Itinerant Delivery of Popular Data via WIDE Hot Spots

Wireless Information Delivery Environment (WIDE) is a distributed data dissemination system, which uses IEEE 802.11b technology. WIDE aims to deliver popular information services to registered mobile clients in WLAN hot spots. Data delivery is based on broadcasting and multicasting to provide scalability and efficient use of the wireless channel. Reliability is assured with a combination of Forward Error Correction (FEC), data carousel, and ARQ techniques. This paper presents the proposed system architecture with the details of reliable and secure data dissemination mechanisms. Functional evaluation of the proposed system and mechanisms on the implemented prototype are also included in this paper. This work is partially supported by the State Planning Organization of Turkey under the grant number 98K120890, and by the Bogazici University Research Projects under the grant number 02A105D. A shorter version of this paper was presented in WONS 2004 in Madonna di Campiglio, Italy. Sinan Isik received the B.S. degree in mathematics, and the M.S. degree in computer engineering from Bogazici University, Istanbul, Turkey in 1999 and 2003, respectively. He is currently working toward for the PhD degree in computer engineering in the same university. His research interests include wireless communication, wireless ad-hoc networks and wireless sensor networks. Mehmet Yunus Donmez received his B.S. degree from Mathematics Department in 1999 and his MS degree from the Computer Engineering Department of Bogazici University, Istanbul, Turkey in 2003. He is currently studying for his PhD degree in computer engineering. His research interests are wireless networks and content delivery systems, along with QoS, multicasting and fairness issues in MANETs. Cem Ersoy received his BS and MS degrees in electrical engineering from Bogazici University, Istanbul, in 1984 and 1986, respectively. He worked as an R&D engineer in NETAS A.S. between 1984 and 1986. He received his PhD in electrical engineering from Polytechnic University, Brooklyn, New York in 1992. Currently, he is a professor and department head in the Computer Engineering Department of Bogazici University. His research interests include performance evaluation and topological design of communication networks, wireless communications and mobile applications. Dr. Ersoy is a Senior Member of IEEE.     

Effects of the γ-irradiation strength and basalt additive content on the mechanical performance and dielectric response of polypropylene films

In this study, we investigated the effects of high-dose γ-ray irradiation on the mechanical and dielectric properties of polypropylene (PP)–basalt thick films. PP–basalt thick-film composites with various basalt contents from 0.5 to 10.0% were prepared by a hot-press method. The samples were exposed to γ radiation at different doses in the range 3–25 kGy. The mechanical properties of the samples, such as the Young's modulus, tensile strength, percentage strain at break, and energy at break, were examined in the context of the γ-irradiation process. Although the maximum elasticity was obtained for the unirradiated 0.5% basalt-added composite, the 6 kGy γ-irradiated PP–1.0% basalt sample exhibited the highest elasticity properties among all of the composites. The best mechanical properties, including the ultimate tensile strength and energy at break values, were achieved for the 12 kGy γ-irradiated neat PP. The dielectric properties of the PP–basalt composites were also investigated in the 100 Hz to 15 MHz frequency region at room temperature. According to the analysis of the dielectric properties, the 3 kGy γ-irradiated neat PP may have potential for microelectronic device applications that require low dielectric constant and dielectric loss materials. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47414.     

Effect of dispersed phase feed time on the droplet size of Pickering emulsions produced in a stirred tank

The aim of this study was to investigate the effect of feed time of the oil phase on the average droplet size of Pickering emulsions produced in stirred tanks. Three types of impellers were tested: RT, up-pumping PBT (PBTU), and down-pumping PBT (PBTD). All the impellers were tested at two sizes, T/3 and T/2. All configurations were compared at constant tip speed, power per mass, and impeller Reynolds number. The droplet diameters were measured in Mastersizer® 3,000 (Malvern). The results showed that an increase in feed time causes a reduction in the average droplet size. At lower impeller speeds and higher feed times, the effect is more pronounced. It was found that some other geometric parameters also have an impact on the average droplet size.     

Information Model for Machine-Tool-Performance Tests

This report specifies an information model of machine-tool-performance tests in the EXPRESS [1] language. The information model provides a mechanism for describing the properties and results of machine-tool-performance tests. The objective of the information model is a standardized, computer-interpretable representation that allows for efficient archiving and exchange of performance test data throughout the life cycle of the machine. The report also demonstrates the implementation of the information model using three different implementation methods.     

Uptake of trivalent chromium ions from aqueous solutions using kaolinite

The sorption of Cr(III) from aqueous solutions on kaolinite has been studied by a batch technique. We have investigated how solution pH, ionic strength and temperature affect this process. The adsorbed amount of chromium ions on kaolinite has increased with increasing pH and temperature when it has decreased with increasing ionic strength. The sorption of Cr(III) on kaolinite is endothermic process in nature. Sorption data have been interpreted in terms of Freundlich and Langmuir equations. The adsorption isotherm was measured experimentally at different conditions, and the experimental data were correlated reasonably well by the adsorption isotherm of the Langmuir, and the isotherm parameters (q(m) and K) have been calculated as well. The enthalpy change for chromium adsorption has been estimated as 7.0 kJ mol(-1). The order of enthalpy of adsorption corresponds to a physical reaction.     

The impact of distraction mitigation strategies on driving performance

OBJECTIVES: An experiment was conducted to assess the effects of distraction mitigation strategies on drivers' performance and productivity while engaged in an in-vehicle information system task. BACKGROUND: Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. METHOD: An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). RESULTS: Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F (6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t (155) = 8.37, p < .05, and locking strategies, t (155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F (6, 138) = 2.43, p < .05. CONCLUSIONS: Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. APPLICATION: These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.     

Processing and electrical properties of (1 − x)[(1 − y)(Pb(Mg1/3Nb2/3)O3)–y(Pb(Yb1/2Nb1/2)O3)]–xPbTiO3 ceramics

Ferroelectric ceramics in the vicinity of morphotropic phase boundary (MPB) with compositions represented as (1 ? x)[(1 ? y)(Pb(Mg_1/3Nb_2/3)O₃)–y(Pb(Yb_1/2Nb_1/2)O₃)]–xPbTiO₃ were prepared by solid state reaction. The addition of PYbN to PMN–PT decreased the sintering temperature from 1200 °C (y = 0.25) to 1000 °C (y = 0.75). The PT content, where the MPB was observed, increased with the PYbN addition. A remanent polarization value of 28.5 µC/cm² and a coercive field value of 11 kV/cm were measured from 0.62[0.25PMN–0.75PYbN]–0.38PT ceramics, which were close to the ones measured from PMN–0.32PT ceramics. In addition, the Curie temperature was found to increase with PYbN additions.     

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.

     

Supporting intelligent and trustworthy maritime path planning decisions

The risk of maritime collisions and groundings has dramatically increased in the past five years despite technological advancements such as GPS-based navigation tools and electronic charts, which may add to, instead of reduce, workload. We propose that an automated path planning tool for littoral navigation can reduce workload and improve the overall system efficiency, particularly under time pressure. To this end, a maritime automated path planner (MAPP) was developed, incorporating information requirements developed from a cognitive task analysis, with special emphasis on designing for trust. Human-in-the-loop experimental results showed that MAPP was successful in reducing the time required to generate an optimized path, as well as reducing path lengths. The results also showed that while users gave the tool high acceptance ratings, they rated the MAPP as average for trust, which we propose is the appropriate level of trust for such a system.