Structural characteristics of camel-bone gelatin by demineralization and extraction

Camel bone was demineralized through HCl acidulation process at different concentrations (0.0%, 1.5%, 3.0%, and 6.0%) over 1–5 days. The level of demineralization was acid concentration and soaking time dependent. Highest demineralization (62.0%) was recorded in bone sample treated with 6.0% dilute acid for 5 days. Energy dispersive X-ray spectroscopy (EDX) elemental analysis revealed reduction in Ca and increase in N and H, while O remains unaffected. Particulate characteristics by scanning electron microscope showed an increased surface roughness of bone after demineralization. Fourier transform infrared (FT-IR) analysis of ossein depicted the presence of functional group similar to that of bone protein (collagen). Statistical optimization by central composite design (CCD) revealed a significant quadratic model for optimum values of extraction temperature, pH, and extraction time. The highest gelatin yield from camel bone was 23.66% at optimum extraction condition (71.87°C, pH 5.26, and 2.58 h) and the bloom was 205.74 g. Camel bone is suitable for production of gelatin with good potentials in food and nonfood applications. © 2017 Taylor & Francis Group, LLC

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis

Bone Marrow Transplant, a gradational rescue for a wide range of disorders emanating from the bone marrow, is an efficacious surgical treatment. Several risk factors, such as post-transplant illnesses, new malignancies, and even organ damage, can impair long-term survival. Therefore, technologies like Machine Learning are deployed for investigating the survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient survival classification model is presented in a comprehensive manner, incorporating the Chi-squared feature selection method to address the dimensionality problem and Hyper Parameter Optimization (HPO) to increase accuracy. A synthetic dataset is generated by imputing the missing values, transforming the data using dummy variable encoding, and compressing the dataset from 59 features to the 11 most correlated features using Chi-squared feature selection. The dataset was split into train and test sets at a ratio of 80:20, and the hyperparameters were optimized using Grid Search Cross-Validation. Several supervised ML methods were trained in this regard, like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have been performed for both the default and optimized hyperparameters by using the original and reduced synthetic dataset. After ranking the features using the Chi-squared test, it was observed that the top 11 features with HPO, resulted in the same accuracy of prediction (94.73%) as the entire dataset with default parameters. Moreover, this approach requires less time and resources for predicting the survivability of children undergoing BMT. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.Comment: 25 pages, 14 figures, 38 table

Attaining a sustainable competitive advantage through service co-creation with strategic partners: Measurement Company X

The thesis aims to examine the importance of collaborative activities with customers in product and service development to focus on sustainability. This thesis is carried out to establish a value co-creation framework, which will be customer-centric and sustainable. Sustainability is an organization’s strong attribute since the world is moving continuously with changes. The change is happening following the global trend and economy. Customers are an important element of the market. Market demands are created by customers depending on the current situation. Considering all these consequences, this study is starting to find out how to develop a co-creation model for Company X. Several challenges have been worked out and sorted with specific outcomes. The research questions for this study aim to form a proactive service co-creation approach with key stakeholders in the strategic business network to enhance company X's industry leadership position in the next decade. Furthermore, it also finds out the strategic partners of Company X, their activities, and their type of interactions. This thesis follows the qualitative interview method to conduct research in developing the current model of the existing activities of the employees of Company X and propose a new co-creation model with customers. After the interview sessions, a workshop was done with all the informants to discuss the summary of the findings from the interview. Thematic analysis is done with the finding of the interview sessions. To validate the proposed research framework a questionnaire survey has been done at the end of this research. Following thematic analysis from the feedback of the interviews, Company X’s current working model is found. The co-creation model has also been suggested following the interview feedback, which took place with different company employees. This study discovers several findings and related challenges with existing working situations. And finally, it established a co-creation model, which can bring im-portance to the company to be sustainable in the market

Evaluation of adhesive binders for the development of yarn bonding for new stitch-free non-crimp fabrics

Non-crimp fabrics (NCFs), especially multi-axial warp-knitted fabrics, are used as reinforcement materials for fiberreinforced composites. The manufacturing of multi-axial warp-knitted fabrics by a conventional stitch bonding process to produce NCF has several disadvantages, such as filament damage, low production speed, yarn disorientation, etc. In order to overcome the existing limitations, the idea of using an adhesive binder to attach the fabric layers is a promising approach, so that the use of stitching yarns can be eliminated. The fundamental investigations presented in this paper show that the selection of the binder material has a major influence on the parameters of the textile products. Whereas the tested hotmelt adhesives offer a short curing time and a small but nevertheless sufficient bonding strength between bonded yarns, the tested reactive adhesives show a bonding strength up to 10 times higher, but at a considerably longer curing time. The reason for the different bonding strength is identified in the different penetration into the yarns. The experiments also show a significant influence of the fiber type and sizing, which needs to be taken into account when selecting fabric binders