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Model Description Sample Clauses

Model DescriptionThe model which uses the Lead-time interface agreement is similar to the model which makes use of the Min-max interface agreement. In the Lead-time interface agreement it is agreed that the repair shop must repair parts within a given lead-time. There are two kinds of lead-times, a regular lead-time and an emergency lead-time. The agreement specifies also the length of the regular lead-time and the emergency lead-time and what percentage of parts can get an emergency lead-time.
Model Description. FCB-FF3W1 EO/OE Box with SMPTE HFO Connector (Female) FCB-FM3W2 EO/OE Box with SMPTE HFO Connector (Male) FCB-OF3W1 EO/OE Box with Japanese HFO Connector (Female) FCB-OM3W2 EO/OE Box with Japanese HFO Connector (Male) ★ ★ ★ ★ Key Features and Benefits • All-in-one solution EO/OE modules and power unit • Ideal for outside broadcasting • Maximizing existing HFO camera assemblies • Flexible configuration for EO/OE modules • AC and DC input redundancy ★ Production by order EO/OE Config. SDI1 Slot EO-100 OE-101 EO-100 OE-101 SDI2 Slot OE-101 EO-100 OE-101 EO-100 HFO Connector Canare FCFR (SMPTE, Female) Canare FCMR (SMPTE, Male) Canare OCFR(Jananese, Female) Canare OCMR (Japanese, Male) SDI I/O Connector 2x 75 ohm BNC EXT Connector 2x XLR3 Female 2x XLR3 Male 2x XLR3 Female 2x XLR3 Male Power Requirement AC100 to 240V, DC 12V Power Consumption Max. 10W Power Connector AC3P Jack XLR4 Male (DC) Operating Temperature 0 to 40°C Dimensions 210x 44x 240mm Weight 1300g Fiber-Optic Systems Canare Cable Checker allows fast, easy confirmation of HFO cables in the field. No heavy equipment to drag around. The compact design features a backlight digital display to measure optic loss/power and electrical continu- ity. Small and light, Canare cable checker helps make mobile installations smooth, secure and constant. FCT-FCKIT FCT-FC FCT-FCLB FCT-OCKIT FCT-OC FCT-OCLB Connectors Key Features and Benefits • Compact, hand-held design • Measured optical loss and power in addition to electrical signals • 2x AA, 20 hours battery life • The kit includes TB-3 storage case, soft cases, AA Batteries, and cleaning sticks Cables Specifications Connector SMPTE/ARIB (Canare FC Series) JAPANESE (Canare OC Series) LD XX-XX Wavelength 1310nm Sensitivity -24 to -2dBm Maximum Length 3.5km (Xxxxxx XX-2SM9R) Optic Lines Two Lines: Power and Loss Copper Lines Power, Control, and Shield: Connectibility Battery/Life 2pcs of AA/ Approx. 20hours Operating Temperature -10 to 60°C Dimensions FCT-FC/OC: 46x 46x 150mm FCT-FCLB/OCLB: 46x 46x 65mm Weight FCT-FC/OC: 380g FCT-FCLB/OCLB: 170g Accessories Included TB-3 storage case, soft cases, AA Batteries, and cleaning sticks Panels & Patchbays CE, FCC, FDA registered FCT-FCLB Hybrid Camera Cable FCT-FC Multichannel Systems United States Patent No.7,113,678 Patent pending in Japan Cable Assemblies Loop-back Quick Reference (Typical Attenuation Value) Under 200m 1.2 2.2 3.2 4.2 5.2 6.2 7.2 8.2 9.2 10.2 11.2 12.2 13.2 14.2 15.2 500m 1.5 2.5 3.5 4.5 5.5 6.5 7.5...
Model Description. The interface agreement in the Min-max case consists of the repair shop having to maintain the inventory level of the Ready-For-Use parts equal to or above a minimum level. The control mechanism of the repair shop is a priority rule, based on the inventory level of SKUs, to adhere to this agreement. The priority rule used is taken into account in the model. The exact priority rule will be explained later on in this section. Inventory control specifies the turn-around stock and minimum levels for every SKU. The values for the turn-around stock and minimum levels are input parameters of the model. . The model developed has five input variables which are unique characteristics for a SKU: These values are needed as input parameters for the different elements in the model. In the following subsections, different sub processes are explained in more detail.
Model Description. The purpose of the Professional Growth Option (PGO) is professional growth and improvement for the employee and student achievement. The PGO is a voluntary evaluation model that provides employees instructional improvement opportunities based on a plan developed from specific goals.
Model Description. ‌ The binary classifier aims to detect which patients belong to the high increasing QoL trajectory (positive class) during the 18 month period from baseline. The negative class emerges from the grouping of the low decreasing and moderate QoL trajectory clusters identified during trajectory analysis (D4.3b), as depicted in Fig. I5.
Model DescriptionThe current PAC FE model was built by CRF and Altair in the past, by assembling existing validated parts of other available FE dummy models and by “interpreting” the possible way to realize their connections, on the basis of a published technical paper [16] describing this experimental tool; no detailed drawings of the modifications implemented on the real dummy were in fact available. Figure 19 shows this model together with its physical counterpart.
Model Description. Four sets of models have been considered and implemented according to the ML-based pipeline towards assessing the predictive power of aggregated M0 and M3 medical, psychological, socio- demographics and lifestyle variables for the overall mental health status and global QoL registered at 12 months post diagnosis and during recovery. between M0/M3 and M12 scores on the same self-report scales, this model explored the optimal prediction capacity of the measurements used in the present study. Model 3 involves prediction of global QoL at M12 based on all available M0 and M3 waves, with the exception of QoL indices, whereas Model 4 involves prediction of Global QoL at M12 using all available variables at M0 and M3. Tree-based classifiers were applied within the ensemble methodology, such as Random Forest (RF), Decision Trees (DTs) and Gradient Boosting Machines (GBM) estimators on a total of 532 (Models 1-2) and 528 patients with sufficient M12 data (Models 3-4).
Model Description. The Enviro-HIRLAM (Environment – High Resolution Limited Area Model) is an online coupled numerical weather prediction and atmospheric chemical transport modelling system for research and forecasting of both meteorological and chemical weather (Korsholm et al 2008; Xxxxxxxx et al., 2009; Xxxxxxxx et al., 2008). The meteorological and chemistry model solve the governing equations describing the main processes: emission, advection, horizontal and vertical diffusion, wet and dry deposition, convection, chemistry and aerosol feedbacks. The system realisation includes the nesting of domains for higher resolutions, different types of urbanization; implementation of chemical mechanisms and aerosol dynamics and feedback mechanisms). Natural land covers are simulated by the Interaction Soil-Biosphere-Atmosphere (ISBA) land surface scheme, originally developed by Noilhan and Xxxxxxx (1989) and further up-dated in the HIRLAM model as modified by Xxxxxx et al. (2005) to include the urban effects implementing the Building Effect Parameterization (BEP, Xxxxxxxx et al., 2002) module. BEP represents the city by a combination of several urban districts. Each district is classified as a combination of multiple streets and buildings of constant widths but with different heights and with similar thermo-dynamical characteristics. The parameterization includes computation of contributions from every facet of the urban substrate (street canyon floor, roofs and walls of buildings) for the momentum, heat and turbulent kinetic energy equation separately as contributions of the vertical surfaces (building walls) as well as horizontal surfaces (floors and roofs).
Model Description. Consider a supermarket that buys the products from its suppliers based on its prediction regarding the selling amounts during the shelf lives of the products. The Supermarket usually buys the goods more than a specific amount from the suppliers to benefit from the wholesale price and reduce the transportation cost. The selling periods of each product are categorized based on the quality of the items in terms of the number of remaining days before their expiration date. For instance, consider the shelf life of a product is four weeks and it is divided into four periods with the length of one week. In other words, the supermarket divides the shelf life of the product into four quality levels. The highest quality level with the highest selling price belongs in the first week and the quality of the proposed product will reduce during the remaining weeks. The second and third quality levels belong to the second and third weeks respectively and the supermarket considers the lowest quality level for the last week with the lowest selling price. The error in selling prediction may result in food waste in the supermarket. To avoid waste, the supermarkets usually offer more discounts for the products that are near their expiration dates or donate them. Despite using these strategies, a huge amount of food is being wasted in supermarkets each year. It seems that they need better strategies to avoid food waste as well as sales losses. One of the main reasons that the supermarkets cannot decide quickly about the surplus products is that there are many types of products in a supermarket and deciding regarding the surplus inventories based on the selling rates and the expiry dates is time-consuming. Therefore, automating this process using the new technologies and smart contracts can be helpful for the supermarkets to act more agile regarding the surplus inventory before that they are wasted. Moreover, they can cooperate with more organizations and partners to minimize food waste by deciding about the surplus inventory at the least time. Figure (2) shows an example of a supermarket that purchases a product such as apples at wholesale price from the supplier and divides the shelf life of the product into n periods (considering n quality levels for the product). The product has the highest quality during the quality period 1 and has the lowest quality in the quality period n. If it cannot sell the products before the end of period n, the remaining products will be wasted. T...
Model DescriptionThe concept is to model time windows at customers, inventory constraints at production sites and capacity constraints of vessels in the same model to capture the complexity of the maritime distribution problem. The production sites produce several product types and send shipments by vessels to customers, the number and locations of customers can change from any time horizon to the next. The terminals are used as depots and to transmit products to customer locations by trucks. The model contains a node set N that contains all locations. This node set is split into subsets of loading and unloading nodes. Let be the set of production sites, the set of terminals and the set the customers. The set are defined as loading nodes and the set are defined as unloading (customer) nodes. The quantity given in the orders are deterministic values, the concept is to accommodate the model to assign the appropriate vessels, in order to bring the demanded order sizes to the customers. In the model we only consider the possibility of carrying one order per shipment, but it is possibly to carry multiple products on an order. The fleet of heterogeneous vessels is represented by the set , and we let the set be the products Nodes Production sites Terminals Customers Product line at site , Vessels Orders Number of days in the time horizon Maximal number of tours per vessel carrying an order in the time horizon Shipment cost between node and node , assume same cost structure for all vessels, Travel time from node to node , assume same speed for all vessels, Quantity in order of product , Daily production rate of product at production site , Capacity of inventory bin for product at production site , Draft vessel , Depth node , Capacity vessel in tons, Possible exit nodes for order , Destination node for order , Loading/unloading time per ton for vessel at node , Initial inventory of product production site , Arrival time to initial position for vessel , Initial position of vessel , Earliest arrival time at customer , Latest arrival time at customer , Earliest arrival time of order , Latest arrival time of order , Large number 1 if vessel v travels from to carrying order on tour , 0 otherwise Inventory level of product at site at the end of day Time when vessel arrives at node carrying order on tour 1 if order is delivered from site on day , 0 otherwise Amount of product delivered from site during day Day that order departs from node 1 if vessel arrives at node with order before vess...