Neural networks based concrete airfield pavement layer moduli backcalculation

The Heavy Weight Deflectometer (HWD) is a Non-Destructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems. This paper presents an Artificial Neural Networks (ANN) based approach for non-destructively estimating the stiffness properties of rigid airfield pavements subjected to full-scale dynamic traffic testing using simulated new generation aircraft gears. HWD tests were routinely conducted on three Portland Cement Concrete (PCC) test items at the Federal Aviation Administration\u27s (FAA) National Airport Pavement Test Facility (NAPTF) to verify the uniformity of the test pavement structures and to measure pavement responses during full-scale traffic testing. Substantial corner cracking occurred in all three of the rigid pavement test items after 28 passes of traffic had been completed. Trafficking continued until the rigid items were deemed failed. The study findings illustrate the potential of ANN-based models for routine and real-time structural evaluation of rigid pavement NDT data

Can Migration Fear and Policy Uncertainty Be the Source of Macroeconomic Fluctuations?

There is no doubt that migration is a complex phenomenon with varied economic, political, and cultural impacts on both the countries of origin as well as the countries of destination. In particular, migration fears and policy uncertainties may have positive or negative consequences on economic activities such as the labor market, price stability, and economic activity in economies either directly or indirectly. For this reason, this study analyzes the causality effect of migration fear and policy uncertainty indices on macroeconomic variables in the most immigrant-receiving countries, the US, the UK, France, and Germany, using both panel and time-varying causality tests. In the study, no causality relationship was found from migration fear and policy uncertainty indices to macroeconomic variables in panel data. However, country-specific time-varying causality relationships were detected in the time series dimension. According to the findings, it can be stated that policymakers and researchers should consider migration fear and policy uncertainty when determining policies to ensure macroeconomic stability in these countries

Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement surface deflections with very low average errors comparable with those obtained directly from the finite element analyses

Nondestructive Evaluation of Iowa Pavements-Phase I

Evaluating structural conditions of existing, in-service pavements is a part of the routine maintenance and rehabilitation activities undertaken by the most departments of transportation (DOTs). In the field, the pavement deflection profiles (or basins) gathered from the nondestructive falling weight deflectometer (FWD) test data are typically used to evaluate pavement structural conditions. Over the past decade, interest has increased in a new class of computational intelligence system, known as artificial neural networks (ANNs), for use in geomechanical and pavement systems applications. This report describes the development and use of ANN models as pavement structural analysis tools for the rapid and accurate prediction of layer parameters of Iowa pavements subjected to typical highway loadings. ANN models trained with the results from the structural analysis program solutions have been found to be practical alternatives. The ILLI-PAVE, ISLAB2000, and DIPLOMAT programs were used as the structural response models for solving the deflection parameters of flexible, rigid, and composite pavements, respectively. The trained ANN models in this study were capable of predicting pavement layer moduli and critical pavement responses from FWD deflection basins with low errors. The developed methodology was successfully verified using results from long-term pavement performance (LTPP) FWD tests, as well as Iowa DOT FWD field data. All successfully developed ANN models were incorporated into a Microsoft Excel spreadsheet-based backcalculation software toolbox with a user-friendly interface. The final outcome of this study was a field-validated, nondestructive pavement evaluation toolbox that will be used to assess pavement condition, estimate remaining pavement life, and eventually help assess pavement rehabilitation strategies by the Iowa DOT pavement management team

Estimation of Compressive Strength of Waste Andesite Powder-Added Concrete Using an Artifical Neural Network

In this study, the effects of using andesite powder wastes-produced from natural stone factories as mineral additives in concrete manufacturing-on the compressive strength of concrete were modeled using an Artificial Neural Network (ANN). To achieve this, cement mixtures were produced by using waste andesite powder (WAP) mixture at ratios of 0% (control), 10%, 15% and 20%. The effects of curing time were investigated by preparing specimens at 28 and 90 days. The training set was formed by using cement and the specified WAP mixtures and curing time parameters. It was observed that the results obtained from the training ANNs were consistent with the experimental data

Diş hekimleri ve diş hekimliği öğrencilerinin ağrılı üst ekstremite kas iskelet sistemi problemlerinde germe ve gevşeme egzersizlerinin etkisinin araştırılması

Bu çalışma, diş hekimlerinde ve diş hekimliği öğrencilerinde üst ekstremite germe ve gevşeme egzersizlerinin etkisini araştırmak için yapıldı. Çalışmaya 54 diş hekimliği öğrencisi, 47 diş hekimi dahil edildi. Diş hekimleri 1-4 yıllık mesleki tecrübeye sahip diş hekimleri ve 4 yıldan fazla mesleki tecrübeye sahip diş hekimleri olarak; diş hekimliği öğrencileri ise 1. Sınıf lisans öğrencileri ve 4. Sınıf lisans öğrencileri olarak gruplara ayrıldı. Katılımcılara basılı materyal ile ev egzersiz programı planlanlandı. Katılımcıların; demografik özellikleri, fiziksel aktivite alışkanlıkları, ağrı düzeyleri ve kas iskelet problemlerinin dereceleri 6 haftalık egzersiz programı öncesi ve sonrası değerlendirilerek kaydedildi. Ağrının lokalizasyonu, özelliği, zamanla ağrının ilişkisi ve ağrının şiddetini ölçmek amacıyla Mc Gıll - Melzack (MPQ) ağrı anketi, kas iskelet sistemi değerlendirmesi için Nordic Kas İskelet Sistemi Anketi, Fiziksel aktivite seviyesinin belirlenmesi için ise Uluslararası Fiziksel Aktivite Anketi kullanıldı. Katılımcılara 6 hafta boyunca 1 set 10 tekrar şeklinde üst ekstremite germe ve gevşeme egzersizleri yaptırıldı. Çalışmanın sonucunda ağrı yönünden tedavi öncesi ve tedavi sonrası karşılaştırıldığında ağrının azaldığı, ağrının iş yapmaya engel oluşturma durumunun azaldığı, fiziksel aktivitenin arttığı bulundu (p<0.5). Gruplar karşılaştırıldığında ise 4. Sınıf lisans öğrencilerinin diğer gruplara göre ağrı düzeyinin daha düşük oranda düştüğü gözlendi (p>0.5). Sonuç olarak, diş hekimlerinde üst ekstremite germe ve gevşeme egzersizlerinin, ağrı ve kas iskelet sistemi problemleri üzerinde azaltıcı etkisi, fiziksel aktivite düzeyinde artıcı etkisi olduğu görülmüştür

A strategy based on the dispersive liquid-liquid microextraction of cadmium in environmental samples prior to ıts determination by flame atomic absorption spectrometry

This work has been supported by the Scientific Research Projects of Karadeniz Technical University (Project no: 1223), Turkey.A simple method was developed by combining dispersive liquid-liquid microexraction (DLLME) and flame atomic absorption spectrometry (FAAS). For the pre-concentration of trace amounts of cadmium, a new complexation chelate of 2-[(4-phenylpiperazine-5-Thioxo- 4,5-dihydro-1 ,3,4-oxadiazole-2- yl)methyl]-5-methyl-4-[2-(1H-indol e-3-yl)ethyl]-2,4-dihydro-3H-1, 2,4-Triazole-3-one (PPTOMDT) was used and mixed with the solvents of chloroform and methanol. The mixture of the extraction solutions was then directly injected into an aqueous solution containing Cd2+ ions. After centrifugation, the settled phase was diluted with 500 mu L of ethanol/nitric acid and aspirated into the FAAS. The limit of detection (LOD) was found at 0.69 mu g L-1 under optimum conditions. The relative standard deviation (RSD) for 15 replicates at a 3.75 mu g L-1 Cd2+ concentration level was 3.21%. The calibration plot was linear within the range of 2.5-15 mu g L-1 of Cd2+. After the analytical characteristics were determined, the CRM-TMDW-500 Drinking Water and CRM-SA-C Sandy Soil C, both certified reference materials, were analyzed in order to validate the method. The application of the DLLME method has been successfully tested for the determination of cadmium in solid and liquid samples. The recoveries of the spiked sample ranged between 92-96%

Preparation of diethyl malonate adducts from chalcone analogs containing a thienyl ring

Nine chalcone-diethyl malonate derivatives (4a-i) were prepared by the reaction of chalcone derivatives (3a-i) with diethyl malonate in the presence of a catalytic amount of KOt-Bu in CH2CI2 in good to excellent yields. The products were characterized by FTIR, 1H-NMR, 13C-NMR and elemental analyses. KEY WORDS: Michael addition, Chalcone, KOt-Bu, Diethyl malonate  Bull. Chem. Soc. Ethiop. 2010, 24(1), 85-91. 

The Yo-Yo IR2 test in professional basketball players

The aim of this study was to investigate the relationship between direct method and indirect method (Yo-Yo Intermittent Recovery Test Level II) that are used to determine aerobic capacity and endurance. Fourteen basketball players (22.49 ±04.82 year-old with the height 192.36±5.90 cm, weight 89.21±7.6 kg, Body fat percentage 11.07 ± 1.82 %) voluntarily participated in this study. Yo-Yo Intermittent Recovery Test Level II protocol (YIRT 2) was implemented to participants. Distance covered by the athletes, estimated VO2max, direct VO2max, Anaerobic threshold VO2 and Anaerobic threshold heart rate (HRAT) values were recorded. Direct VO2max and other respiratory parameters were measured with telemetric gas analyzer. After 5 minute rest in supine position heart rates (HRrest), rest lactate levels (LArest) and maximal heart rate (HRmax) during the test and post test lactate levels (LApost) were measured. Pearson Correlation Test was used to determine the relationship between the variables. P value was set at 0,05. According to the findings, positively significant large correlation was found between estimated VO2max and direct VO2max(r = 0.504, p &lt; 0.05), HRmax (r = 0.501, p &lt; 0.05), positively significant large correlation was found between distance and Direct VO2max(r = 0.521, p &lt; 0.05), HRmax (r = 0.516, p &lt; 0.05). There is no significant relationship except above-mentioned variables (p&lt;0.05). YIRT2 can guess VO2max of professional male basketball players and can be used to evaluate endurance

Applications of Graphene Modified by Self-Assembled Monolayers

Self-assembled monolayers (SAMs) are well-oriented molecular structures that are formed by the adsorption of an active site of a surfactant onto a substrate’s surface. Aromatic SAMs were used to modify anode/hole transport layer interface in order to achieve preferable barrier alignment and charge carrier injection from anode to an organic-based thin film material. Other functions of SAMs include current blocking layers or moisture penetration blocking layers, dipolar surface layers for enhanced charge injection, and modification of work function of a material such as graphene acting as a spacer to physically separate and electrically decouple it from the substrate. Additionally, SAM modification of graphene leads to its electronic passivation at layers’ edges, elimination of defects, and enhanced adhesion and stability. The surface modification with molecules capable of forming SAM is a fast, simple, low-cost, and effective technique for the development of novel materials especially for the production of electronic devices. The ability to modify its properties by SAM technique has opened up a wide range of applications in electronic and optoelectronic devices