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The polymer‐based acetabular cup prosthesis, a vital component of hip replacement surgery, significantly contributes to the recovery of patients afflicted with osteoarthritic conditions. Nevertheless, the current clinical usage of polymer acetabular cup prostheses commonly encounters the challenge of balancing wear resistance and oxidation resistance, significantly impacting both their lifespan and patients′ quality of life. Consequently, researchers have persistently enhanced the attributes of polymer acetabular cup prosthetic materials. These enhancements, including irradiation and filler modifications, are intended to concurrently bolster both the wear and oxidation resistance of the prosthesis materials. This comprehensive approach aims to address wear‐associated clinical complications like osteolysis and oxidative brittleness, ultimately extending their in vivo service life. For this reason, this paper retrospectively discusses the progress of research on the modification of polymer acetabular cup prosthesis materials for high wear and oxidation resistance and explores potential design methods for optimising artificial acetabular cup materials, with a view to providing new ideas for extending the service life of artificial joint implants. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of this study was to determine the association of inflammation and immune responses with the outcomes of patients at various stages, and to develop risk stratification for improving clinical practice and reducing mortality. PATIENTS AND METHODS: We included 77 patients with primary outcomes of either death or survival. Demographics, clinical features, comorbidities, and laboratory tests were compared. Linear, logistic, and Cox regression analyses were performed to determine prognostic factors. RESULTS: The average age was 59 years (35-87 years). There were 12 moderate cases (16.2%), 42 severe cases (54.5%), and 23 critical cases (29.9%); and 41 were male (53.2%). Until March 20, 68 cases were discharged (88.3%), and nine critically ill males (11.7%) died. Interleukin-6 (IL-6) levels on the 1st day were compared with IL-6 values on the 14th day in the severe and the critically ill surviving patients (F=4.90, p=0.034, ß=0.35, 95% CI: 0.00-0.10), and predicted death in the critically ill patients (p=0.028, ß=0.05, OR: 1.05, 95% CI: 1.01-1.10). CD4+ T-cell counts at admission decreased the hazard ratio of death (p=0.039, ß=-0.01, hazard ratio=0.99, 95% CI: 0.98-1.00, and median survival time 13.5 days). CONCLUSIONS: The present study demonstrated that IL-6 levels and CD4+ T-cell count at admission played key roles of predictors in the prognosis, especially for critically ill patients. High levels of IL-6 and impaired CD4+ T-cells are seen in severe and critically ill patients with COVID-19. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.04.006 Byline: K.-J. Wang (a), S.-M. Wang (b), S.-J. Yang (c) Keywords: Production; Capacity planning and allocation; Genetic algorithms; Semiconductor testing Abstract: Profitable but risky semiconductor testing market has led companies in the industry to carefully seek to maximize their profits by developing a proper resource portfolio plan for simultaneously deploying resources and selecting the most profitable orders. Various important factors, such as resource investment alternatives, trade-offs between the price and speed of equipment and capital time value, further increase the complexity of the simultaneous resource portfolio problem. This study develops a simultaneous resource portfolio decision model as a non-linear integer programming, and proposes a genetic algorithm to solve it efficiently. The proposed method is employed in the context of semiconductor testing industry to support decisions regarding equipment investment alternatives (including new equipment procurement, rent and transfer by outsourcing, and phasing outing) for simultaneous resources (such as testers and handlers) and task allocation. Experiments have showed that our approach, in contrast to an optimal solution tool, obtains a near-optimal solution in a relatively short computing time. Author Affiliation: (a) Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC (b) Department of Industrial Engineering, Chung-Yuan Christian University, Chung Li 320, Taiwan, ROC (c) Australian Graduate School of Management, The University of Sydney & The University of New South Wales, Australia Article History: Received 3 March 2005; Accepted 3 April 2006

I. Introduction The investment on facilities for manufacturing semiconductor and electronic products requires a high amount of capital. TFT-LCD and wafer manufacturing industries are some typical examples. For instance, the [...], The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiplefunction machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently. Keywords: Capacity planning; Resource portfolio; Genetic algorithm

Predictive biomarkers for early diagnosis of prostate cancer are important for its treatment. The functional microRNAs in the exosomes of plasma and serum samples are of interest as stable and non-invasive biomarkers for recurrence in cancer patients. The present study aimed to clarify the value of plasma exosomal miRNA-125a-5p and miR-141-5p as biomarkers for the diagnosis of prostate cancer. The study included 19 healthy individuals and 31 prostate cancer patients. In comparison to the levels in healthy controls, exosomal miR-141-5p levels showed a slight increase in prostate cancer patients (p=0.085), and miR-125a-5p levels that showed a significant decrease in patients with prostate cancer than in healthy controls (p=0.032). As a derived parameter, the miR-125a-5p/miR-141-5p ratio was significantly higher in patients with prostate cancer than in healthy controls (p<0.001). We found that exosomal miR-141-5p in plasma showed a promise in distinguishing prostate cancer patients with the AUC of 0.652, and for miR-125a-5p, the AUC was 0.691. For the miR-125a-5p/miR-141-5p ratio, the AUC value was 0.793. We found that miR-125a-5p has a weak positive correlation with PSA (correlation coefficient = 0.3413). Moreover, miR-141-5p has been found to hold a negatively no-significant correlation with PSA, with the correlation coefficient is -0.1102. We speculate that, as diagnostic markers for prostate cancer, miR-125-5p and miR-141-5p might be independent of the PSA. In summary, the results of this study suggest that high plasma exosomal expression of miR-141-3p and low expression of miR-125a-5p in plasma exosomes from prostate cancer patients might be useful markers of specific tumor traits associated with prostate cancer. Moreover, the miR-125a-5p/miR-141-5p ratio seems to perform better than either of the single values alone. [ABSTRACT FROM AUTHOR]

Uncertain and lumpy demand forces capacity planners to maximize the profit of individual factory by simultaneously taking advantage of outsourcing to and/or being outsourced from its supply chain and even competitors. This study develops a resource-planning model of a large manufacturer with two profit-centered factories. The proposed model enables a collaborative integration for resource and demand sharing which is highly attractive to the high-tech industries against the challenges of short product life cycle, intensive capital investment and decreasing marginal profit. Each of the individual factories applies an economic resource-planning model and a genetic algorithm to improve its objective while purchasing extra capacity requirement from its peer factory or selling extra capacity of resources to the others through a negotiation algorithm. This study makes a contribution in successfully building a mutual negotiation model for a set of customer tasks to be realized by the negotiating parties, each with private information regarding company objectives, cost and price. Experimental results reveal that near-optimal solutions for both of the isolated (a single factory) and negotiation-based (between two factories) environments are obtained. [ABSTRACT FROM AUTHOR]

A distributed model for allocating a single job to be processed through multiple machines and alternative routings so that the job flow time is experimentally shown to reach its minimum is presented. Issues are addressed pertaining to dynamic and fully distributed decision-making for determining the job flow time in a cooperative manner between system agents (i.e. machines and a job), and for selecting a good routing (i.e. a short flow time) for a job when alternative routings are available. Two cases with confirmed jobs and without confirmed jobs are examined. The proposed protocols can deal with job shop scheduling with multiple product types in a sequential manner. The model is developed on the basis of a distributed shortest path algorithm using asynchronous job-initiated protocols. These protocols are examined using object-oriented simulation models and it is shown how the agents converge to the optimal path in a finite time. [ABSTRACT FROM AUTHOR]

In the semiconductor testing process, many resources such as testers, handlers, loadboards and toolings are required to be ready simultaneously so that testing tasks can be conducted. A limited budget under depressed economy enhances the need for exploring better solutions of testing capacity expansion and allocation. However, to maximize profit as planning with multiple resources is very challenging. This study focused on the issues pertaining to the decisions of (1) the type and number of testers that should be invested to deal with forthcoming orders at a semiconductor testing facility under a constrained budget and (2) the allocation of tester capacity for the orders so as to maximize company profit with limited, multiple resources. Owing to the high computational complexity of the problem, the study developed a genetic algorithm to resolve the two issues simultaneously. A mathematical model was developed to formalize the problem and serve as a benchmark for comparison with the proposed algorithm that attacked the same problem more efficiently. Taguchi experimental design was employed to find the most appropriate parameters for the proposed genetic algorithm under a variety of budget set-up. Experimental results indicated that the proposed algorithm was robust enough to budget plans, and its performance approximated closely with that of the mathematical model. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive system with a high degree of malignancy. Currently, there have been many studies on exosomal microRNAs (miRNAs) discovery in pancreatic cancer. This systematic review aimed to give an overview about known exosomal miRNAs and discuss their diagnostic performance, as well as prognostic value in PC. MATERIALS AND METHODS: PubMed and Web of Science were used for systematic literature research for this review. This literature research was mainly to identify studies that performed plasmatic and serological testing for exosomal miRNAs in pancreatic cancer patients and controls. Two independent reviewers separately extracted data on study characteristics and results. RESULTS: In total, nine prior studies were included in this review. Of which, eleven different single exosomal miRNAs and three exosomal miRNA panels were reported. CONCLUSIONS: When single exosomal miRNA was used as a diagnostic tool, the specificity is generally high, but the sensitivity is commonly low. When multiple of exosomal miRNAs were used simultaneously, higher sensitivities can be obtained at relatively reasonable specificity levels with certain miRNA combinations. Developing a combination of miRNA markers may be a promising approach for early detection of pancreatic cancer. [ABSTRACT FROM AUTHOR]