Excavation and support design of the Dicle–Kralkizi water tunnel: an overview

Tunneling projects have their uniqueness in terms of engineering problems. The expertise gained from analyzing these projects establishes a sound basis for future application. This paper conveys experiences gained during the construction and support of the design of the Dicle–Kralkizi water tunnel, Turkey. Tunnel stability problems including overbreaks and surface subsidence are evaluated. An analysis of the breakdowns, factors controlling advance rate and the overall performance of tunnel are covered. The accumulated information presented here is believed to be useful and reliable for a successful tunnel excavation in similar formations.     

Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs

Oxidation of raw water with chlorine results in formation of trihalomethanes (THM) and haloacetic acids (HAA). Factors affecting their concentrations have been found to be organic matter type and concentration, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with water reservoirs from Terkos, Buyukcekmece and Omerli lakes, Istanbul, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, contact time, chlorine dose, and specific ultraviolet absorbance (SUVA). The determination of disinfection by-products (DBP) was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total THM and total HAA based on the use of pH, contact time, chlorine dose, and SUVA. The developed models provided satisfactory estimations of the concentrations of the DBP and the model regression coefficients of THM and HAA are 0.88 and 0.61, respectively. Further, the Durbin-Watson values confirm the reliability of the two models. The results indicate that under these experimental conditions which indicate the variations of pH, chlorine dosages, contact time, and SUVA values, the formation of THM and HAA in water can be described by the multiple linear regression technique.     

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.     

Stabilization and Encapsulation of Gold Nanostars Mediated by Dithiols

Surface chemistry plays a pivotal role in regulating the morphology of nanoparticles, maintaining colloidal stability, and mediating the interaction with target analytes toward practical applications such as surface‐enhanced Raman scattering (SERS)‐based sensing and imaging. The use of a binary ligand mixture composed of 1,4‐benzenedithiol (BDT) and hexadecyltrimethylammonium chloride (CTAC) to provide gold nanostars with long‐term stability is reported. This is despite BDT being a bifunctional ligand, which usually leads to bridging and loss of colloidal stability. It is found however that neither BDT nor CTAC alone are able to provide sufficient colloidal and chemical stability. BDT‐coated Au nanostars are additionally used as seeds to direct the encapsulation with a gold outer shell, leading to the formation of unusual nanostructures including semishell‐coated gold nanostars, which are characterized by high‐resolution electron microscopy and electron tomography. Finally, BDT is exploited as a probe to reveal the enhanced local electric fields in the different nanostructures, showing that the semishell configuration provides significantly high SERS signals as compared to other core–shell configurations obtained during seeded growth, including full shells.     

Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network

Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R² and lower RMSE and MEP.     

A new approach for rebalancing of U-lines with stochastic task times using ant colony optimisation algorithm

In real world, line balancing involves existing lines in existing factories and the line typically needs to be rebalanced rather than balanced. Rebalancing of a U-line can be defined as a changeover process from its initial configuration to a new configuration for a while due to the reasons such as demand variations, changes in product design and changes in task times, etc. This study defines U-line rebalancing problem with stochastic task times and proposes a solution procedure based on ant colony optimisation. The objective of the proposed algorithm is to minimise total cost of rebalancing which is the sum of task transposition costs, workstation opening/closing costs and operating costs of workstations for a particular planning horizon. A comprehensive experiment is conducted to generate problem instances and to compare rebalancing costs of U-lines by means of several factors. A total of 6600 rebalancing solutions are obtained and several comparisons are performed.     

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.     

Structural properties and kinetics of nitro-niobized steels

In the present study, structural characterization and kinetics of nitro-niobized AISI 1010, AISI D2, and AISI M2 steels by thermo-reactive deposition technique in the powder mixture consisting of ferro-niobium, ammonium chloride, and alumina at the temperatures of 1173, 1273, and 1373 K for 60–240 min were investigated. The thickness of the niobium nitride layers formed on the nitro-niobized AISI 1010, AISI D2, and AISI M2 steels are ranged from 2.80 ± 0.90 to 11.89 ± 1.10 μm, 3.16 ± 0.60 to 13.16 ± 1.51 μm, and 3.85 ± 0.91 to 16.77 ± 2.10 μm, respectively. The phases formed in the coating layer deposited on the surface of the steel substrates are NbN_0.95 and Nb₂CN. The hardness of the niobium nitride coating layers produced on AISI 1010, AISI D2, and AISI M2 steels are changing from 1151 ± 126 to 1446 ± 121 HV_0.005, 1359 ± 413 to 1594 ± 761 HV_0.005, and 1321 ± 51 to 1915 ± 134 HV_0.005, respectively. Diffusion constants of the coating layers were changing between 1.517 × 10⁻¹⁵ and 2.043 × 10⁻¹⁴ m²/s, depending on steel compositions, treatment time and temperatures, and activation energies of the AISI 1010, AISI D2, and AISI M2 steels for the process were calculated as 128.7, 123.8, and 132.5 kJ/mol, respectively. Moreover, an attempt was made to investigate the possibility of predicting the contour diagram of niobium nitride coating thickness variation, depending on process time and temperature.     

Nanogold plasmon resonance-based glucose sensing. 2. Wavelength-ratiometric resonance light scattering

Gold colloids are well known to display strong plasmon absorption bands due to electron oscillations induced by the incident light. When the colloids are in proximity, the plasmon absorption bands are often perturbed. This has enabled us recently to successfully develop a glucose sensing platform based on the disassociation of dextran-coated gold colloids, cross-linked with Con A, by glucose. However, a much more useful and simpler property of gold colloids, which has been ill explored with regard to sensing, is their ability to efficiently scatter excitation light. We have found that our nanogold sensing aggregates are indeed efficient light scatters around the nanogold plasmon absorption band. By measuring the ratio of scattered light intensities at two different arbitrary wavelengths, 560 and 680 nm, glucose concentrations can be readily determined from a few millimolar up to approximately 60 mM, using a simple white light LED and detection system. Further, by measuring the ratio of the scattered intensities, this sensing approach is independent of the total sensing aggregate concentration and the excitation and detection instrumentation fluctuations or drifts. This simplistic and low-cost approach to glucose sensing, coupled with the sensing aggreagates' ability to scatter red light, suggests the potential use of these aggregates for use in physiological transdermal glucose monitoring, either for implantable skin sensors or glucose sensing tattoos (discussed later).     

Auction-theoretic coordination of production planning in the supply chain

Most planning and optimization methods in manufacturing logistics assume centralized or hierarchical decision-making using monolithic models. Motivated by the increasing needs to coordinate diverse decision processes and systems, we investigate an auction-theoretic mechanism for production coordination in a supply chain. Our effort focuses on structural mappings between mathematical decomposition and iterative auction mechanisms wherein agents compete based on their local utilities, announced conflict pricing, and production targets. Building upon the rich literature in optimization and auction-theoretic analysis, we investigate the advantages and limitations of this distributed decision scheme on a large set problem in supply chain production planning. Experimental results show that the proposed auction mechanism provides impressive improvement over the traditional monolithic method without significant degradation to the solution quality.