Performance evaluation analysis of Ti-6Al-4V foam fan blades in aircraft engines: a numerical study

In the aerospace industry, the structures are subjected to significant loads and extreme conditions whilst being required to be lightweight and resilient. Metallic foams seem to meet these criteria. However, their usage in the aerospace applications are not as common as one would expect. To explore a potential application of foams, this study evaluates the performance of the foams of Ti-6Al-4V, a conventional material/alloy for aircraft engine fan blade applications performing numerical simulations. First, the mechanical properties of the Ti-6Al-4V alloy are calculated using the Mori–Tanaka mean-field homogenisation and finite element (FE) methods employing representative volume elements (RVE). Using those calculated material properties and the computer-aided design (CAD) model of a representative aircraft engine fan blade, the FE models are built. In these numerical models, the material properties and the rotational speed with the static aero-loads are selected as variables, whilst boundary conditions remain consistent to ensure a systematic investigation. Stress analysis and the prestressed modal analyses of the blades are performed, and the results are presented to discuss the impact of the void volume fraction of the alloy foams. This study reveals the complex nature of the mechanics of fan blades when made of foams

Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that inflate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility

Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls

Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with special attention to the validity and representativeness of social media big data analyses. Persistent issues include the over-emphasis of a single platform, Twitter, sampling biases arising from selection by hashtags, and vague and unrepresentative sampling frames. The socio-cultural complexity of user behavior aimed at algorithmic invisibility (such as subtweeting, mock-retweeting, use of "screen captures" for text, etc.) further complicate interpretation of big data social media. Other challenges include accounting for field effects, i.e. broadly consequential events that do not diffuse only through the network under study but affect the whole society. The application of network methods from other fields to the study of human social activity may not always be appropriate. The paper concludes with a call to action on practical steps to improve our analytic capacity in this promising, rapidly-growing field.Comment: Tufekci, Zeynep. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. In ICWSM '14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 2014. [forthcoming

Comparison of the Effectiveness of Single and Double Surface Light Emitting Diodes Phototherapy and Intensive Compact Fluorescent Phototherapy in the Treatment of Neonatal Hyperbilirubinemia

Aim: In newborns with extremely high serum total bilirubin levels, the phototherapy method that reduces serum total bilirubin levels most rapidly should be applied to reduce the need for exchange transfusions and thus prevent the development of acute and/or chronic bilirubin encephalopathy. The aim of this study was to compare the efficacy of single or double light-emitting diode (LED) and intensive compact fluorescent tube (CFT) phototherapy in the first 4 hours of treatment for hyperbilirubinemia. Methods: The study was a retrospective analysis of prospectively collected data, and designed as a single-center, cross-sectional study. Sixty newborns born between 35 and 42 weeks of gestation and treated with intensive phototherapy were included in the study. Total serum bilirubin (TSB) levels were measured 4 hours after the initiation of treatment in neonates who received LED or CFT phototherapy, and the efficacy of these methods was compared. Results: The rate of decline in TSB was 1.07 mg/dL/h in CFT, 0.74 mg/dL/h in double LED, and 0.44 mg/dL/h in single LED phototherapy. Compact fluorescent tube and double LED phototherapy were found to be more effective than single LED phototherapy (p<0.01, p<0.01). Conclusion: In neonates with hyperbilirubinemia, intensive CFT or double LED phototherapy in the first few hours of treatment may reduce the risk of bilirubin encephalopathy

The Endotoxin-Induced Neuroinflammation Model of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the progressive loss of dopaminergic (DA) neurons in the substantia nigra. Although the exact cause of the dopaminergic neurodegeneration remains elusive, recent postmortem and experimental studies have revealed an essential role for neuroinflammation that is initiated and driven by activated microglial and infiltrated peripheral immune cells and their neurotoxic products (such as proinflammatory cytokines, reactive oxygen species, and nitric oxide) in the pathogenesis of PD. A bacterial endotoxin-based experimental model of PD has been established, representing a purely inflammation-driven animal model for the induction of nigrostriatal dopaminergic neurodegeneration. This model, by itself or together with genetic and toxin-based animal models, provides an important tool to delineate the precise mechanisms of neuroinflammation-mediated dopaminergic neuron loss. Here, we review the characteristics of this model and the contribution of neuroinflammatory processes, induced by the in vivo administration of bacterial endotoxin, to neurodegeneration. Furthermore, we summarize the recent experimental therapeutic strategies targeting endotoxin-induced neuroinflammation to elicit neuroprotection in the nigrostriatal dopaminergic system. The potential of the endotoxin-based PD model in the development of an early-stage specific diagnostic biomarker is also emphasized

The Nrf2/ARE Pathway: A Promising Target to Counteract Mitochondrial Dysfunction in Parkinson's Disease

Mitochondrial dysfunction is a prominent feature of various neurodegenerative diseases as strict regulation of integrated mitochondrial functions is essential for neuronal signaling, plasticity, and transmitter release. Many lines of evidence suggest that mitochondrial dysfunction plays a central role in the pathogenesis of Parkinson's disease (PD). Several PD-associated genes interface with mitochondrial dynamics regulating the structure and function of the mitochondrial network. Mitochondrial dysfunction can induce neuron death through a plethora of mechanisms. Both mitochondrial dysfunction and neuroinflammation, a common denominator of PD, lead to an increased production of reactive oxygen species, which are detrimental to neurons. The transcription factor nuclear factor E2-related factor 2 (Nrf2, NFE2L2) is an emerging target to counteract mitochondrial dysfunction and its consequences in PD. Nrf2 activates the antioxidant response element (ARE) pathway, including a battery of cytoprotective genes such as antioxidants and anti-inflammatory genes and several transcription factors involved in mitochondrial biogenesis. Here, the current knowledge about the role of mitochondrial dysfunction in PD, Nrf2/ARE stress-response mechanisms, and the evidence for specific links between this pathway and PD are summarized. The neuroprotection of nigral dopaminergic neurons by the activation of Nrf2 through several inducers in PD is also emphasized as a promising therapeutic approach