Design concept evaluation based on rough number and information entropy theory

Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes

A review of high‐velocity impact on fiber‐reinforced textile composites: potential for aero engine applications

Considerable research has indicated that fiber-reinforced textile composites are significantly beneficial to the aerospace industry, especially aero engines, due to their high specific strength, specific stiffness, corrosion resistance, and fatigue resistance. However, damage caused by high-velocity impacts is a critical limitation factor in a wide range of applications. This paper presents an overview of the development, material characterizations, and applications of fiber-reinforced textile composites for aero engines. These textile composites are classified into four categories including two-dimensional (2D) woven composites, 2D braided composites, 3D woven composites, and 3D braided composites. The complex damage mechanisms of these composite materials due to high-velocity impacts are discussed in detail as well

The regulatory roles of Notch in osteocyte differentiation via the crosstalk with canonical Wnt pathways during the transition of osteoblasts to osteocytes

Osteocytes comprise more than 90% of the cells in bone and are differentiated from osteoblasts via an unknown mechanism. Recently, it was shown that Notch signaling plays an important role in osteocyte functions. To gain insights into the mechanisms underlying the functions of Notch in regulating the transition of osteoblasts to osteocytes, we performed a luciferase assay by cloning the proximal E11 and dentin matrix acidic phosphoprotein 1 (DMP1) promotor regions into pGluc-Basic 2 vectors, which were subsequently transfected into the IDG-SW3 (osteocytes), MC3T3 (osteoblasts) and 293T (non-osteoblastic cells) cell lines. Two approaches were used to activate Notch signaling in vitro. One was a Notch1 extracellular antibody-coated cell culture plate, and the other was transfection of a Hairy/Enhancer of Split 1 (Hes1) overexpression vector. The interaction between the Notch and Wnt signaling pathways was probed by assessing the expression of a series of phosphorylated proteins involved in the cascade of both signaling pathways. Our data suggested that Notch signaling regulates E11 expression through Hes1 activity, while Hes1 solely did not initiate the expression of DMP1. The regulatory function of E11 by Hes1 was not observed in the 293T cell line, indicating a cell context-dependent manner of the Notch signaling pathway. Additionally, we found that Notch inhibited Wnt signaling at the late differentiation stage of osteocytes by both directly repressing phosphorylated Akt and preventing the nuclear aggregation of β-catenin. These findings provide profound understandings of Notch's regulatory function in osteocyte differentiation

Blood clot formed on rough titanium surface induces early cell recruitment

Objectives\ud \ud The initial contact of blood with biomaterials and subsequent recruitment of inflammatory and marrow-derived stromal cells are among the first phases of bone regeneration. The aim of this study was to investigate the migratory potential of mesenchymal stem cells by treating rat bone marrow mesenchymal stromal cells (rBMSCs) with the extract of the blood clot formed on implant surfaces.\ud \ud Materials and methods\ud \ud Cell attachment and morphology on the blood clot was observed using scanning electron microscopy. The cell metabolism was reflected by the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay, and the cell proliferation was assessed by the CyQuant® assay based on DNA content. Cytokine profiles in the incubation medium derived from different blood–titanium surface were detected using the rat cytokine antibody array. Scratch wound assay and transwell migration assay were performed to determine the effect of blood–implant conditioned medium on cell migration and movement.\ud \ud Results\ud \ud No significant difference was found in cell attachment and morphology on the blood clot formed on smooth and rough surfaces. Increased rBMSC proliferation was induced by the blood clot on rough surfaces. Comparison of cytokine secretion showed a significant increase of CINC-2α, IL-2, L-selectin, MCP-1, prolactin AA and VEGF levels in the elution of blood clot formed on rough titanium surfaces, which led to significantly improved mobility and wound healing ability of rBMSCs.\ud \ud Conclusions\ud \ud Rough titanium surfaces could influence the blood clot formation and properties, which will induce cell recruitment and stimulate wound healing

Cross domain knowledge cell clustering method for biologically inspired design

To tackle the problem existing in the process of cross-domain knowledge acquisition in biologically inspired design, a functional semantic clustering based on functional feature semantic correlation and an environment-based clustering based on environment-constrained adaptability for biologically inspired design are proposed. On the one hand, the fuzzy theory and fuzzy mathematics are introduced into the knowledge cell clustering algorithm, and the semantic similarity calculation method based on the fuzzy membership function is proposed to realize the semantic clustering based on the functional keywords. On the other hand, an AFCM algorithm is proposed by introducing the FCM clustering algorithm into the knowledge cell clustering process, and combining the provided different types of environmental feature constraints similarity algorithm, the environment constra-ined clustering based on the adaptability of environmental feature constraints is achieved. Finally, the corresponding prototype system is developed, and the visual prosthesis device design is tested. The results show that the clustering time and accuracy are greatly improved and the clustering efficiency is improved significantly. The algorithm avoids effectively the discreteness of cross domain knowledge distribution, reduces the number of the research objects during the design process, and can acquire reasonably the existing design knowledge, which establishes a basis for further study

Improved carrier phase shift modulation and voltage equalization control strategy in modular multilevel converter

In order to solve the problem of traditional carrier phase-shift modulation with multiple ratios or PI controllers and cumbersome tuning parameters, this paper uses improved carrier phase-shift modulation. The total turn-on number of sub-modules each bridge arm is determined by comparing the sinusoidal modulated wave with the triangular carrier, and then the control signal is generated according to the capacitance voltage sorting result and the bridge arm current polarity. However, this modulation method uses a sorting method that causes the insulated gate bipolar transistor (IGBT) have an excessively high switching frequency. Therefore, a sorting trigger condition that can effectively reduce the switching frequency is used. The method determines whether to reorder based on the error between the voltage average and the actual value. For the circulation problem, the double-frequency negative sequence component is extracted by rotating coordinate transformation, and it is suppressed by PI control. A 21-level MMC model was built in MATLAB/simulink to analyze the sub-module capacitor voltage fluctuation, output current, voltage distortion rate and bridge arm circulation. It is verified that the modulation method can combine the sorting algorithm and circulation suppression method at the same time, and has better voltage equalization and circulation suppression effects

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2− breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2− cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann–Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2− groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively

The Application of Quantitative 1H-NMR for the Determination of Orlistat in Tablets

A quantitative nuclear magnetic resonance (qNMR) method to measure the content of Orlistat in tablets was studied and found to be efficient, accurate, reliable, and simple. In this paper, phloroglucinolanhydrous and dimethylsulfoxide-d6 (DMSO-d6) served as the internal standard and solvent, respectively. The qNMR methodology, including the linearity, range, the limit of detection (LOD) and quantification (LOQ), stability, precision, and accuracy, was validated seriatim, and the results were very favorable. The content determination results of three batches of Orlistat in tablets were almost identical upon comparing the qNMR method and the high-performance liquid chromatography (HPLC) method. The recommended method authentically compensated the deficiencies of the current HPLC method for determining Orlistat content, and proved to be a method complementary to traditional analysis for the purity measurement of Orlistat in some pharmaceutical preparations

Sm-CeO₂/Zeolite Bifunctional Catalyst for Direct and Highly Selective Conversion of Bioethanol to Propylene

A series of Sm-CeO2/Beta composites with various Beta contents were prepared by an incipient impregnation method, followed by calcination at 650 °C. They were characterized by XRD, N2 adsorption, SEM, NH3-TPD, CO2-TPD and 27Al MAS NMR. The Sm-CeO2/Beta bifunctional catalysts exhibit eminent catalytic performances in the selective conversion of ethanol to propylene. In particular, the Sm-CeO2/10%Beta catalyst with 10% Beta zeolite gives the highest C3H6 yield of 59.3%. A good match between Sm-CeO2 and Beta accounts for its optimal result