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Some entrepreneurs lack the knowledge and skills to deal with risks created by business uncertainties. Small business entrepreneurs must develop strategies to help them run successful businesses for a long time. Grounded in the uncertainty management theory and enterprise risk management, the purpose of this qualitative study was to explore strategies entrepreneurs, especially those with startups within the food service industry, use to deal with risks created by business uncertainties. Participants were five food and service entrepreneurs within the Northeast region of the United States who successfully dealt with risks caused by business uncertainties

Real estate marketing has traditionally been accomplished through conventional media outlets such as print advertising since the inception of the industry. However, the digital evolution is changing the way real estate professionals engage with their customers. This doctoral study explored effective digital marketing strategies for increased engagement in real estate. The general business problem was that it was not known what digital marketing strategies were used by property managers at GildEstates to increase consumer engagement or how those marketing strategies could be improved. The specific business problem was that some Real Estate managers lack the necessary digital marketing strategies to increase consumer engagement. The purpose of this qualitative case study was to determine what digital marketing strategies were used by GildEstates to increase consumer engagement and how those marketing strategies could be improved

Cosmological measurements of the average spatial curvature of the spacetime metric are one of only a handful of methods available for testing the inflationary paradigm for the early universe. Current observations are consistent with the inflationary prediction of a nearly flat universe. However, the precision of curvature measurements is only at the percent level at best (e.g., Eisenstein et al., 2005; Komatsu et al., 2009), whereas the expected level of curvature in standard inflationary scenarios is generally much smaller.

On March 1, 2018, Russian President Vladimir Putin unveiled several new nuclear technologies in his annual address to Russia's Federal Assembly, including systems such as a hypersonic glide vehicle (HGV), nuclear-armed unmanned underwater vehicle (UUV), and nuclear-powered cruise missile that would be the first such weapons deployed in the world.1 These systems, according to Putin, would ensure the invincibility of Russia's nuclear arsenal.During the speech, Putin played a video showing one of the new systems-the Sarmat ICBM striking targets in Florida, the home state of then-President Donald Trump.2 Four years later,Russia would use the conventional variant of one of their novel systems-the Kinzhal hypersonicaeroballistic missile-to strike an underground arms depot in Western Ukraine.3 The use of Kinzhal as part of Russia's war on Ukraine came after deploying it to Kaliningrad and Syria-placing it near the borders of multiple NATO members.4

Smart home technology (SHT), an Internet of Things (IoT) application, is a growing industry centered on the remote control of devices and networks that offers convenience, cost savings,energy efficiency, and improved quality of life. However, in the face of mounting evidence, several federal agencies and security experts have voiced concerns about the susceptibility of these networked devices to cyber-attacks. They are also susceptible to secure shell (SSH) brute force attacks, often followed by the propagation of malware through botnets. Recently, machine learning (ML) algorithms have been deployed for anomaly detection based on similarities and trends in network traffic. Thus, ML algorithms may be used to develop prediction models for detecting network attacks automatically. This study offers a comprehensive examination of the use of ML techniques to detect these two prevalent SHT network attacks, SSH brute force and botnet attacks.

Suicide is a multi-faceted human experience that threatens and harms Black communities.Historical data, critical theories, and extant research literature suggest that such threats and harms result from interactions between macrosystemic systemic forces and individual level meaning-making processes. To explore the dynamic and nuanced complexities between systemic forces and deaths often labelled "suicides" in contemporary U.S. Black communities, this project centered and amplified the critical perspectives of fourteen Black female clinicians. Because of their marginalized identities, intersectional lived experiences, critical orientation, and clinical training, these participants were wellpositioned to analyze and understand the degrees to which suicides in Black communities are associated with oppressive macrosystemic dynamics and/or individual-level psychological factors.

Jack is a mid-level manager in the insurance industry. For the past two years, Jack has observed what might be considered hubristic behavior of his Executive Team. Hubris behavior is described as a form of destructive pride that leads to actions like aggression, hostility, relational issues, interpersonal conflicts, and other negative behaviors (Tracy and Robins, 2007). Jack was frustrated with leaders' egos interrupting decision-making.For example, in a recent team meeting, Jack and his colleague took a leap of faith and shared a challenge in a training program. When they shared their critiques, the executive leader heading the training project exploded out of anger, blasted them with negative sentiments, and left the call. This explosive reaction was driven by the executive's egos. It was also not the only time Jack had been on the receiving end of this behavior. As a result of other experiences similar to this, Jack decided to resign a few weeks after the incident. Jack was not alone in this decision, as several mid-level managers in the same company resigned around the same time as him.

This research investigated the potential to enhance global science data sharing through market-driven strategies utilizing technology platform-based business models compared to the existing fiat strategy characterized by government-funded programs and highly fragmented information systems. With less than 1% of science data shared globally, and some estimates of data collection time-cost valued at USD billions per year, there is significant social value to be released in addressing this issue.

Neural language models are probabilistic models of text parameterized by neural networks. They are widely applicable to applications with outputs consisting of discrete sequences, such as document summarization, question answering, and image captioning. The minimal assumptions about data enable advancements in language modeling to drive improvements across a diverse array of applications. In natural language, structures are both pervasive and essential. For example, a bookis organized into chapters, with a logical flow connecting them; without this structure, the book would lose its coherence. Therefore, effectively understanding and modeling textual sequences requires comprehending and representing the inherent structures. This thesis focuses on structure modeling for language models.

Platforms consisting of neutral atoms individually trapped in arrays of optical tweezers have achieved important milestones in quantum simulation and quantum computation. With access to programmable Rydberg interactions, individual qubit control, longcoherence times, and naturally identical qubit transitions, neutral atoms are a promising building-block for the use in future quantum machines. However, they face the same challenge as other quantum platforms in scaling the number of qubits needed to perform algorithms with a provable advantage over classical computers. An avenue toward scaling systems of neutral atoms may be to use a quantum network that connects individual systems to construct a distributed computing architecture.

Quantum science and technology provides new possibilities in processing information, simulating novel materials, and answering fundamental questions beyond the reach of classical methods. Realizing these goals relies on the advancement of physical platforms, among which superconducting circuits have been one of the leading candidates offering complete control and read-out over individual qubits and the potential to scale up. However, most circuit-based multi-qubit architectures onlyinclude nearest-neighbor (NN) coupling between qubits, which limits the efficient implementation of low-overhead quantum error correction and access to a wide range of physical models using analog quantum simulation

Nonlinear photonics has provided a scientific cornerstone for a majority of modern technology during the past half-century, such as diversifying laser wavelengths, manufacturing nanostructures, and guiding the design of telecommunication systems.Moreover, it keeps supporting the emergence of novel applications, including high-resolution spectroscopy, atomic clocks, and especially quantum technology. However, in photonic quantum state engineering, current recipes for design and modelingare impotent in specific quantum applications, for which advanced techniques are urgently needed. In this dissertation, the quantum-level interplay between the linear response of integrated photonic devices and multiple nonlinearities withinthe system has been investigated in detail. We utilize these interactions to manage the generation of limit-breaking quantum photonic states, including customized temporal-spectral entanglement, high-brightness quantum sources, and high-purityquantum states.