy vehicles have been recognized as a sustainable alternative to lower gasoline consumption and emissions in the transportation sector. To alleviate environmental pressure, a spectrum of government policies has been introduced to inspire the production and penetration of new energy vehicles (NEVs). Meanwhile, some of the incentive policies are facing renewals and modifications to meet consumers’ demand of purchase and the present growth of the NEV industry. This means that the understanding of what the current and upcoming policies are, how to formulate policy portfolios, and consumers’ purchasing NEV behavior in a response to these policies and its adjustment are of practical and academic importance for the NEV sector. Different from prior research which analyzed the role of government policy as a whole, we here separately examined the impact of policy portfolios (i.e., production policy, purchase/usage policy and recycle policy) on NEV adoption from the product life cycle perspective. The hypotheses were empirically tested by analyzing data collected from 299 respondents in China. The results showed that production policy has a significantly positive impact on financial benefits, esteem needs and infrastructure, whereas it insignificantly influences NEV performance; similarly, purchase/usage policy positively affects esteem needs and infrastructure, yet its effect on financial benefits is found to be insignificant; meanwhile, recycle policy has a significantly positive effect on financial benefits, and esteem needs as well as NEV performance. Furthermore, financial benefits, esteem needs, NEV performance and infrastructure are found to significantly and positively impact on consumers’ purchase intention. Parallel to this, we observed perceived usefulness and perceived ease of use play partially mediating relations between policy portfolios and consumers’ adoption intent. Hints for decision-makers and avenues for future study are discussed in this research

How to Choose the Refueling of New Energy Vehicles under Swapping vs. Charging Mode: From the Consumers’ Perspective

Battery charging mode (CM) is a prevalent method of trans-shipping power to new energy vehicles (NEVs). Unfortunately, due to the limited capacity of batteries, typical NEVs can only travel for approximately 350 miles on a single charge and require hours to be recharged. Battery swapping mode (SM), as a novel alternative, can offer an ideal solution by exchanging depleted batteries for recharged ones at swapping stations in the middle of long trips, inevitably influencing potential consumers’ purchase behaviors. To examine the impact of SM and CM on consumers’ purchase intention, this paper examines a duopolistic market consisting of two new energy vehicle manufacturers (i.e., a NEV-SM manufacturer and a NEV-CM manufacturer), who adopt SM and CM to service consumers, respectively. Considering SM is characterized by low initial investment and ease of use for consumers, NEV-CM manufacturers capitalize on extended battery warranty services in response to rivals’ utilization of SM. Thereby, non-cooperative game models are formulated, in which government subsidies are taken into account. The optimal production decision for both the NEV-SM manufacturer and the NEV-CM manufacturer are analyzed under three scenarios: without extended warranty service, with extended warranty service, and with extended warranty service and subsidy. The results show that the two manufacturers’ market dominance relies on the ratio of the swapping station’s convenience to the extended warranty service and the valuation incremental rate. Additionally, we also find that the government subsidy can dramatically improve the NEV-SM manufacturer’s performance at the initial stage, but if the subsidy is insufficient in size at the subsequent stage, this will lead to policy failure and inefficiency in propelling the diffusion of swapping mode

A Hierarchical Innovation-Related Crowdsourcing Decision in Fast Fashion Industry

Due to scarcity of designers in fast fashion industry and proliferation of the Internet, small- and medium-sized garment makers have gradually turned to external designers to enhance their innovation efficiency via crowdsourcing initiative. However, few have investigated the issue of fast fashion customized-design matching decision in the crowdsourcing context. Different from previous works, we split crowdsourcing matching decision process into three hierarchical submodels in terms of three key factors, namely, surplus, due date, and goodwill. From a dynamic perspective, we first develop a two-sided matching model where garment makers and online designers select one another by maximizing their total surpluses with an aim to reach robust final pairs and derive the corresponding conditions under which the optimal pairs can be obtained. Then, the extensions of the matching model are conducted by incorporating the critical factors of due date and garment makers’ goodwill, respectively. Followed by that, an improved Gale–Shapley algorithm is devised to solve the crowdsourcing matching problems. The results illustrate when garment makers exceed online designers in number, crowdsourcing design tasks without due-date constraint are more attractive for designers’ participation than those with due-date constraint, and garment makers intend to share the incremental surpluses with designers to maximize the total surpluses. By contrast, when online designers surpass garment makers in number, designers prefer due-date design tasks to those without it. In addition, regardless of whether under the irregular or regular case, the model with goodwill concern always outperforms the two others. Moreover, celebrated garment makers are invited to post design tasks, thus enabling to entice more designers’ engagement in crowdsourcing activities, which in turn facilitating to transfer myopic designers to strategic ones. Finally, sensitivity analysis further verifies the models are stable and robust