An investigation of the melt blown web defect known as shot using metallocene and ziegler-natta based polypropylene resins

An extensive experimental investigation was undertaken to study the influence of processing parameters and resin type on the production of shot in the melt blowing process. Both early generation commercial metallocene and conventional Ziegler-Natta based polypropylene resins were used in this investigation. In addition to the base metallocene and Ziegler-Natta resins, the effect of blending these resins together was studied. Finally, the effect of adding nucleating agents to the base metallocene resin on shot production was investigated. The study showed that the early generation commercial metallocene resin used in this research produced more shot than conventional resins with all other processing conditions being equal. Die air pressure had a strong effect on the shot production; an increase in die air pressure produced more shot in the web sample. Processing temperature produced a similar trend; an increase in process temperature produced more shot in the web sample. Blending Ziegler-Natta polypropylene into a metallocene based polypropylene tended to decrease the shot formation in proportion to the percentage of the conventional resin. Examination of the crystallization kinetics of the base resin indicated that the Ziegler-Natta base resin crystallized faster under quiescent conditions than the metallocene base resin. Similarly, measurements on the blends indicated a gradual increase of crystallization kinetics from that of the metallocene resin to that of the Ziegler-Natta resin

Investigating The Role of AEG-1 in Mouse Models of Pain

Background: Astrocyte Elevated Gene 1 (AEG-1) is a multifunctional protein shown to be a regulator of transcription and multiple intracellular signaling pathways. The role of AEG-1 in cellular inflammation appears to be primarily facilitated by its direct interaction with the transcription factor NFκB, transcriptional regulator of inflammatory cytokines. May be have a potential role in models of pain, particularly chronic inflammatory and chemotherapy induced peripheral neuropathy (CIPN). Methods: C57BL6/J male and female mice, 8-14 weeks old. AEG-1 wild type (WT) and global knockout (KO) male and female mice, 8-14 weeks old. Chronic Inflammatory Pain induced via i.pl. injection of 50% Freund\u27s Complete Adjuvant (CFA) or vehicle into mouse right hind paw. CIPN induced via four 8 mg/kg, i.p. injections of Paclitaxel or vehicle (Toma, et. al). Mechanical hypersensitivity assessed via von frey filaments. Acetone Test was used to assess cold sensitivity. mRNA transcripts collected from tissues were measured via qRT-PCR. Results: AEG-1 KO mice displayed protection from CFA induced mechanical hypersensitivity, thermal sensitivity, and reduces paw edema compare to WT mice. AEG-1 KO mice displayed enhanced recovery from paclitaxel induced mechanical hypersensitivity and cold sensitivity compared to WT mice. AEG-1 expression levels in the periaqueductal grey, spinal cord, and L4-6 corresponding dorsal root ganglia collected from C57BL6/J mice treated with 8mg/Kg paclitaxel or 50% CFA (3 days post injection) showed no difference from control groups. Conclusions: Our data suggest that AEG-1 may be involved in inflammatory and CIPN related nociception in C57BL6/J mice.https://scholarscompass.vcu.edu/gradposters/1093/thumbnail.jp

Modelling of porosity defects in high pressure die casting with a neural network

High Pressure Die Casting (HPDC) is a complex process that results in casting defects if configured improperly. However, finding out the optimal configuration is a non-trivial task as eliminating one of the casting defects (for example, porosity) can result in occurrence of other casting defects. The industry generally tries to eliminate the defects by trial and error which is an expensive and error -prone process. This paper aims to improve current modelling and understanding of defects formation in HPDC machines. We have conducted conventional die casting tests with a neural network model of HPDC machine and compared the obtained results with the current understanding of formation of porosity. While most of our findings correspond well to established knowledge in the field, some of our findings are in conflict with the previous studies of die casting.<br /

Improving the quality of die castings by using artificial neural networks for porosity defect modelling

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.<br /

Mixed transfer function neural networks for knowledge acquistition

Modeling helps to understand and predict the outcome of complex systems. Inductive modeling methodologies are beneficial for modeling the systems where the uncertainties involved in the system do not permit to obtain an accurate physical model. However inductive models, like artificial neural networks (ANNs), may suffer from a few drawbacks involving over-fitting and the difficulty to easily understand the model itself. This can result in user reluctance to accept the model or even complete rejection of the modeling results. Thus, it becomes highly desirable to make such inductive models more comprehensible and to automatically determine the model complexity to avoid over-fitting. In this paper, we propose a novel type of ANN, a mixed transfer function artificial neural network (MTFANN), which aims to improve the complexity fitting and comprehensibility of the most popular type of ANN (MLP - a Multilayer Perceptron).<br /

The electrical properties of Au/GaN and PEDOT: PSS/GaN diodes

In the present paper, using a numerical simulator, the simulation of Au/n-GaN and PEDOT: PSS/GaN structures were performed in a temperature at room temperature. The electrical parameters: barrier height, ideality factor, shunt resistance series, and resistance have been calculated using different methods: conventional I-V, Norde, Chattopadhyay, and Mikhelashvili. Statistical analysis showed that the Au/GaN structure has a barrier height of (0.6 eV) which is higher compared with the PEDOT: PSS/GaN structure (0.72 eV) and ideality factor (1.88 and 2.26) respectively. The values of resistance shunt were increased from 77150.056 Ω to 11207586 Ω. It is observed that the leakage current increased from 6.64E-5 to 4.98926E-5A at −0.85 V

CLINICAL HYPOGLYCEMIC EFFECTS OF ALLIUM CEPA (RED ONION) IN TYPE 2 DIABETIC PATIENTS

Background: Type 2 diabetes mellitus results from defects in insulin secretion and/or insulin action or both. Objectives: The present study was conducted to investigate the hypoglycemic effects of Allium cepa in patients with type 2 diabetes mellitus. &nbsp; Results: In type 2 diabetic patients (n=21) the administration of crude Allium cepa (100g) markedly reduced fasting blood glucose levels by 40 mg/dl 4 hours later, compared to glibenclamide (81 mg/dl). Also Allium cepa significantly reduced the induced hyperglycemia (GTT) after ingestion of 75 grams dextrose by 159 mg/dl in the test subgroup (n=7) of type 2 diabetic patients to a point below that produced in the negative control group after 4 hours. Conclusion: Crude Allium cepa produced hypoglycemic effects, thus it could be used as a dietary supplement in management of diabetes