Identifying Unnatural Variation in Precision Rotational Part Manufacturing

In the manufacturing industry, it is well known that in-process variation is a major contributor to poor quality products. In order to fabricate a precise part, the source of unnatural variation (UV) needed to be properly identified, monitored and controlled while the process is running. In relation to this issue, this study aims to identify the error root causes of UV in bivariate process associated with statistical process control (SPC) chart patterns. In research methodology, in-process variation in manufacturing roller head component was discussed systematically based on real product of roller head, computer aided design (CAD) and statistical process control (SPC) chart patterns. Initially, the CAD software was used to model a precise rotational part, and to analyse the cause of UV. Then, the programming software was used to generate the artificial SPC data streams based on an established mathematical model. Data generation also involved linear correlation between two dependent variables (bivariate). The outcome of this study would be helpful for industrial practitioners as a database when applying SPC for monitoring bivariate process

Control Chart Pattern Recognition Using Small Window Size for Identifying Bivariate Process Mean Shifts

There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. &nbsp

Control Chart Pattern Recognition Using Small Window Size for Identifying Bivariate Process Mean Shifts

There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. &nbsp

Antiplatelet drug ticagrelor enhances chemotherapeutic efficacy by targeting the novel P2Y12-AKT Pathway in pancreatic cancer cells

Background: Extensive research has reported that extracellular ADP in the tumour microenvironment can stimulate platelets through interaction with the platelet receptor P2Y12. In turn, activated platelets release biological factors supporting cancer progression. Experimental data suggest that the tumour microenvironment components, of which platelets are integral, can promote chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC). Thus, overcoming chemoresistance requires combining multiple inhibitors that simultaneously target intrinsic pathways in cancer cells and extrinsic factors related to the tumour microenvironment. We aimed to determine whether ticagrelor, an inhibitor of the ADP–P2Y12 axis and a well-known antiplatelet drug, could be a therapeutic option for PDAC. Methods: We investigated a functional P2Y12 receptor and its downstream signalling in a panel of PDAC cell lines and non-cancer pancreatic cells termed hTERT-HPNE. We tested the synergistic effect of ticagrelor, a P2Y12 inhibitor, in combination with chemotherapeutic drugs (gemcitabine, paclitaxel and cisplatin), in vitro and in vivo. Results: Knockdown studies revealed that P2Y12 contributed to epidermal growth factor receptor (EGFR) activation and the expression of SLUG and ZEB1, which are transcriptional factors implicated in metastasis and chemoresistance. Studies using genetic and pharmacological inhibitors showed that the P2Y12–EGFR crosstalk enhanced cancer cell proliferation. Inhibition of P2Y12 signalling significantly reduced EGF-dependent AKT activation and promoted the anticancer activity of anti-EGFR treatment. Importantly, ticagrelor significantly decreased the proliferative capacity of cancer but not normal pancreatic cells. In vitro, synergism was observed when ticagrelor was combined with several chemodrugs. In vivo, a combination of ticagrelor with gemcitabine significantly reduced tumour growth, whereas gemcitabine or ticagrelor alone had a minimal effect. Conclusions: These findings uncover a novel effect and mechanism of action of the antiplatelet drug ticagrelor in PDAC cells and suggest a multi-functional role for ADP-P2Y12 signalling in the tumour microenvironment

Factors Towards Voluntary Turnover Among Employees in Malaysia Banking Institution

Voluntary Turnover refers to the decision of an employee to leave their organization based on their own intention and self-control. Thus this condition may give negative affects to one organization in terms of monetary, productivity and time deficit. The paper aimed to explore the relationship between factors namely job satisfaction, career adaptability, turnover intention and pay towards voluntary turnover in the banking sector. Self-administered questionnaire is distributed conveniently among respondents from several departments known as Human Resources, Marketing, Retail Rehabilitation, Branch Operation Processing, Treasury and Investigation Audit. The study uses SPSS (Scientific Package for Social Sciences) to specified frequencies distribution, reliability analysis, correlation coefficient (Pearson’s) and regression analysis. The analysis proved that there is a significant relationship between voluntary turnover and three independent variables (turnover intention, job satisfaction and pay). The result revealed that the turnover intention is the most influence factor toward voluntary turnover.     Keywords: Voluntary Turnover; Job Satisfaction; Career Adaptability; Turnover Intention; Pa

Computational Methods for Stability and Control (COMSAC): The Time Has Come

Powerful computational fluid dynamics (CFD) tools have emerged that appear to offer significant benefits as an adjunct to the experimental methods used by the stability and control community to predict aerodynamic parameters. The decreasing costs for and increasing availability of computing hours are making these applications increasingly viable as time goes on and the cost of computing continues to drop. This paper summarizes the efforts of four organizations to utilize high-end computational fluid dynamics (CFD) tools to address the challenges of the stability and control arena. General motivation and the backdrop for these efforts will be summarized as well as examples of current applications

Neutrophil Extracellular Traps in Breast Cancer and Beyond: Current Perspectives on NET Stimuli, Thrombosis and Metastasis, and Clinical Utility for Diagnosis and Treatment

Abstract The formation of neutrophil extracellular traps (NETs), known as NETosis, was first observed as a novel immune response to bacterial infection, but has since been found to occur abnormally in a variety of other inflammatory disease states including cancer. Breast cancer is the most commonly diagnosed malignancy in women. In breast cancer, NETosis has been linked to increased disease progression, metastasis, and complications such as venous thromboembolism. NET-targeted therapies have shown success in preclinical cancer models and may prove valuable clinical targets in slowing or halting tumor progression in breast cancer patients. We will briefly outline the mechanisms by which NETs may form in the tumor microenvironment and circulation, including the crosstalk between neutrophils, tumor cells, endothelial cells, and platelets as well as the role of cancer-associated extracellular vesicles in modulating neutrophil behavior and NET extrusion. The prognostic implications of cancer-associated NETosis will be explored in addition to development of novel therapeutics aimed at targeting NET interactions to improve outcomes in patients with breast cancer

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations