Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Status of Gulf War and Era Veterans Serving in the US Military in 2000

Objective: This research describes Gulf War and era veterans enrolled in the Millennium Cohort Study, who were sampled from US military personnel serving in 2000, and compares health characteristics of this sample to a Department of Veterans Affairs study sampled from the complete population. Methods: Demographics characteristics of this sample were described. Self-reported health characteristics were compared between the two studies. Results: Gulf War and era veterans in the Millennium Cohort were generally healthier than in the VA study; they had fewer medical conditions and mental health disorders and better self-reported health. In both studies, Gulf War veterans had poorer health outcomes than era veterans. Conclusion: The Millennium Cohort Study is a unique resource for examining the long-term health effects of Gulf War deployment, particularly comparing deployed and nondeployed personnel and examining illnesses with long latencies.
From Medical Images to Simulations : 4 D MRI Flow Analysis

According to World Health Organization estimates, 17 million people around the globe die of cardiovascular disease (CVD) each year. About 600 million people with high blood pressure are at risk of heart attack, stroke and cardiac failure. Low and middle-income countries contributed to 78 percent of CVD deaths. By 2010 CVD is estimated to be the major cause of death in developed countries. This huge impact has motivated the development of new non-invasively techniques in order to understand cardiovascular pathologies and interventions. The effect of blood flow on arterial wall remodeling the investigation of vascular hemodynamics within the vascular vessels is of great interest. It has the potential to greatly enhance the understanding of the pathogenesis and progression of vascular diseases and to aid in the decision of whether treatment is warranted. Non-invasive magnetic resonance imaging (MRI), with its intrinsic sensitivity to blood flow, offers the unique possibility to simultaneously acquire morphology and spatially coregistered hemodynamic information noninvasively. ABSTRACT: Medical imaging techniques, such as MRI and CT scanning, are valuable tools for getting a lot of information non-invasively and it is useful for reconstructing the geometry of complex objects about the patients. Medical-GiD is a medical image platform that incorporates a module to read directly the blood velocity profile from the MR scan, in particular for deformable registration of 4D MRI images, Electrocardiography (ECG)-synchronized and respiration controlled 3D magnetic resonance (MR) velocity mapping (flow-sensitive 4D MRI), 3D morphologic and 3-directional blood flow data. Furthermore, Medical-GiD is focus in the medical image processing in the biomechanical research field to generating meshes from the medical images, to apply in Computational Fluid Dynamics (CFD) or structural mechanics (stress analysis). To date, these techniques have largely been applied to compute meshes for numerical simulations, but with Medical-GiD, we will have the integration between the real data and numerical simulations.
Nephrectomy for benign disease? A case of isolated renal echinococcosis

Abstract Cystic hydatid disease may be found in virtually any organ, although involvement of the urinary tract is relatively uncommon. We report a case of isolated renal hydatid disease presenting with hydatiduria. A short review of the literature regarding diagnosis and management of renal echinococcosis is also presented.
The Use of 2 , 4-D for Selective Control of an Early Infestation of Eurasian Watermilfoil in Loon Lake , Washington

A patchy distribution of Eurasian watermilfoil (Myriophyllum spicatum L.) in Loon Lake was treated with the herbicide 2,4-D during July 1998. Aquatic plant biomass and frequency data were collected before treatment, and six weeks and one year after treatment. Aqueous concentrations of 2,4-D increased to 1 to 2 mg/l within one day of treatment, and were below detection limits by one week after treatment. Macrophyte data were analyzed to assess the herbicide’s impacts on Eurasian watermilfoil as well as the rest of the aquatic plant community. Results showed a significant decrease in Eurasian watermilfoil biomass and frequency in treated areas 6 weeks after treatment, which continued through the one year posttreatment samples. No other plant species were significantly affected by the herbicide application.
Optimal Path Pair Routes through Multi-Criteria Weights in Ad Hoc Network Using the Genetic Algorithm

An ad hoc network can establish cooperative communication through path pair routes. The path pair route formed depends on the number of hops and multi-criteria used. The cross-layer criteria observed is power consumption, signal-to-noise ratio (SNR), and load variance optimized using multi-criteria optimization through scalarization with varying weights. With many path pair routes and complicated computing then in finding the optimal value genetic algorithm method was used. From the simulation results, the optimal path pair routes were obtained with varying weights; greater weight had higher priorities and produced optimum performance and computing time for scalarization function with varying weights having a very small difference even almost identical. Different computing time will be seen when compared in an exhaustive manner.
Factors Affecting Employment in Food and Beverage Industries within Mazandaran Province, Iran

The linkages between agricultural and nonagricultural activities in rural areas are strong, and the latter establishes a significant sources of employment and income for rural families. A relatively large share of the agricultural labour force in Iran will have to be relocated in non‐agricultural occupations. This is not an option but rather a necessity. If measures are not taken soon to encourage a more rapid inter‐sectorial movement of labour, circumstances will force it upon the economy. Unemployment has been known as the most important challenges of Iran economy during the last decades. Food and beverage industries had leading role in creation of employment in industrial sector in Iran. In spite of large number of workforce in the agricultural sector, job creation potential is low in Mazandaran Province so, development of non‐agricultural sector should be prioritized in the region. The present paper, has attempted to study the factors which influence number of employment in food and beverage industries within Mazandaran Province. Time series data of employment (EM), Labour productivity (RLP), the cost of energy (RFC), investment (RIC) and spending on other inputs (ROIC) were applied for the time period of 1991‐2010.The required data sets were gathered from the database of Iranian Statistics Center and Central Bank of Iran. The methodology was based on Fomby approach and the selection of the best model was run by vector error correction model (VECM). Johansen‐Juselius’s co‐integration test revealed a long‐run balanced relationship among the mentioned variables. The results of VECM also revealed that in a long‐run, 1% change in ROIC, RFC, RLP and RIC resulted in 6.17%, 11.45%, 3.30% and 2.47% changes in employment, respectively. According to the results the government investment in food and beverage industries can considerably solve the problem of unemployment in both short‐term and long‐term within Mazandaran Province.
Discovery of Community Structures in Static and Dynamic Networks

With the development of computer technology, researchers are able to observe and collect enormous amount of data, where the independent and identical distributed assumption is violated. For example, in sociology, individuals in an organization interact with each other to change the underlying social structure; in biology, understanding the gene-gene interaction helps researchers to detect potential diseases; in politics, voters are mutually influenced before the election via private/public speeches and parades, which might ultimately change the election results. It is crucial to study how individuals interact with each other from the data, which would lead to tremendous contributions to the society. Centuries ago, mathematicians started to describe the interaction of objects with mathematical language in the field of graph theory. The concepts of vertices/nodes and edges are the cornerstone of graph theory. Vertex can be used to describe individual, and edge is a way to portray interaction between a pair of vertices. Taking advantage of the accumulated discoveries in graph theory, statisticians are able to develop stochastic models to make inference of the data, which can be represented by network structures. My main research goal is to develop statistical models to discover the underlying community structure in various types of network data, including a snap shot of a network and time-varying network. The word "community" is an intermediate concept between a single node and the whole network, and can refer to a partition, a block structure, etc. Additionally, I desire to make my models be feasible to large size data, so that gigantic networks, e.g. social network, can be analyzed using my con-
Prognostic significance of failure to cross esophageal tumors by endoluminal ultrasound.

SUMMARY Failure to intubate and cross esophageal tumors by endosonography is reported in as many as 30% of cases and is thought to be associated with an especially poor prognosis. The aim of this study was to audit the above in a large consecutive case series of Endoscopic Ultrasound (EUS) examinations for esophageal cancer performed in a regional specialist cancer network with particular reference to outcome. A consecutive series of 411 patients underwent EUS examination by a specialist radiologist over a period of 9 years. Forty (10%) of patients required dilation, and there was total failure to cross the tumor in 12 patients (2.9%). Failure to traverse the primary tumor was associated with a diagnosis of squamous cell cancer (8 of 12 patients, 66%, rho = -0.182, P = 0.011). Limited staging information was obtained in 7 of these patients, which altered the computed tomography stage in 5 patients (71%, 3 upstaged, 2 downstaged). Six patients received definitive chemoradiotherapy, two patients surgery and four patients palliative chemotherapy. The median and 5-year survival in patients whose tumors were not crossed was 10 months and 28%, respectively, compared with 24 months and 24%, respectively in patients whose tumors were fully assessed. Failure to cross esophageal tumors in practice was far less common than the literature suggests, and esophageal tumor luminal stenosis should no longer be considered a limitation of endosonography.
Optimizing LSSVM Using ABC For Non-Volatile Financial Prediction

Inflation is one of the primary issues in economics where numerous studies about it have been done extensively. These days, the world economy instability not only gives negative impact to the whole world, but they also become as the catalyst to the investors to invest in commodity that is hedge against inflation, which is gold. This yellow metal commodity acts as a strong shield against inflation, investment and economic instability. In financial field, it posses low volatility feature where the price of gold does not easily face with tremendous fluctuation and also not affected by inflation. Thus, accurate prediction of gold price is vital in determining time to buy and sell in order to help the investor to gain profit and reduce risk. This paper proposes supervised learning approach, Least Squares Support Vector Machines (LSSVM) to predict gold price prediction. The performance of LSSVM depends on the value of its hyper-parameters, which are regularization parameter γ and kernel parameter σ. This is due to allow the model to yield good generalization performance. Thus, the application of new Swarm Intelligent technique Artificial Bee Colony (ABC) is applied to obtain the ideal value of LSSVM’s hyper parameters. For experimental purposes, three important features in predicting gold prices are employed. The first two features are commodity prices, which are the world gold price based on London Fix and the world crude oil price. One commodity-relevant macro financial variable, U.S. dollar exchange rate data were also been employed. The implementation of the proposed model was executed using MATLAB. The comparisons are made in term of Mean Square Error (MSE) and prediction accuracy. Compared with typical nonlinear intelligent model, Back Propagation Neural Network (BPNN), the proposed approach shows that the combination of LSSVM and ABC leads to a better performance. This finding indicates that the integration of ABC-LSSVM learning paradigm is a promising approach for non-volatile financial prediction.
Type-2 fuzzy Approach for Disease-Associated Gene Identification on Microarrays

Fuzzy set theory has been widely used in the analysis of gene microarray data. However, due to noise and uncertainty inherent in microarray data, traditional fuzzy methods sometimes do not perform well. In this paper, we propose a type-2 fuzzy membership test (Type-2 FM test) for disease-associated gene identification on microarrays to improve traditional fuzzy methods. We apply this method on diabetes and lung cancer microarrays and make a comparison with traditional fuzzy methods. For diabetes data, we can identify 7 genes which have been confirmed to be related to diabetes treatment in the published literature and one more gene can be identified than original approaches. For lung cancer data, we can also identify 7 genes which have been confirmed to be associated with lung cancer treatment in published literature and the type-2 d-values are significantly different. The results show that our type-2 FM test performs better than traditional fuzzy methods when analyzing microarray data with similar expression values and noise.
Biofouling of reverse osmosis membranes in a process water treatment system in a gold mine

Bacterial biofilms commonly cause biofouling in reverse osmosis (RO) membranes. The biofilm confers a protective environment to the bacteria, improving their tolerance against stress factors. In this study, bacterial biofouling was studied in a full-scale RO system, producing process water for a Finnish gold mine. Raw water bacterial levels corresponded to levels in natural waters. During normal RO operation, bacteria attached to the membrane did not detach to the concentrate water. In contrast, during washing, a high bacterial disperse from the membrane biofilm was observed. Bacterial community analysis suggests a dominant bacterium to be a biofouling indicator in this RO system.
Rheology of Aqueous Dispersions of Hydrogenated Castor Oil

Abstract Three major hydrogenated castor oil crystal morphologies have been observed: fiber, rosette and irregular crystal. Due to the difficulty in obtaining samples with a single crystal morphology, rheological studies of suspensions containing mixtures of the three morphologies in an aqueous solution have been undertaken. The viscometry of dilute suspensions has shown that the magnitude of intrinsic viscosity is dominated by the fraction of a crystal morphology type, i.e. fiber > rosette > irregular crystal. A modified Farris model was fitted to the rheology data for mixtures of crystal morphology with interacting particles. A yield stress exists for concentrated suspensions followed by a shear thinning behavior with the increase of shear rate. A power-law relation has been found between yield stress and total particle volume fraction, and a constant exponent of 1.5 has been obtained regardless of crystal morphology.
Adaptive MultiTask Compressive Sensing for Localization in Wireless LANs

The spatially distributed sparsity of the mobile devices (MDs) in indoor wireless local area networks (WLANs) makes compressive sensing (CS) based localization algorithms feasible and desirable. In this paper, we exploit the most recent developments in CS to efficiently perform localization in WLANs and design an accurate indoor localization scheme by taking advantage of the theory of multi-task Bayesian compressive sensing (MBCS). The proposed scheme assembles the strength measurements of signals from the MDs to distinct access points (APs) and jointly utilizes them at a central unit or a specific AP to achieve localization, thus being able to alleviate the burden of MDs while simultaneously giving a precise estimation of the locations. Afterwards, we give a deeper insight into the localization problem in more practical scenarios with varying number of MDs and investigate two different adaptive algorithms to meet the satisfactory localization error requirement. Compared to the conventional MBCS algorithms, simulation results validate that both adaptive algorithms could provide superior localization accuracy and exhibit stronger resilience to the changes in the number of MDs.
Low-Complexity LDS-CDMA Detection Based on Dynamic Factor Graph

Low density spreading code division multiple access (LDS-CDMA) is a newly emerging multiple access technique with its potential to support massive connectivity. However, the existing LDS-CDMA has the drawback of high multiuser detection complexity and does not take full advantage of the channel state information. To tackle these problems, a novel low-complexity multiuser detection scheme based on dynamic factor graph is proposed with the aid of a preset threshold ${{V}_\mathrm {th}}$. For the proposed scheme, a good tradeoff between computational complexity and bit error rate (BER) performance is determined by ${{V}_\mathrm {th}}$. Moveover, a ${{V}_\mathrm {th}}$ setting method is given, according to the average effective function node degree ${E\left( {{d}_{k,j}} \right)}$. Simulation results show that the proposed scheme has a substantially reduced computational complexity with negligible BER performance losses, compared to the original MPA-based scheme.
SEISMIC RISK OF LARGE DAMS IN BULGARIA

Most of the large dams in Bulgaria are situated in regions with high seismicity. The regulations that have been in force during the design and construction of the structures differ considerably from the modern codes. As a consequence a program for seismic assessment of the existing dams in Bulgaria has been prepared. It is essentially based on seismic risk assessment methodology. Because of the difference between the initial seismic design loads and the present code requirements a realistic analysis should be performed to take into consideration the capacity of the as-built structure. The methodology used for seismic risk assessment and the results reached are presented and discussed in the paper. The probabilistic seismic risk assessments for two concrete gravity dams and for one arch dam carried out recently are discussed. The obtained results are based on realistic assessment of the seismic hazard for the region, statistically and in-situ defined material properties and statistical definition of loads. For the aim of statistical characterization of the structure response under seismic excitation the Latin Hypercube Experimental Design (LHCED) procedure is used. The hydrostatic and hydrodynamic loads as well as the transient thermal loads are statistically represented. A list of failure scenarios for the dam is analyzed. As a result, the scenario with the highest probability of failure is determined. The acceptability of the probability of failure is discussed. In case of unacceptable probability of failure, measures for risk reduction are proposed.
A Mobile Offloading Game Against Smart Attacks

Mobile devices, such as smartphones, can offload applications and data to the cloud via access points or base stations to reduce energy consumption and improve user experience. However, mobile offloading is vulnerable to smart attackers that use smart and programmable radio devices, such as universal software radio peripherals, to perform multiple types of attacks, such as spoofing and jamming, based on the radio environment and offloading transmissions. In this paper, a mobile offloading game is investigated that consists of three players: a mobile device that chooses its offloading rate, a smart attacker that determines its attack mode, and a security agent that decides whether or not to initiate full protection for the serving access point during the offloading. Nash and Stackelberg equilibria of the offloading game are derived and their existence conditions are discussed. A Q-learning-based mobile offloading strategy is proposed for mobile devices that are unaware of system parameters, such as the channel conditions, in dynamic radio environments. Simulation results show that the proposed offloading strategy can improve the utility of the mobile device and reduce the attack rate of smart attackers.
USING GRASP FOR RESOURCE AVAILABILITY COST PROBLEM WITH TIME DEPENDENT RESOURCE COST

In this paper, we consider the resource investment project scheduling problem of minimizing the total resource availability costs by a given project deadline subject to recruitment and release dates for resources with time dependent recruitment setup cost. The project contains activities interrelated by finish-start type precedence relations with a time lag of zero, which require a set of renewable resources. A mixed integer programming formulation is proposed for the problem. The problem formed in this way is an NP-hard one forcing us to use Greedy Randomized Adaptive Search Procedure (GRASP) algorithm to obtain a satisfying solution. The performance of the proposed algorithm is evaluated on 150 test problems by statistically comparing in term of the objective function and computational times. Comparative computational results reveal that the proposed algorithm is efficient and effective.
Wolbachia enhances expression of NlCYP4CE1 in Nilaparvata lugens in response to imidacloprid stress

The brown planthopper, Nilaparvata lugens, is one of the main insect pests of rice. The N. lugens gene NlCYP4CE1 encodes cytochrome P450 monooxygenase (P450), which is a key enzyme in the metabolism of the insecticide imidacloprid. Previous research has suggested that the expression of NlCYP4CE1 is induced by imidacloprid stress, but the effect of bacterial symbionts on its expression has not been determined. The results of this study show that exposure to subtoxic imidacloprid changed the structure of the bacterial symbiont community in N. lugens. Specifically, the total bacterial content increased, but the bacterial species diversity significantly decreased. Wolbachia accounted for the largest proportion of bacteria in N. lugens; its abundance significantly increased after subtoxic imidacloprid exposure. The transcript level of NlCYP4CE1 was significantly increased by imidacloprid, but this effect was significantly weakened after Wolbachia was cleared with tetracycline. This result suggests that Wolbachia enhances the expression of NlCYP4CE1 to promote the detoxification metabolic response to imidacloprid stress. Understanding the effect of bacterial symbionts on gene expression in the host provides a new perspective on interactions between insecticides and their target insect pests, and highlights that subtoxic imidacloprid exposure may raise the risk of insecticide resistance by altering the structure of bacterial symbiont communities.