Next generation smart manufacturing and service systems using big data analytics

© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

A novel method produces native light-harvesting complex II aggregates from the photosynthetic membrane revealing their role in nonphotochemical quenching.

Nonphotochemical quenching (NPQ) is a mechanism of regulating light harvesting that protects the photosynthetic apparatus from photodamage by dissipating excess absorbed excitation energy as heat. In higher plants, the major light-harvesting antenna complex (LHCII) of photosystem (PS) II is directly involved in NPQ. The aggregation of LHCII is proposed to be involved in quenching. However, the lack of success in isolating native LHCII aggregates has limited the direct interrogation of this process. The isolation of LHCII in its native state from thylakoid membranes has been problematic because of the use of detergent, which tends to dissociate loosely bound proteins, and the abundance of pigment-protein complexes (e.g. PSI and PSII) embedded in the photosynthetic membrane, which hinders the preparation of aggregated LHCII. Here, we used a novel purification method employing detergent and amphipols to entrap LHCII in its natural states. To enrich the photosynthetic membrane with the major LHCII, we used Arabidopsis thaliana plants lacking the PSII minor antenna complexes (NoM), treated with lincomycin to inhibit the synthesis of PSI and PSII core proteins. Using sucrose density gradients, we succeeded in isolating the trimeric and aggregated forms of LHCII antenna. Violaxanthin- and zeaxanthin-enriched complexes were investigated in dark-adapted, NPQ, and dark recovery states. Zeaxanthin-enriched antenna complexes showed the greatest amount of aggregated LHCII. Notably, the amount of aggregated LHCII decreased upon relaxation of NPQ. Employing this novel preparative method, we obtained a direct evidence for the role of in vivo LHCII aggregation in NPQ

Management of a Fungal Perinephric Abscess (PNA): Dilemma Revisited

ABSTRACT Candida species can cause a wide variety of clinical syndromes, especially in immunocompromised and diabetic patients. Primary perirenal candidial abscess has been a rare reported entity. Here we report a rare case of primary PNA due to Candida species in an immunocompetent patient with diabetes mellitus and its minimal invasive management. Retrospective and prospective analysis of clinical, laboratory and radiological records along with continued follow up of patient was done. This patient was 48-year-old man, admitted with burning in micturition, right flank discomfort and low grade fever since 1 year. Abdominal ultrasound and computerized tomography were suggestive of a PNA of the right kidney. Candida species was isolated from sample obtained by C.T. guided needle aspiration. Culture of aspirate showed sensitivity to azoles. Systemic antifungal therapy based on culture report was started in form of oral drug. The patient responded well leading to resolution of lump and the fever. Appropriate timely treatment appears to be having a promising role in definitive therapy for renal and PNA due to Candida even in immunocompetent host with predisposing factors such as diabetes mellitus. This case highlights the fact that fungal infections should be included in the differential diagnosis of PNA in such patients

Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the predecided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

Refractory periods and climate forcing in cholera dynamics

Outbreaks of many infectious diseases, including cholera, malaria and dengue, vary over characteristic periods longer than 1 year(1,2). Evidence that climate variability drives these interannual cycles has been highly controversial, chiefly because it is difficult to isolate the contribution of environmental forcing while taking into account nonlinear epidemiological dynamics generated by mechanisms such as host immunity(2-4). Here we show that a critical interplay of environmental forcing, specifically climate variability, and temporary immunity explains the interannual disease cycles present in a four-decade cholera time series from Matlab, Bangladesh. We reconstruct the transmission rate, the key epidemiological parameter affected by extrinsic forcing, over time for the predominant strain ( El Tor) with a nonlinear population model that permits a contributing effect of intrinsic immunity. Transmission shows clear interannual variability with a strong correspondence to climate patterns at long periods ( over 7 years, for monsoon rains and Brahmaputra river discharge) and at shorter periods ( under 7 years, for flood extent in Bangladesh, sea surface temperatures in the Bay of Bengal and the El Nino Southern Oscillation). The importance of the interplay between extrinsic and intrinsic factors in determining disease dynamics is illustrated during refractory periods, when population susceptibility levels are low as the result of immunity and the size of cholera outbreaks only weakly reflects climate forcing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62876/1/nature03820.pd

A Synthetic Uric Acid Analog Accelerates Cutaneous Wound Healing in Mice

Wound healing is a complex process involving intrinsic dermal and epidermal cells, and infiltrating macrophages and leukocytes. Excessive oxidative stress and associated inflammatory processes can impair wound healing, and antioxidants have been reported to improve wound healing in animal models and human subjects. Uric acid (UA) is an efficient free radical scavenger, but has a very low solubility and poor tissue penetrability. We recently developed novel UA analogs with increased solubility and excellent free radical-scavenging properties and demonstrated their ability to protect neural cells against oxidative damage. Here we show that the uric acid analog (6, 8 dithio-UA, but not equimolar concentrations of UA or 1, 7 dimethyl-UA) modified the behaviors of cultured vascular endothelial cells, keratinocytes and fibroblasts in ways consistent with enhancement of the wound healing functions of all three cell types. We further show that 6, 8 dithio-UA significantly accelerates the wound healing process when applied topically (once daily) to full-thickness wounds in mice. Levels of Cu/Zn superoxide dismutase were increased in wound tissue from mice treated with 6, 8 dithio-UA compared to vehicle-treated mice, suggesting that the UA analog enhances endogenous cellular antioxidant defenses. These results support an adverse role for oxidative stress in wound healing and tissue repair, and provide a rationale for the development of UA analogs in the treatment of wounds and for modulation of angiogenesis in other pathological conditions

Coupling of CFD and semiempirical methods for designing three-phase condensate separator: case study and experimental validation

This study presents an approach to determine the dimensions of three-phase separators. First, we designed different vessel configurations based on the fluid properties of an Iranian gas condensate field. We then used a comprehensive computational fluid dynamic (CFD) method for analyzing the three-phase separation phenomena. For simulation purposes, the combined volume of fluid–discrete particle method (DPM) approach was used. The discrete random walk (DRW) model was used to include the effect of arbitrary particle movement due to variations caused by turbulence. In addition, the comparison of experimental and simulated results was generated using different turbulence models, i.e., standard k–ε, standard k–ω, and Reynolds stress model. The results of numerical calculations in terms of fluid profiles, separation performance and DPM particle behavior were used to choose the optimum vessel configuration. No difference between the dimensions of the optimum vessel and the existing separator was found. Also, simulation data were compared with experimental data pertaining to a similar existing separator. A reasonable agreement between the results of numerical calculation and experimental data was observed. These results showed that the used CFD model is well capable of investigating the performance of a three-phase separator

Genomic Analysis of Individual Differences in Ethanol Drinking: Evidence for Non-Genetic Factors in C57BL/6 Mice

Genetic analysis of factors affecting risk to develop excessive ethanol drinking has been extensively studied in humans and animal models for over 20 years. However, little progress has been made in determining molecular mechanisms underlying environmental or non-genetic events contributing to variation in ethanol drinking. Here, we identify persistent and substantial variation in ethanol drinking behavior within an inbred mouse strain and utilize this model to identify gene networks influencing such “non-genetic” variation in ethanol intake. C57BL/6NCrl mice showed persistent inter-individual variation of ethanol intake in a two-bottle choice paradigm over a three-week period, ranging from less than 1 g/kg to over 14 g/kg ethanol in an 18 h interval. Differences in sweet or bitter taste susceptibility or litter effects did not appreciably correlate with ethanol intake variation. Whole genome microarray expression analysis in nucleus accumbens, prefrontal cortex and ventral midbrain region of individual animals identified gene expression patterns correlated with ethanol intake. Results included several gene networks previously implicated in ethanol behaviors, such as glutamate signaling, BDNF and genes involved in synaptic vesicle function. Additionally, genes functioning in epigenetic chromatin or DNA modifications such as acetylation and/or methylation also had expression patterns correlated with ethanol intake. In verification for the significance of the expression findings, we found that a histone deacetylase inhibitor, trichostatin A, caused an increase in 2-bottle ethanol intake. Our results thus implicate specific brain regional gene networks, including chromatin modification factors, as potentially important mechanisms underlying individual variation in ethanol intake