Ensuring location diversity in privacy-preserving spatio-temporal data publishing

The rise of mobile technologies in the last decade has led to vast amounts of location information generated by individuals. From the knowledge discovery point of view, these data are quite valuable, but the inherent personal information in the data raises privacy concerns. There exists many algorithms in the literature to satisfy the privacy requirements of individuals, by generalizing, perturbing, and suppressing their data. Current techniques that try to ensure a level of indistinguishability between trajectories in a dataset are direct applications of \(k\) -anonymity, thus suffer from the shortcomings of \(k\) -anonymity such as the lack of diversity in sensitive regions. Moreover, these techniques fail to incorporate some common background knowledge, an adversary might have such as the underlying map, the traffic density, and the anonymization algorithm itself. We propose a new privacy metric \(p\) -confidentiality that ensures location diversity by bounding the probability of a user visiting a sensitive location with the \(p\) input parameter. We perform our probabilistic analysis based on the background knowledge of the adversary. Instead of grouping the trajectories, we anonymize the underlying map, that is, we group nodes (points of interest) to create obfuscation areas around sensitive locations. The groups are formed in such a way that the parts of trajectories entering the groups, coupled with the adversary background, do not increase the adversary’s belief in violating the \(p\) -confidentiality. We then use the map anonymization as a model to anonymize the trajectories. We prove that our algorithm is resistant to reverse-engineering attacks when the statistics required for map anonymization is publicly available. We empirically evaluate the performance of our algorithm and show that location diversity can be satisfied effectively.     

Optimization of specific energy consumption for Bomaplex Red CR-L dye removal from aqueous solution by electrocoagulation using Taguchi-neural method

In this investigation, firstly, Taguchi method was applied to determine the optimum specific energy consumption (SEC) for dye removal from aqueous solution by electrocoagulation using aluminum electrodes. An orthogonal array (OA₁₆) experimental design that allows to investigate the simultaneous variations of five parameters (Initial dye concentration, Initial pH of the solution, Supporting electrolyte concentration, Supporting electrolyte type and Current density) having four levels was employed to evaluate the effects of experimental parameters with two replicates. According to Taguchi-neural method, while the optimum conditions that dye removal efficiency equals to 62.71 % were found to be initial dye concentration 600 mg/L, initial pH of the solution 6, supporting electrolyte concentration 7.0 mM, supporting electrolyte type NaCl, and current density 0.10 mA/cm². Under these optimum conditions, energy consumption is 0.38 kW h/m³. Alternatively, it can be said that optimum conditions can be modified as follows supporting electrolyte concentration of 10.0 mM and supporting electrolyte type CaCl₂, for 600 mg/L, initial dye concentration initial pH of the solution 6, and current density 0.10 mA/cm². Under these optimum conditions, SEC and dye removal efficiency are 0.45 kW h/m³ and 69.18 %, respectively.     

Heuristic-based neural networks for stochastic dynamic lot sizing problem

Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.     

Design optimization of circular antenna arrays using Taguchi method

This paper presents the application of Taguchi method (TM) to design optimization of non-uniform circular antenna array (CAA) for suppression of sidelobe levels (SLLs). TM, a robust design approach, takes signal-to-noise ratio and orthogonal array tools from the statistical design of experiments. These tools allow instead of full factorial parametric analysis minimize the design parameters; thus, increase the convergence speed and generate more accurate solutions. TM is used to determine an optimal set of amplitudes and positions of CAA for 8, 10, and 12 elements. Comparison of the results of the TM with those of latest meta-heuristic algorithms in the literature reveals that the CAA design with TM provides the best SLL reduction performance in all cases.

     

Can Dentin Surfaces Be Bonded Safely With Ozone and Boric Acid?

The aim of this study is to evaluate the effect of different cavity disinfectants on shear bond strength. In this study, 60 caries-free human molar teeth were used. Smooth dentin surfaces were revealed by cutting occlusal enamel and a standard smear layer was obtained by using 600-grid sandpaper. Specimens were randomly assigned to four groups according to the disinfectant used: Group 1: Control (no disinfectant); Group 2: Ozone; Group 3: Chlorhexidine, Group 4: Boric acid. Specimens were bonded using S3 Plus Bond and the composite buildups were created by using composite resin according to the manufacturer’s instructions. After specimens were kept for 24 h, at 37 °C in distilled water, the shear bond strength test was measured with a universal test machine. The highest bond strength values were observed in the control group. The bond strength was significantly lower in the chlorhexidine, and boric acid-treated groups than it was in the control group (respectively, p = 0.046 and p = 0.028); however, no significant difference in the bond strength was observed between the ozone group and the control group (p = 0.444). The ozone group was detected as having the best shear bond strength values in groups which were applied to cavity disinfectants.     

Artificial neural network models for lot-sizing problem: a case study

Lot-sizing is one of the most difficult problems in production planning. The main purpose of this study is to propose a new lot-sizing based on artificial neural network (ANN), which may lead to a better performance than commonly used lot-sizing heuristics (SM, EOQ, PPB, LUC, and LTC). The data obtained are the results of years 2004 thru 2009 for 186 different types of stock items from the 2nd Air Supply and Maintenance Centre Command, a state-funded factory in Kayseri, Turkey. Factual data were applied under the coverage of the study, and the system from which the data have been obtained is still in live and active status. In the study, the purchasing costs, holding costs, and set-up costs were taken into consideration. These data were obtained from the administration data system of the enterprise. The solutions of this lot-sizing heuristics were found by WinQSB software accordingly. The ANN was constituted by using the NeuroSolutions software. The criterion of deviation from the optimum solution and the criterion of percentage of times obtaining the optimum order pattern were taken into account for the comparison purposes. The performance values of 400 ANNs were compared to lot-sizing methods. MS Excel and Visual Basic Macro were utilized for all calculations applied after this stage. The results showed that the proposed ANN-based method outperformed all lot-sizing methods taken into account in this study.     

Changes in vibrational entropy during the early stages of chemical unmixing in fcc Cu–6% Fe

A nanocrystalline face-centered cubic (fcc) solid solution of 6% Fe in Cu was prepared by high-energy ball milling, and annealed at temperatures from 200 to 360 °C to induce chemical unmixing. The chemical state of the material was characterized by three-dimensional atom probe microscopy, Mössbauer spectrometry and X-ray powder diffractometry. The unmixing was heterogeneous, with iron atoms forming iron-rich zones that thicken with further annealing. The phonon partial density of states (pDOS) of ⁵⁷Fe was measured by nuclear resonant inelastic X-ray scattering, showing the pDOS of the as-prepared material to be that of an fcc crystal. The features of this pDOS became broader in the early stages of unmixing, but only small changes in average phonon frequencies occurred until the body-centered cubic (bcc) phase began to form. The vibrational entropy calculated from the pDOS underwent little change during the early stage of annealing, but decreased rapidly when the bcc phase formed in the material.     

In vitro bioaccessibility of coenzyme Q10 in enriched yoghurts

In this study, the aim was to determine the bioaccessibilities of coenzyme Q10 (CoQ10) in yoghurt samples produced using enriched skim milk with different coenzyme Q10 preparations. First, emulsified coenzyme Q10, γ‐cyclodextrin/coenzyme Q10 complex and nanoparticle coenzyme Q10 were prepared with the reference coenzyme Q10 standard. These coenzyme Q10 preparations were added into the milk to produce coenzyme Q10 enriched yoghurt samples. Nanoparticle coenzyme Q10 was obtained as in spherical shape with 176.00 ± 50.62 nm diameter. Coenzyme Q10 bioaccessibility was found as 50.59 ± 1.88% in control yoghurt. The yoghurt enriched nanoparticle coenzyme Q10 had the highest coenzyme Q10 bioaccessibility (73.81 ± 1.61%) among the produced yoghurts (P < 0.01). Coenzyme Q10 bioaccessibilities were also found as 63.75 ± 0.91% and 46.83 ± 1.27% in yoghurts enriched with emulsified coenzyme Q10, and γ‐cyclodextrin/coenzyme Q10 complex, respectively.     

Uncertainties in the prediction of flow in a long reach of the Sacramento River

The accurate prediction of the flow field in a long reach of a complex waterway is prerequisite to the evaluation to acceptable engineering accuracy of strategies for the restoration, resource management and hazard assessment. In this study, we consider some aspects of the numerical prediction of flow in a long reach of the Sacramento River in California with view to gaining a better understanding the sources and the extent of the uncertainties that are present in the predictions. The predictions were obtained using a finite‐volume method to solve the time‐averaged forms of the equations governing the conservation of mass and momentum. Body‐fitted coordinates were used to define the complex river bathymetry. Both depth‐averaged and fully three‐dimensional (3D) computations were performed. The results were compared with data obtained from a large‐scale physical model of the same reach. The outcome of the comparisons served to provide the basis for the assessment of the sensitivity of the solutions to assumptions made regarding the conditions at inlet to the solution domain, the choice of turbulence model used to close the time‐averaged equations and, for the depth‐averaged calculations, the choice of method used for prescribing the bed friction. The influence of other parameters such as the density of the computational grid, the method used to define the channel bathymetry, and the unsteadiness of the mean flow that results from vortex shedding from a bridge pier and abutments were also assessed. The results obtained form a useful addition to the growing literature on the computational modelling of flows in natural waterways.