A General Framework for Complex Network Applications

Complex network theory has been applied to solving practical problems from different domains. In this paper, we present a general framework for complex network applications. The keys of a successful application are a thorough understanding of the real system and a correct mapping of complex network theory to practical problems in the system. Despite of certain limitations discussed in this paper, complex network theory provides a foundation on which to develop powerful tools in analyzing and optimizing large interconnected systems.Comment: 8 page

Acute Biliary Septic Shock

Forty-seven cases of biliary tract infection with septic shock are presented. The sepsis was caused by empyema of the gallbladder in 23 cases and by cholangitis in the remainder. Gallstones were most frequently the cause of the sepsis. An appropriate diagnostic description of the syndrome of biliary tract infection and septic shock should therefore include a description of the underlying biliary disease as well as the term acute biliary shock. In this series, emergency surgical management by removal of gallstones and drainage of suppuration was felt to be the most appropriate treatment. There was a high incidence of gallbladder rupture (10.6%) and intrahepatic stones (53.2%). Of the 13 patients who died, 8 might have survived if early operation had been performed after the diagnosis of acute biliary septic shock was established

On the Leibniz rule and Laplace transform for fractional derivatives

Taylor series is a useful mathematical tool when describing and constructing a function. With the series representation, some properties of fractional calculus can be revealed clearly. This paper investigates two typical applications: Lebiniz rule and Laplace transform. It is analytically shown that the commonly used Leibniz rule cannot be applied for Caputo derivative. Similarly, the well-known Laplace transform of Riemann-Liouville derivative is doubtful for n-th continuously differentiable function. By the aid of this series representation, the exact formula of Caputo Leibniz rule and the explanation of Riemann-Liouville Laplace transform are presented. Finally, three illustrative examples are revisited to confirm the obtained results

Development of Enhanced Emission Factor Through the Identification of an Optimal Combination of Input Variables Using Artificial Neural Network

A great deal of attention is being paid worldwide to particulate matter (PM), which is now considered a significant component of air pollution. Specifically, in this thesis, road dust is a primary source of PM that is having a significant impact on human health and air quality. For example, impaired visibility due to road dust can cause more vehicle accidents. Hence, in order to efficiently develop PM control strategies, it is critical to improve the estimation of PM concentration levels generating from paved and unpaved roads. Since 1979, the U.S. Environmental Protection Agency (EPA) has developed emission factor equations to quantify the magnitude of PM for paved and unpaved roads based on multiple linear regression (MLR) models. However, the MLR models are not suitable for PM data that exhibit the characteristics of complexity and non-linearity, thereby limiting the predictive accuracy of MLR to estimate PM. The objective of this thesis is to present a method to improve the quality of the existing EPA emission factor equations for paved and unpaved roads by employing an artificial neural network (ANN). The proposed method consists of the following steps: data processing for outliers, data normalization, data classification, ANN model training to determine the weights of emission factors identified, and method validation through additional data testing. This thesis included a case study using the data retrieved from the database used by the EPA to generate their emission factor equations for paved and unpaved roads. The proposed method was evaluated by demonstrating its improved performance as shown in the coefficient of determination (R2) and the root mean square error (RMSE) values compared to the values obtained with the existing EPA emission equations. The empirical findings of the case study verified that the proposed method using the ANN model is capable of improving the quality of the EPA emission equations, resulting in higher R 2 and lower RMSE values for both paved and unpaved roads. The expected significance of this thesis is that the proposed method improves the ability to develop more reliable emission factors for predictable PM levels that can help agencies establish enhanced PM control strategies. In addition, the method may have application in other fields that require a selection process to identify an optimal combination of input variables

A narrative review on prediabetes or diabetes and atrial fibrillation: From molecular mechanisms to clinical practice

Based on glucose levels, people fall into three groups, normal individuals, prediabetic patients, and diabetic mellitus (DM) patients. Prediabetes (pre-DM) is an intermediate condition that exists between normal glucose levels and DM. Atrial fibrillation (AF), one of the most prevalent cardiac arrhythmias in medical practice, contributes to a considerable morbidity and mortality rate. In this review, we looked at the clinical symptoms, pathological alterations, molecular mechanisms, and associated risk factors of pre-DM, type 2 DM (T2DM), and AF. In clinical practice, pre-DM can increase the prevalence of AF. In the hyperglycemic state, oxidative stress, inflammation, and endoplasmic reticulum stress can cause alterations in atrial cell or cardiac fibroblast function through tumor necrosis factor-α/nuclear factor-κB (NF-κB)/transforming growth factor-β, mitogen-activated protein kinase-matrix metalloproteinase-9 and PARP-1 is poly (ADP-ribose) polymerase 1. IκB kinase-α/NF-κB pathways, and further cause atria undergo structural, electrical, and neural remodeling which lead to the occurrence and persistence of AF. In addition, pre-DM and T2DM may worsen as a result of obesity, obstructive sleep apnea, and arterial hypertension. Furthermore, clinical researches have demonstrated that lifestyle interventions and/or pharmacotherapy in pre-DM patients can effectively delay the progresssion of pre-DM to T2DM. Individualized glycemic management and AF management should be provided to AF patients with pre-DM or DM

A geometrical theory for {111} <hkl> recrystallisation texture formation in cold rolled IF steel

The introduction of a conditional deformating banding into BCC rolling texture modellling has produced a clue as too the origins of the {111} texture as it develops from the γ fibre material. OIM has reveqaled that deformation bands are present in many deformed γ grains, which therefore produced the gometrical condition for successful nucleation of rotated ND material by either subgrain growth or SIBM. Clearly the roles of solutes and precipitates have to be established in this deformation microstructure.published_or_final_versio