Talk This Way: A Look at the Historical Conversation Between Hip-Hop and Christianity

Christianity and Hip-Hop culture are often said to be at odds with one another. One is said to promote a lifestyle of righteousness and love, while the other is said to promote drugs, violence, and pride. As a result, the public has portrayed these two institutions as conflicting with no willingness to resolve their perceived differences. This paper will argue that there has always been a healthy conversation between Hip-Hop and Christianity since Hip-Hop’s inception. Using sources like Hip-Hop lyrics, theologians, historians, autobiographies, sermons, and articles that range from Ma$e to Tipper Gore, this paper will look at the conversation between Hip-Hop and Christianity that has been ongoing for decades. This thesis will show why that conversation is essential for the church and necessary for Hip-Hop artists to express themselves fully. This paper will show rap and Hip-Hop culture to be a complex institution with its own theology, history, and prophets – that uses its own voice to express how urban youth view not only their lives but also how God and the church are present in their lives

Emergency Preparedness and Response: Examining Rural Hospitals (RHs) Communication Systems Before, During, and After a Natural Disaster

Introduction: Rural Hospitals (RHs) have distinctive characteristics that constitute unparalleled challenges. One of those challenges is the ability to communicate effectively in a disaster, impacting the various effects on the affected society. Research has shown that effective emergency preparedness and response (EPR) communication is paramount when communicating informed details about emergent events. Despite the evolving complexities of communication and the technology associated with disaster communication, very few studies have successfully investigated RHs communication systems before, during, and after a natural disaster. The purpose of this study was to examine and assess RHs communication systems and to highlight the strengths, identify any gaps, areas for improvement, and best practices utilized by RHs pre/post and during a natural disaster. Findings included preparedness and response efforts. Methods: 24 semi-structured interviews were conducted with RH leaders, which comprised 12 females and 12 males with expertise and knowledge in emergency preparedness and disaster communications. The Centers for Disease Control and Prevention (CDC) Crisis and Emergency Risk Communication (CERC) Model was applied to examine RHs disaster communication systems and participants\u27 knowledge of the communication platforms/systems used at their facility and in their community to deliver information in an emergency. Emerging themes and sub-themes were identified from the participant interviews. Rapid Qualitative Data Analysis was utilized to analyze and code the interview transcriptions. Results: Many participants reported being aware of or familiar with emergency preparedness and communication models or plans used at their hospital for disasters; however, outdated emergency operation and communication plans, platforms/systems, staffing, and funding continue to pose challenges for RHs in this area. Conclusion: The study results provide insight into the importance of RH communication and communication systems, operations, platforms, and partnerships needed during crises, natural/man-made disasters, and emergencies. Although the focus of this research was to examine “natural disaster” communication and information systems/technology in RHs, several hazards that can and have led to disasters were identified by participants that present challenges to how they prepare to communicate and respond before, during, and after a disaster or catastrophic event. Overall, these findings could serve as an outline for the implementation of enhanced communication platforms/systems, development of a standardized communication model, improved emergency operating protocols in disasters for RHs, increased funding, addressing challenges with healthcare communications, public health emergency communications in the United States, and on a national level

Converging Human Intelligence With AI Systems to Advance Flood Evacuation Decision Making

The powers that artificial intelligence (AI) has developed are astounding, with recent success in integrating into a human cognitive workflow. AI will attain its full potential only if, as part of its intelligence, it also actively teams up with humans to co-create solutions. Combining AI simulation with human understanding and strategic abilities through data convergence may optimize the process and provide a capacity akin to teaming intelligence. This thesis will introduce the concepts of Human AI Convergence (HAC) capabilities for flood evacuation decision-making. The concept introduced in this thesis is the first step toward the HAC concept in weather disaster applications. This research demonstrates a synergy between humans and AI by integrating the data produced by humans through social media with an AI system to enhance a flood evacuation decision-making problem. The prediction from Long short-term memory (LSTM) and a river hydraulic model, i.e., Height Above Nearest Drainage (HAND), is integrated with human data from X (previously Twitter) to visualize flood inundation areas, which acts as a 3rd party agent for a HAC system. The goal is to synthesize and analyze HAC competence in flood evacuation emergency management and harness the full potential of AI as a partner in real-time planning and decision-making. This thesis has explored why HAC intelligence is essential to emergency planning and decision-making, providing a general structure for researchers to use HAC to devise effective systems that cooperate well and evaluate state-of-the-art, and, in doing so, providing a research agenda and a roadmap for future flood evacuation emergency management, rescue, and decision making. This state-of-the-art flood evacuation product stands to advance the frontier of human-AI collaborative research significantly

Elastic neural network method for load prediction in cloud computing grid

Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e.g. urban driving scenes. Motivated by the success of 2D recognition, we revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. Omni3D re-purposes and combines existing datasets resulting in 234k images annotated with more than 3 million instances and 98 categories. 3D detection at such scale is challenging due to variations in camera intrinsics and the rich diversity of scene and object types. We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object recognition and show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training.Comment: CVPR 2023, Project website: https://omni3d.garrickbrazil.com

2019-2020 Piano and Strings Concert

The chamber music program will feature young virtuosi from the piano and strings studios.https://spiral.lynn.edu/foc-events/1035/thumbnail.jp