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Parameter selection in the design of displacement and motion functions by means of B-splines
Enrique Zayas,Salvador Cardona-Foix,Lluïsa Jordi-Nebot 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.5
This work analyses the incidence of the parameter selection of B-spline curves, used in the design of displacement and motion functions, on its degree of freedom and shape. A complete design process based on the use of non-parametric B-spline curves and the convenience of selecting the curve parameters from the point of view of its practical application is shown. In order to make easy the design and use of the displacement function, the algorithms for derivation and integration of the B-splines used are presented. Three case studies validate the proposed design process and the selection of the adequate parameters. The first case presents the design of a displacement function of a roller follower driven by a disk cam; the corresponding cam profile and its prototype are shown. The second case presents the design of the motion function corresponding to the cutting unit of a manufacturing cardboard tube machine. The third case exposes the design of the displacement function of the bar feeding mechanism in a single-spindle automatic lathe, to produce a partial thread screw of hexagonal head.
Hasan Symum,José L. Zayas-Castro 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.1
Objectives: The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions. Methods: An administrative claim dataset (2008–2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE). Results:LSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model’s sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS. Conclusions: Prolonged LOS is mostly associated with pre-intraoperative clinical and patient socio-economic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.
Murine Mammary Carcinoma Induces Chronic Systemic Inflammation and Immunosuppression in BALB/c Mice
Dasha Fuentes,Alejandro Cabezas-Cruz,Circe Mesa,Tania Carmenate,Darel Martínez,Anet Valdés-Zayas,Enrique Montero,Rolando Pérez 한국유방암학회 2022 Journal of breast cancer Vol.25 No.3
Purpose: The F3II cell line is a highly invasive variant of mammary carcinoma. Although it is frequently used as a model to evaluate the efficacy of immunotherapy, its impact on the immune system remains poorly understood. The main objectives of this study were to evaluate the effects of F3II tumors on the development of chronic inflammation and to characterize tumor-associated immunosuppression. Methods: Following the experimental implantation of F3II tumors in BALB/c mice, alterations in the liver and spleen anatomy and the numbers of circulating leukocytes, myeloid-derived suppressor cells (MDSCs), and regulatory T cells were measured using hematological techniques, histopathological analysis, and flow cytometry. The capacity of the F3II tumor-bearing mice to reject MB16F10 allogeneic tumor transplantation was also evaluated. In addition, the restoration of immune parameters in tumor-bearing mice was evaluated after standard breast cancer chemotherapy and surgical tumor excision. Results: F3II tumor implantation increased the levels of chronic inflammatory markers, such as the neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios, and caused myeloid alterations, including extramedullary granulopoiesis and megakaryopoiesis, along with the recruitment of MDSCs to the spleen. Chemotherapy or surgical F3II tumor removal completely rescued the tumor-associated extramedullary granulopoiesis and megakaryopoiesis. Notably, the presence of F3II tumors reduced the capacity of BALB/c mice to reject MB16F10 allogeneic tumor transplantation. Conclusion: These results support the occurrence of F3II tumor-mediated immune cell dysfunction, which mimics the immune alterations characterized by chronic systemic inflammation and immunosuppression observed in breast cancer in clinical settings. Thus, the F3II tumor model is relevant for evaluating novel breast cancer immunotherapies and combinations in preclinical studies. This model could also be useful for identifying appropriate therapeutic targets and developing proof-of-concept experiments in the future.
Creating a better healthcare environment: Usage of Big data recommendations
Zaya Sukhbat,Jaewon Choi 한국경영과학회 2018 한국경영과학회 학술대회논문집 Vol.2018 No.10
The healthcare industry has experienced much progress in data management and analysis. This includes more than a decade in the large-scale digitization of medical records, as well as the aggregation of research and development in electronic form. In addition, governments have also accelerated the move towards transparency, making stored data more accessible to the industry as a whole. Big data technologies have already made some impact in fields related to healthcare: medical diagnosis from imaging data in medicine, quantifying lifestyle data in the fitness industry, to mention a few. Although there is already a huge amount of healthcare data around the world and while it is growing at an exponential rate, nearly all of the data is stored in individual silos. Medical research has always been a data-driven science, with randomized clinical trials being a gold standard in many cases. However, due to recent advances in omics-technologies, medical imaging, comprehensive electronic health records, and smart devices, medica lresearch as well as clinical practice are quickly changing into “Bigdata-driven” fields.
Ranking universities by innovation potentials
Banzragch Mijiddorj,Zaya Mashlai,Bindirya Dugersuren 대한경영교육학회 2017 경영교육저널 Vol.28 No.1
One of key challenges for existing managers is making decision in their daily activities by selecting the best optimal and timely decisional choice. Therefore, choosing and using the optimal criteria, which positively influence in the right decision from multiple criteria, have become significantly important to make the best decision for solution a problem. This method of “Multiple criteria decision making” has been used frequently for last years in management practices. This article illustrates findings of rankings by innovation potentials of top public universities in Mongolia using TOPSIS method ranking them by18 criteria in five groups.