A new correlation for calculation of the chemical exergy of bio-oils obtained from agricultural residues by using elementary analyses data

The correlation proposed in this work has been compared statistically with a larger database of bio-oils taken from values in the literature. The correlation developed here uses only one formula used in the major constituents of elementary analyses. Seventy-nine data points have been used to derive this correlation. This correlation is valid for bio-oils having a broad range of elemental composition, that is C: 8.2–89.3%; H: 4.6–14.4%; O: 1.4–76.9%; N: 0.0–10.8%; and S: 0.0–1.8%. It presents an average absolute error of 1.21% and bias error of 0.59%, and thereby establishes its versatility.     

Limestone dust and wood sawdust as brick material

The majority of abandoned limestone powder wastes (LPW) and wood sawdust wastes (WSW) is accumulated from the countries all over the world and causes certain serious environmental problems and health hazards. This paper presents a parametric experimental study which investigates the potential use of WSW–LPW combination for producing a low-cost and lightweight composite as a building material. Some of the physical and mechanical properties of concrete mixes having high level of WSW and LPW are investigated. The obtained compressive strength, flexural strength, unit weight, ultrasonic pulse velocity (UPV) and water absorption values satisfy the relevant international standards. The results show that the effect of high-level replacement of WSW with LPW does not exhibit a sudden brittle fracture even beyond the failure loads, indicates high energy absorption capacity, reduces the unit weight dramatically and introduces smother surface compared to the current concrete bricks in the market. It shows a potential to be used for walls, wooden board substitute, economically alternative to the concrete blocks, ceiling panels, sound barrier panels, etc.     

Housing price dynamics and bubble risk: the case of Turkey

Abstract

Housing prices have increased substantially in some emerging markets in recent years. Turkish housing market has also experienced a boom over the last decade with rapid house price appreciations. This study is the first to employ two different house price indexes to analyze housing bubble in Turkey in two different time periods, 2010:M1–2014:M12 and 2007:M6–2014:M12. We first capture the determinants of housing price by employing Bounds test and then examine whether rising house prices have been justified by fundamentals by employing OLS/FMOLS/DOLS, Kalman filter and ARIMA models. The Bounds test results suggest that there is a long-term cointegration among house price indexes and housing rent, construction cost and real mortgage interest rate. The results imply that the Turkish housing market has experienced some cases of overvaluation, but not bubble formation. This evidence has several implications for house price dynamics and risks in the Turkish housing market. Based on Turkish experience, the study also draws policy implications for emerging housing markets.     

Delineating compositionally different dykes in the Ulukışla basin (Central Anatolia,Turkey) using computer‐enhanced multi‐spectral remote sensing data

In the Uluk??la basin (Central Anatolia, Turkey) several geological mapping campaigns were carried out using conventional field methods to delineate compositionally different Middle–Upper Eocene dykes. However, complete and correct mapping of these dykes was hampered by rugged terrain, lack of road access, wide spatial dyke distributions with small exposures and diverse weathering of these dykes. For these reasons, Landsat‐5 Thematic Mapper (TM) satellite image of the study area was used to facilitate delineation of the exact boundaries of gabbroic, dioritic and trachytic dykes found in the area. Remotely sensed data were analysed using several image enhancement procedures, including colour composites, band ratios, principal components analysis (PCA), and Crosta technique. Results obtained from all the processes were examined, and it was found that dyke boundaries are best visible in the PCA123 image; RGB 731 colour composite; TM band ratio 5/7, 5/1, 4 combination; and 1457‐PC4 image obtained by Crosta technique. The alteration differences of three dyke groups are enhanced much better in the 1457‐PC4 image obtained by Crosta technique, which highlights the hydroxyl‐bearing minerals as white‐coloured pixels. Using computer‐enhanced multi‐spectral remote sensing data, we were able to map the boundaries and spatial distributions of compositionally different dykes, which otherwise is an overwhelmingly difficult task to achieve using conventional field methods. In similar settings, remote sensing techniques applied in this study may provide an efficient and low‐cost alternative to time‐consuming and physically demanding field‐mapping campaigns.     

A multi-dimensional shooting algorithm for the two-facility location–allocation problem with dense demand

We develop an efficient allocation-based solution framework for a class of two-facility location–allocation problems with dense demand data. By formulating the problem as a multi-dimensional boundary value problem, we show that previous results for the discrete demand case can be extended to problems with highly dense demand data. Further, this approach can be generalized to non-convex allocation decisions. This formulation is illustrated for the Euclidean metric case by representing the affine bisector with two points. A specialized multi-dimensional shooting algorithm is presented and illustrated on an example. Comparisons with two alternative methods through a computational study confirm the efficiency of the proposed methodology.     

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

Verification has grown to dominate the cost of electronic system design, consuming about 60% of design effort. Among several verification techniques, logic simulation remains the major verification technique. Speeding up logic simulation results in great savings and shorter time-to-market. We parallelize logic simulation using Graphics Processing Units (GPUs). In the past, GPUs were special-purpose application accelerators, suitable only for conventional graphics applications. The new generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. We develop a parallel cycle-based logic simulation algorithm that uses And Inverter Graphs (AIGs) as design representations. AIGs have proven to be an effective representation for various design automation applications, and we obtain similar benefits for speeding up logic simulation. We develop two clustering algorithms that partition the gates in the designs into independent blocks. Our algorithms exploit the massively parallel GPU architecture featuring thousands of concurrent threads, fast memory, and memory coalescing for optimizations. We demonstrate up-to 5x and 21x speedups on several benchmarks using our simulation system with the first and second clustering algorithms, respectively. Our work ultimately results in significant reduction in the overall design cycle.     

Semantic web-based social network access control

The existence of online social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the first step toward addressing the existing security and privacy concerns related to online social networks. To address some of the current limitations, we have created an experimental social network using synthetic data which we then use to test the efficacy of the semantic reasoning based approaches we have previously suggested.     

A General BRDF Representation Based on Tensor Decomposition

Generating photo‐realistic images through Monte Carlo rendering requires efficient representation of light–surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four‐dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well‐known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.     

Convex cone-based partial order for multiple criteria alternatives

In this paper, we consider the problem of finding a preference-based strict partial order for a finite set of multiple criteria alternatives. We develop an approach based on information provided by the decision maker in the form of pairwise comparisons. We assume that the decision maker's value function is not explicitly known, but it has a quasi-concave form. Based on this assumption, we construct convex cones providing additional preference information to partially order the set of alternatives. We also extend the information obtained from the quasi-concavity of the value function to derive heuristic information that enriches the strict partial order. This approach can as such be used to partially rank multiple criteria alternatives and as a supplementary method to incorporate preference information in, e.g. Data Envelopment Analysis and Evolutionary Multi-Objective Optimization.     

A new algorithm for initial cluster centers in k-means algorithm

Clustering is one of the widely used knowledge discovery techniques to reveal structures in a dataset that can be extremely useful to the analyst. In iterative clustering algorithms the procedure adopted for choosing initial cluster centers is extremely important as it has a direct impact on the formation of final clusters. Since clusters are separated groups in a feature space, it is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to compute initial cluster centers for k-means algorithm. The algorithm is applied to several different datasets in different dimension for illustrative purposes. It is observed that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm.