9838 and 0.9937, which indicated 98.38% and 99.37% of variability in the response could be explained by the model. Therefore, the present R2-values reflected a very good fit (>0.9) between the experimental and predicted values [17].In addition, the R2Adj (0.9353 and 0.9997) were satisfactory, which confirms the aptness of the model. Moreover, the adequate precision (12.57 and 170.96) shows remarkable signal (4). This ensured model (quadratic) was suitable to navigate the design space and provide a satisfactory match of the polynomial model to the experimental data.3.2. The Quadratic Expression ModelIt is normal to describe experimental data by forming a mathematical relationship between the factors (independent variables) and responses (dependent variables).

The final model to describe the relationship of the energy band gap and surface roughness with control factors is shown in Equations (2) and (3), respectively, as follows:Y1=0.44266?0.14248X
In recent years, wireless sensor networks (WSNs) have seen tremendous applications in different aspects of our lives such as habitat, structure health and remote health monitoring, precision agriculture, home automation, smart electric grids, and intelligent transportations systems. Typically, a large number of tiny computing devices (nodes) constitute a WSN where nodes are considered as constrained in resources, i.e., with limited on-board memory, short-range radio transceivers, and battery power. Depending on the application environment, nodes are interfaced with various sensors for monitoring some phenomenon of interest (temperature, humidity, pressure, etc.

) and forward sensory data to special devices (sinks) in a cooperative manner (typically multi-hop). The sink device (base-station) upon receiving the sensory data analyses the reported activity and may further route the data to a remote user/database via some regular infrastructure such as the Internet [1]. A typical WSN architecture is illustrated in Figure 1.Figure 1.Wireless sensors network.Nodes in a sensor network are battery operated and in most situations, battery replacement or recharging is not viable. To achieve prolonged network lifetime, sensor nodes must tailor their activities in an energy-efficient way so that the scarce energy reserves are used very efficiently. Upon deployment, sensor nodes sense, process and communicate an observed phenomenon.

Among these tasks, Drug_discovery communication is considered as the main consumer of sensor energy reserves, thereby imposing strict energy-aware constraints on all communication activities by the sensor nodes [2]. Since routing protocols and media access control (MAC) protocols are directly related to the communication module, hence protocols at these two layers must make an intelligent utilization of the scarce energy resources.

These circumstances also introduce what context might be capable of during an interaction process. Desktop environments are known for being less prone to suffer from context conditions (obviously certain
Transferring the utilization of robots from the repetitive and limited tasks of the industrial environment to more complex operations for interacting with human beings has recently raised growing interest in both the research and applied technology fields. In this context, great improvements are required, not only for in-hand manipulation and exploration tasks, but also for safe operations and interactions with humans. Humanoid robots, unlike the industrial ones, are required to achieve their goals interacting with humans and their tools, adapting to the changes in the environment thanks to an autonomous learning process.

In order to satisfy these requirements, robots need to be able to perform advanced human-like manipulation tasks such as rotation, translation and in-hand grasping [1�C3].To operate in changing environments, humanoid robots need to sense and elaborate the information about the surrounding environment, while interacting with real world objects. By analyzing the force and the position at all points of contact, robots can obtain information about the weight, the stiffness and the surface of a tool and elaborate a way to complete the assigned tasks. In order to satisfy these requirements, there is increased interest in the robotic community in the development of large area or whole-body tactile sensing structures.

Without a high throughput tactile sensing system, humanoid projects strongly limit their interaction and cognitive capabilities [4]. Tactile sensing is also essential for fine manipulation tasks in humans. When our mechanoreceptors are anesthetized, like when our hands are chilled from cold weather, this results in a loss of sensing and our movements become inaccurate and clumsy. Simple operations like lacing up shoes or simply maintaining a stable grasp on an object can become very complex tasks. In order to reproduce human tactile sensing performances for fabricating sensor devices to be implemented in robot hands and bodies, several researchers have defined the guidelines and requirements Entinostat which a robot tactile system has to satisfy for performing the basic in-hand manipulation tasks.

These requirements, presented in Table 1, were determined by analyzing the human sense of touch, but even if they are almost exhaustive, they could be modified depending on the specific application in which the device would be used [3�C7]. Moreover, even if some criteria are strict and technologically challenging, a possible solution to fulfill them could be complex systems integrating different devices instead of using a single tactile sensor.Table 1.Specific requirements for the design of tactile sensor devices to be implemented on human robots.

In the remainder of this paper, we first describe the motivations for the development of MoDisNet system as well as the main contributions of this paper. Then we discuss the novel techniques we provide to address the problems when a sensor grid is constructed based on the mobile and high-throughput real-time data environment. We also present the system architecture to meet the demands of the project as well as the sensor unit itself. This is then followed by the simulation platform design and the networking performance simulation as well as the real-time pollution data analysis scenarios. We conclude the paper with a summary of the research and a discussion of future work.2.

?Motivations and ContributionsRoad traffic makes a significant contribution to the following emissions of pollutants: benzene(C6H6), 1,3~butadiene, carbon monoxide(CO), lead, nitrogen dioxide(NO2), Ozone(O3), particulate matter(PM10 and PM2.5) and sulphur dioxide(SO2). The impact of local air quality pollutants on the environment and health have been studied and well documented [6]. We summarize the interaction and cooperation chain of the population, traffic, air quality and health as Figure 1.Figure 1.The adverse health impacts chain.The figure shows that, increased car ownership and use in urban areas (road traffic) generate some chemical emissions to the air to form the air pollution. With various weather conditions (effected by the temperature, wind, humidity, pressure, etc.

), these pollutants pose different air qualities.

When human beings expose to the polluted air (especially in the urban areas), driving in heavy traffic, near the highways or at the ��downwind�� locations, with the dose-response, people may suffer breathing problems and asthma attacks, which will contribute to risk of heart GSK-3 attacks among people with heart disease.Under the current Environment Act of UK [7], most local authorities have air quality monitoring stations to provide environmental information to public daily via internet. To date, the development of work in these areas has been hampered by critical data gaps and asymmetries in data coverage, as well as the lack of on-line data processing capability offered by the e-Science.

Information on a number Entinostat of key Site URL List 1|]# factors such as individual driver/vehicle activity, pollution concentration and individual human exposure has traditionally either simply not been available or only available at high levels of spatial and temporal aggregation, which average out scientifically critical local variations. For example, the conventional approach to assessing pollution concentration levels is based on data collected from a network of permanent air quality monitoring stations.