Production lot sizing with rework and fixed quantity deliveries

This paper is concerned with determination of the optimal lot size for an economic production quantity (EPQ) model with the reworking of random defective items and fixed quantity multiple deliveries. Classic EPQ model assumes continuous issuing policy for satisfying product demand and perfect quality production for all items produced. However, in real life vendor-buyer integrated production-inventory system, multi-delivery policy is used practically in lieu of the continuous issuing policy and generation of defective items during production run is inevitable. In this study, all nonconforming items produced are considered to be repairable and are reworked in each cycle when regular production ends. The finished items can only be delivered to customers if the whole lot is quality assured at the end of the rework. Fixed quantity multiple installments of the finished batch are delivered to customers at a fixed interval of time. The long-run average integrated cost function per unit time is derived. A closed-form optimal batch size solution to the problem is obtained. A numerical example demonstrates its practical usage

Predicting RNA-binding residues from evolutionary information and sequence conservation

Abstract Background RNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments. Results The proposed prediction framework named “ProteRNA” combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. Conclusions This article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.</p

Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

AbstractDifferential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations

A single-producer multi-retailer integrated inventory model with a rework process

This study considers a single-producer multi-retailer integrated inventory model with the reworking of random defective items produced. The objective is to find the optimal production lot size and optimal number of shipments that minimizes total expected costs for such a specific supply chains system. It is assumed that a product is manufactured by a producer. All items are screened for quality purpose and random nonconforming items will be picked up and reworked at the end of regular production in each cycle. After the entire lot is quality assured, multiple shipments will be delivered synchronously to m different retailers in each production cycle. Each retailer has its own annual product demand, unit stock holding cost, and fixed and variable delivery costs. Mathematical modeling and analysis is used to deal with the proposed model and to derive the expected system cost. Hessian matrix equations are employed to prove the convexity of the cost function. As a result, a closed-form optimal replenishment-delivery policy for such a specific single-producer multi-retailer integrated inventory model is obtained. A numerical example is provided to show the practical usage of the proposed model

Factors for poor prognosis of neonatal bacterial meningitis in a medical center in Northern Taiwan

BackgroundBacterial meningitis has long been a severe infectious disease in neonates, as well as a leading cause of adverse outcomes. We designed this study to know the factors for poor prognosis in neonatal bacterial meningitis.MethodsWe enrolled children aged less than 1 month who were admitted to Mackay Memorial Hospital from 1984 to 2008 and had culture-proven bacterial meningitis. The laboratory data and children’s clinical features were recorded. The patients’ outcomes were divided into four groups: death, having sequelae, complete recovery, and loss to follow-up. Patients with the outcomes of death and having sequelae were regarded as having a poor prognosis. Those who were lost to follow-up were excluded from the analysis of outcome. Multivariate analyses were performed to find the risk factors for poor prognosis.ResultsOne hundred fifty-six neonates fulfilled the inclusion criteria. Among these, 96 were boys (61.5%) and 102 (65.4%) had concomitant bacteremia. Group B streptococci (39.1%) and Escherichia coli (20.1%) were the two leading pathogens. Excluding those who were lost to follow-up (4.5%), 22 of 149 patients (14.8%) died, 36 (24.2%) had sequelae, and 91 (61.1%) recovered completely. Cerebrospinal fluid (CSF) protein more than 500 mg/dL at admission {odds ratio (OR): 171.18 [95% confidence interval (CI): 25.6–1000]}, predisposition to congenital heart disease [OR: 48.96 (95% CI: 6.06–395.64)], hearing impairment found during hospitalization [OR: 23.40 (95% CI: 3.62–151.25)], and seizure at admission or during hospitalization [OR: 10.10 (95% CI: 2.11–48.32)] were the factors predicting poor prognosis.ConclusionIn this 25-year study of newborns with bacterial meningitis, approximately one-seventh of the patients died, while two-fifths had sequelae. Nearly two-thirds of these had concomitant bacteremia. Group B streptococci and E. coli remained the two leading pathogens throughout the study period. Several factors for poor prognosis in newborns with culture-proven bacterial meningitis were found: high CSF protein concentration, congenital heart disease, hearing impairment, and seizure

The joint influence of quality assurance and postponement on a hybrid multi-item manufacturing-delivery decision-making

The present research explores the collective influence of quality assurance and postponement on a hybrid multiproduct replenishing-delivery decision-making. Assume the required multiproduct has a standard (common) component, and our replenishing-delivery model has incorporated a two-phase postponement strategy. The first phase makes all standard components and hires an external supplier to partially provide the required parts to cut short the needed uptime. In contrast, the second phase fabricates the finished multiproduct in sequence. To ensure the desired merchandise quality, we apply a quality-assurance action to the in-house processes to screen and remove scrap items and rework the repairable defects in both stages. Upon completing each merchandise, these products are transported to the customer in n fixed-quantity shipment in fixed-time intervals. We employ math modeling and formulating approaches to gain the overall supply-chain operating expenses comprising subcontracting, fabricating, stock holding, transportation, and customer holding costs. By minimizing system operating expenses, this research determines the optimal replenishing-delivery policy. Lastly, we give a numerical example to demonstrate our study’s applicability and usefulness/capability for facilitating managerial decision-making

Solving The Flexible Job Shop Problem using Multi-Objective Optimizer with Solution Characteristic Extraction

It is difficult to find optimal scheduling solutions for abstract scheduling problems with mass parallel tasks on multiprocessors because they are NP-complete. In this paper, a solution searching strategy called solution characteristic extraction is proposed as a multi-objective optimizer for solving flexible job shop problems (FJSP). These problems are concerned with finishing assigned jobs with minimal critical machine workload, total workload, and completion times. A suitable job assignment must consider processor performance, job complexity, and job suitability for each individual processor simultaneously. To test the efficiency and robustness of the proposed method, the experiments will contain two groups of benchmarks; with, and without release time constraints. Each benchmark includes numbers of heterogeneous processors and different jobs for execution. The results indicate the proposed method can find more potential solutions, and outperform related methods

Gallic Acid Induces a Reactive Oxygen Species-Provoked c-Jun NH 2

Idiopathic pulmonary fibrosis is a chronic lung disorder characterized by fibroblasts proliferation and extracellular matrix accumulation. Induction of fibroblast apoptosis therefore plays a crucial role in the resolution of this disease. Gallic acid (3,4,5-trihydroxybenzoic acid), a common botanic phenolic compound, has been reported to induce apoptosis in tumor cell lines and renal fibroblasts. The present study was undertaken to examine the role of mitogen-activated protein kinases (MAPKs) in lung fibroblasts apoptosis induced by gallic acid. We found that treatment with gallic acid resulted in activation of c-Jun NH2-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and protein kinase B (PKB, Akt), but not p38MAPK, in mouse lung fibroblasts. Inhibition of JNK using pharmacologic inhibitor (SP600125) and genetic knockdown (JNK specific siRNA) significantly inhibited p53 accumulation, reduced PUMA and Fas expression, and abolished apoptosis induced by gallic acid. Moreover, treatment with antioxidants (vitamin C, N-acetyl cysteine, and catalase) effectively diminished gallic acid-induced hydrogen peroxide production, JNK and p53 activation, and cell death. These observations imply that gallic acid-mediated hydrogen peroxide formation acts as an initiator of JNK signaling pathways, leading to p53 activation and apoptosis in mouse lung fibroblasts

Managing cardiac arrest with refractory ventricular fibrillation in the emergency department: Conventional cardiopulmonary resuscitation versus extracorporeal cardiopulmonary resuscitation

AbstractAimRefractory ventricular fibrillation, resistant to conventional cardiopulmonary resuscitation (CPR), is a life threatening rhythm encountered in the emergency department. Although previous reports suggest the use of extracorporeal CPR can improve the clinical outcomes in patients with prolonged cardiac arrest, the effectiveness of this novel strategy for refractory ventricular fibrillation is not known. We aimed to compare the clinical outcomes of patients with refractory ventricular fibrillation managed with conventional CPR or extracorporeal CPR in our institution.MethodThis is a retrospective chart review study from an emergency department in a tertiary referral medical center. We identified 209 patients presenting with cardiac arrest due to ventricular fibrillation between September 2011 and September 2013. Of these, 60 patients were enrolled with ventricular fibrillation refractory to resuscitation for more than 10min. The clinical outcome of patients with ventricular fibrillation received either conventional CPR, including defibrillation, chest compression, and resuscitative medication (C-CPR, n=40) or CPR plus extracorporeal CPR (E-CPR, n=20) were compared.ResultsThe overall survival rate was 35%, and 18.3% of patients were discharged with good neurological function. The mean duration of CPR was longer in the E-CPR group than in the C-CPR group (69.90±49.6min vs 34.3±17.7min, p=0.0001). Patients receiving E-CPR had significantly higher rates of sustained return of spontaneous circulation (95.0% vs 47.5%, p=0.0009), and good neurological function at discharge (40.0% vs 7.5%, p=0.0067). The survival rate in the E-CPR group was higher (50% vs 27.5%, p=0.1512) at discharge and (50% vs 20%, p=0. 0998) at 1 year after discharge.ConclusionsThe management of refractory ventricular fibrillation in the emergency department remains challenging, as evidenced by an overall survival rate of 35% in this study. Patients with refractory ventricular fibrillation receiving E-CPR had a trend toward higher survival rates and significantly improved neurological outcomes than those receiving C-CPR

Img2Logo:Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc