A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost. Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy. Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs. Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).Peer Reviewe

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

Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention

Intermittent Prediction Method Based On Marcov Method And Grey Prediction Method

This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the demand quantity in subsequent n intervals using Grey prediction model GM (1, 1) based on the sequence of demand quantity. Finally, the author places the demand quantity in the n intervals in order at the moments where demand occurs to get the predicted result of demand for electric material with intermittent demand. According to instance analysis, the integrated approach mentioned in this paper surpasses existing methods in providing accurate prediction on data of product with intermittent demand

NCACO-score: An effective main-chain dependent scoring function for structure modeling

<p>Abstract</p> <p>Background</p> <p>Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.</p> <p>Results</p> <p>Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.</p> <p>Conclusions</p> <p>Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.</p

All the wiser: Fake news intervention using user reading preferences

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A systematic review of maggot debridement therapy for chronically infected wounds and ulcers

SummaryObjectiveThis study aimed to systematically evaluate maggot debridement therapy (MDT) in the treatment of chronically infected wounds and ulcers.MethodsWe performed a meta-analysis referring to the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We searched for published articles in the following databases: PubMed, Web of Science, Embase, Wanfang (Chinese), and the China National Knowledge Infrastructure (CNKI). The latest search was updated on March 14, 2014. For dichotomous outcomes, the effects of MDT were expressed as the relative risk (RR) and 95% confidence interval (CI). For continuous outcomes with different measurement scales, we calculated the standardized mean difference (SMD). The pooled effects were estimated using a fixed effect model or random effect model based on the heterogeneity test. Subgroup analyses were performed according to the types of wounds or ulcers.ResultsMDT had a significantly increased positive effect on wound healing compared with conventional therapies, with a pooled RR of 1.80 (95% CI 1.24–2.60). The subgroup analysis revealed that the combined RRs were 1.79 (95% CI 0.95–3.38) for patients with diabetic foot ulcers (DFU) and 1.70 (95% CI 1.28–2.27) for patients with other types of ulcers. The time to healing of the ulcers was significantly shorter among patients treated with MDT, with a pooled SMD of −0.95 (95% CI −1.24, −0.65). For patients with DFU, the SMD was −0.79 (95% CI −1.18, −0.41), and for patients with other types of ulcers, the SMD was −1.16 (95% CI −1.63, −0.69).ConclusionMDT not only shortened the healing time but also improved the healing rate of chronic ulcers. Therefore, MDT may be a feasible alternative in the treatment of chronic ulcers

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Forecasting and stock control play an important role in the electric companies because outstanding forecasting and stock control increase service level obviously and decrease stock cost effectively. However, the majority of the electric materials are intermittent demand, resulting in poor forecasting and stock control performance. Therefore, exploring the reasons that affect forecasting performance and stock control is necessary. This paper explores the effects of the correlation of intermittent electric materials on forecasting and stock control. First, we divide the correlation into three categories: autocorrelation in demand sizes, autocorrelation in intervals and cross-correlation between demand size and interval. Forecasting by SBA approach and using periodic dynamic inventory strategy (T, S) to control stock, exploring the effects of these three correlations on forecast accuracy, stock cost and service level. The data shows that correlations of electric materials affect their forecasting and stock control, which will help company find more accurate forecast approach and lower the cost of stock in the future

Network-Based Gene Expression Biomarkers for Cold and Heat Patterns of Rheumatoid Arthritis in Traditional Chinese Medicine

In Traditional Chinese Medicine (TCM), patients with Rheumatoid Arthritis (RA) can be classified into two main patterns: cold-pattern and heat-pattern. This paper identified the network-based gene expression biomarkers for both cold- and heat-patterns of RA. Gene expression profilings of CD4+ T cells from cold-pattern RA patients, heat-pattern RA patients, and healthy volunteers were obtained using microarray. The differentially expressed genes and related networks were explored using DAVID, GeneSpring software, and the protein-protein interactions (PPI) method. EIF4A2, CCNT1, and IL7R, which were related to the up-regulation of cell proliferation and the Jak-STAT cascade, were significant gene biomarkers of the TCM cold pattern of RA. PRKAA1, HSPA8, and LSM6, which were related to fatty acid metabolism and the I-κB kinase/NF-κB cascade, were significant biomarkers of the TCM heat-pattern of RA. The network-based gene expression biomarkers for the TCM cold- and heat-patterns may be helpful for the further stratification of RA patients when deciding on interventions or clinical trials

Observation and simulation study on the rapid intensification mechanism of Typhoon “Mekkhala” (2006)

Based on Doppler Weather radar observations and numerical simulations applying the Weather Research and Forecasting (WRF) system, this study focused on the rapid intensification (RI) of Typhoon “Mekkhala” (2006) in the inshore area in 2020. The simulated track of the typhoon relatively matched with the observation, with a slight eastward bias compared to the observed track. During the phase of RI, there was a slight weakening of vertical wind shear between 200–500 hPa. The temporary decrease in vertical wind shear became a favorable factor for the intensification of the typhoon. In general, vertical wind shear of the lower atmosphere is the key to supporting the RI of Typhoon Mekkhala. In the middle troposphere, the southward component of the vertical wind shear suddenly increases, indicates that the inflow of southern wind to the core of the typhoon had strengthened. Thus, the strengthening of the moisture transport by enhanced southern wind, contributed to the intensification of the typhoon. During the intensification of the typhoon, the low-level vorticity was significantly enhanced, and the high vorticity values expanded from the lower to higher troposphere. The vertical distribution of vorticity transformed from symmetry to asymmetry. The development of secondary circulation on both sides of the typhoon is a dynamic factor for intensification