Proposed Model. This section presents an enhanced authentication protocol for EI-based vehicle to grid communication. Our scheme assumes the similar system architecture as G & S has illustrated in its protocol. In this system model, the three entities namely, user Ui with mobile device, a charging station CSj, and a utility service provider ESP, cooperate one another to enable the mutual authenticity between Ui and CSj. In this manner, the user may qualify for the stipulated recharging services. Our scheme comprises two phases; user registration andmutual authentication phase.
Proposed Model. In this section, we begin by presenting the state-of-the- art mechanism that are the main subjects of our research. In particular, we provide a brief description of the routing-by- agreement mechanism proposed in [45], and an introduc- tion to the self-attention layer [49] functionalities. Then, in Sections 3.3 and 3.4, we describe the similarities between the two mechanisms to provide the theoretical foundations behind the proposed model.
Proposed Model. The recommended model for estimating the change in frequency of human-induced landslides, based on a changing population, is: Δf = Fdev ⋅ Fpop ⋅ aL% ⋅ (ΔP – MΔP) or Δf = Fdev ⋅ Fpop ⋅ aL% ⋅ (1− M)⋅ ΔP where aL% was defined in section 6.3, Fdev, Fpd and M are the factors described in Tables 6.3.1, 6.4.1 and 6.5.1, and ∆P represents the change in population density, as a percentage4. The change in hazard level is directly proportional to the frequency change and can be classified qualitatively as indicated in Table 6.6.1. 4 Since the area of the region is considered unchanging, the change in population density (as a percentage) is equivalent to the change in population (as a percentage).
6.6.1 Change in hazard level of human-induced landslides
Proposed Model. Given the exploratory nature of this study, a Poisson regression with counties as subunits and aggregate covariates was chosen to evaluate the influence between distance from primary distribution center and US county case counts in the 2018 Yuma outbreak. The basis for this model is below: 𝑃𝑜𝑖𝑠(𝜆) = 𝑒!x x! For counts yi in area i (in this case US counties), there is an independently identically Poisson distribution of cases, with the expectation in area i as ei. Multiplied by the θI as the area risk, we get: 𝑦𝑖 , 𝑖𝑖𝑑 ~ 𝑃𝑜𝑖𝑠(𝑒𝑖1𝑖), 𝑖 = 1, . . . . , 𝑛 In the literature review, potential confounding variables, associated with the exposure, which is a measure of supply chain configurations and thereby geographic distribution, and the outcome, county counts of E coli O157:H7. These covariates are described in the data dictionary, but are briefly described below in Table 3. Distances Distance to Food Flow Center Exposure of interest Mar_y March Average Temperature in 2018 Environmental temperature at point of retail was highlighted as potential reason for increased E coli growth in produce supply chains. PropU15 County Proportion of population under age 15 in 2018 Severe illness among persons under 15 are more probable and thereby more likely to contribute to case counts PropOver60 County Proportion of population over 60 in 2018 Severe illness among persons over 60 are more probable and thereby more likely to contribute to case counts PropFemale County Proportion of population that is female sex in 2018 Dietary habits and risk among women who are pregnant may contribute to greater risk of illness form E coli TotalPop County population in 2018 Used for offset in Poisson Regression The overall full model with covariates is below: ln(𝜆 ) = ln 7𝐸(𝑌𝑖); 𝑖 𝘗𝑖 = 𝛽0 + 𝛽1𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑠 + 𝛽2𝑀𝑎𝑟_𝑦 + 𝛽3𝑃𝑟𝑜𝑝𝑈15 + 𝛽4𝑃𝑟𝑜𝑝𝑂𝑣𝑒𝑟60 + 𝛽5𝑃𝑟𝑜𝑝𝐹𝑒𝑚𝑎𝑙𝑒 + O1𝑃𝑟𝑜𝑝𝑂𝑣𝑒𝑟60 ∗ 𝑃𝑟𝑜𝑝𝐹𝑒𝑚𝑎𝑙𝑒 + O2𝑃𝑟𝑜𝑝𝑈15 ∗ 𝑃𝑟𝑜𝑝𝐹𝑒𝑚𝑎𝑙𝑒 + Q1𝑀𝑎𝑟_𝑦 ∗ ��𝑖𝑠𝑡𝑎𝑛𝑐��𝑠 To model this in SAS 9.4, the “offset” is carried over to model the loglinear association of the expected value. Because the model is explicitly evaluating the effect on this outbreak, the duration of the outbreak was used to estimate the person time exposed to contaminated lettuce from this specific public health event. The offset in this model is Person-Years calculated below: 𝘗 = 𝑇𝑜𝑡𝑎𝑙𝑃𝑜𝑝 ∗ S 84 W With the offset, the model being derived from the...
Proposed Model. In the second chapter we present a proposed model for leveraging CA technology in MOOCs as implemented in the context of the colMOOC project. First, in the subsection “Identified gaps to guide the conversational agent design approach”, we analytically discuss the three major pillars of the proposed design, namely: • The colMOOC agent to support productive forms of peer dialogue (cognitive dimension) • The colMOOC agent as domain-independent teachers’ open tool (socio-cultural dimension), and • The colMOOC agent as an interoperable tool to be integrated in MOOCs platforms (technological dimension). Then, we move on to discuss specific aspects of the proposed model such as: • The agent intervention strategies in students’ chat activities • The agent software key components: the Editor and the Player • The agent domain configuration by the teacher
Proposed Model. In proposed scheme, we have introduced an improved version of Xxx’s scheme that not only provides the same level of security with anonymity and untracebility, at a lesser cost, but also protects the user from DoS and stolen verifier attacks. The proposed model comprises four phases, i.
Proposed Model. It is clear from the previous section that VSO and SVO words ordering is important and should be used carefully. This proposal focuses on the agreement requirements based on VSO or SVO pattern is used. The subsequent examples in tables 3 through 6 show different agreement requirements between the verb and the subject depending on whether VSO or SVO words ordering are used. The selection of using VSO or SVO related to the context where we use SVO whenever the subject is our focus.
Proposed Model
