Inclusion Of Students With Mild To Moderate Disabilities In Grades 1-5 Mainstream Language Arts Classrooms

The research question addressed in this capstone project was what are best practices for creating an inclusive language arts program in grades 1-5 in an elementary school? It documents the history of special education and inclusion in the United States, least restrictive environment (LRE), benefits and barriers to inclusion as well as strategies to create an inclusive language arts setting. The author uses research to prepare and present a PowerPoint presentation to licensed teachers to be used as staff development. The PowerPoint presentation addresses the history of special education and inclusion, how LRE is determined, benefits and barriers to inclusion, strategies for creating an inclusive language arts setting as well as how students are selected for inclusion in language arts, service time for students in this setting as well as a comparison of standardized test scores between a school using the inclusive language arts model and one which does not. Supplemental material are also provided for ongoing staff development including information on specific disabilities

Control of free-ranging automated guided vehicles in container terminals

Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces

A Data-Driven Approach for Modeling Agents

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating a gap in the literature. This dissertation proposes a novel data-driven approach for modeling agents to bridge the research gap. The approach is composed of four detailed steps including data preparation, attribute model creation, behavior model creation, and integration. The connection between and within each step is established using data flow diagrams. The practicality of the approach is demonstrated with a human mobility model that uses millions of location footprints collected from social media. In this model, the generation of movement behavior is tested with five machine learning/statistical modeling techniques covering a large number of model/data configurations. Results show that Random Forest-based learning is the most effective for the mobility use case. Furthermore, agent attribute values are obtained/generated with machine learning and translational assignment techniques. The proposed approach is evaluated in two ways. First, the use case model is compared to another model which is developed using a state-of-the-art data-driven approach. The model’s prediction performance is comparable to the state-of-the-art model. The plausibility of behaviors and model structure in the use case model is found to be closer to real-world than the state-of-the-art model. This outcome indicates that the proposed approach produces realistic results. Second, a standard mobility dataset is used for driving the mobility model in place of social media data. Despite its small size, the data and model resembled the results gathered from the primary use case indicating the possibility of using different datasets with the proposed approach

Strategies for Technology Selection in the Retail Sector

Some small retail business owners lack strategies to select the appropriate technology to achieve sustainable competitive advantages, which is why some small businesses fail. Grounded in the resource-based theory, the purpose of this qualitative multiple case study was to explore the strategies small retail business owners use to choose the appropriate technology to achieve sustainable competitive advantages. The participants comprised seven small retail business owners in the U.S. state of New Jersey who successfully implemented strategies to select the appropriate technology for their organization’s success. Data were collected through semistructured interviews and observations of the business owners’ technological footprints. Data were analyzed using thematic analysis. Three themes emerged: (a) customer relationship management, (b) marketing, and (c) process improvement. A key recommendation is for small retail business owners to use social media platforms such as Instagram and Facebook as tools for customer relationship management and development. The implications for positive social change include the potential to contribute strategies and knowledge that retailers can use to facilitate communication and consumer transactions. Implementation of these strategies could allow retailers to be more efficient and competitive, boost local economies, and improve customer satisfaction

Multi-cloud Security Mechanisms for Smart Environments

Achieving transparency and security awareness in cloud environments is a challenging task. It is even more challenging in multi-cloud environments (where application components are distributed across multiple clouds) owing to its complexity. This complexity open doors to the introduction of threats and makes it difficult to know how the application components are performing and when remedial actions should be taken in the case of an anomaly. Nowadays, many cloud customers are becoming more interested in having a knowledge of their application status, particularly as it relates to the security of the application owing to growing cloud security concerns, which is multi-faceted in multi-cloud environments. This has necessitated the need for adequate visibility and security awareness in multi-cloud environments. However, this is threatened by non-standardization and diverse CSP platforms. This thesis presents a security evaluation framework for multi-cloud applications. It aims to facilitate transparency and security awareness in multi-cloud applications through adequate evaluation of the application components deployed across different clouds as well as the entire multi-cloud application. This will ensure that the health, internal events and performance of the multi-cloud application can be known. As a result of this, the security status and information about the multi-cloud application can be made available to application owners, cloud service providers and application users. This will increase cloud customers’ trust in using multi-clouds and ensure verification of the security status of multi-cloud components at any time desired. The security evaluation framework is based on threat identification and risk analysis, application modelling with ontology, selection of metrics and security controls, application security monitoring, security measurement, decision making and security status visualization

A generic provenance framework to document public policy making processes

Public policies impact the day to day activities of individuals. Effective public policy outcomes result in general acceptance among the community. The transparency in policy making process and participation during policy creation holds significant positions for developing trust among the community. Established domains such as e-health employs provenance for creating transparency and trust among the researchers. Public policy making can also use provenance to develop trust and transparency in their processes. At present, however, public policy makers employ various means to manage public policy making. Having no unified platform for the policy making process presents challenges in respect of searching and locating the evidence that was used during policy creation and for ensuring trust and transparency among actors. The absence of such a support also presents challenges for participation in public policy making. To address the given challenges, this research presents the provenance framework that manages the public policy making provenance data and enable participation of diverse actors.Due to dynamicity attached to public policy making, a provenance framework needs to be adaptable. Therefore, a model-driven approach has been used to frame the public policy making provenance framework. In addition to a model-driven approach, a mechanism is required that can enable the capture of public policy processes. However, the knowledge-intensive dynamics of public policy making presents challenges for using process-based solutions. Therefore, this research work describes a process-agnostic approach inspired from a network-based packet switching approach for tracking policy making processes. Managing public policy provenance data is not the only facet that develops trust. What is required is the facilitation of citizens and non-government bodies in the policy creation process. Therefore, a provenance framework has been designed by considering the principles of smart governance which results in a smart cities solution. In order to evaluate the framework, a proof-of-concept has been designed and implemented. An evaluation has been carried out to determine the suitability of a model-driven and a process-agnostic approach for policy making provenance framework in smart cities. For the evaluation purposes, three public policy making case studies Shops Opening Hours’ Resolution, Air Quality Monitoring, and Neighbourhood Planning were employed. The three case studies were used to derive various experiments to test the provenance framework. The experiments captured the dynamic and knowledge-intensive aspects of the provenance framework. The results collected from the execution of the experiments demonstrated the aptness of a process-agnostic approach and model-driven approach for the policy making provenance framework. Lastly, an end user evaluation was carried out to assess the effectiveness of the provenance framework. The positive responses of end users showed the usefulness of the provenance framework

A Comparative Evaluation of State Policies and Programs for Nonpoint Source Pollution Control in the Chesapeake Bay Watershed

The U.S. Environmental Protection Agency (EPA) has reported that over 45 percent of the nation\u27s waterbodies are impaired and has identified nonpoint sources as the major contributors to water quality problems. Although federal and state government agencies have largely controlled pollution from point sources through infrastructure grants and permit programs, few statutes and regulations target nonpoint sources. One exception is the Clean Water Act\u27s Total Maximum Daily Load (TMDL) regulations that require the states to identify causes and sources of impairments and allocate pollutant loads for point and nonpoint sources to achieve the fishable, swimmable standard of water quality. However, the federal and state governments have made little progress towards implementation of TMDLs and enforcement of other nonpoint source pollution controls. Government entities at all three levels--federal, state, and local--have not enforced requirements for pollution control, have lacked coordination with interested parties, and have implemented primarily rigid command-and-control programs. Traditional nonpoint source control programs and policies are not effective in reducing nonpoint source pollution. As an alternative to traditional regulation and program approaches, federal policy has moved to manage pollution in our waterways with flexible and innovative programs, such as water pollution trading and offsets. This research evaluates nonpoint source pollution policies and programs at the federal, state, and local levels, using the Chesapeake Bay watershed as a case study. The Chesapeake Bay, the largest estuary in the United States, is not meeting water quality standards due to high concentrations of nutrients (nitrogen and phosphorus) and sediment, among other contaminants. This research determines the types of regulations and programs that government entities have implemented within a multi-state watershed and assesses their impacts on water quality. Using qualitative and quantitative measures, this study evaluates environmental impacts, economic factors, land-based indicators, as well as, program structure and implementation on nonpoint source pollution. Additionally, this research identifies factors that contribute to the effectiveness of nonpoint source pollution reduction programs. The multi-criteria state evaluation and local watershed prioritization discern the major characteristics that result in effective programs and policies and provide insight into nonpoint source program and policy improvements