Hampton University is a private, historically Black, research university in Hampton, Virginia. Founded in 1868 as Hampton Agricultural and Industrial School, it was established by Black and White leaders of the American Missionary Association after the American Civil War to provide education to freedmen. The campus houses the Hampton University Museum, which is the oldest museum of the African diaspora in the United States and the oldest museum in the commonwealth of Virginia. First led by former Union General Samuel Chapman Armstrong, Hampton University's main campus is located on 314 acres in Hampton, Virginia, on the banks of the Hampton River. The university offers 90 programs, including 50 bachelor's degree programs, 25 master's degree programs and nine doctoral programs. The university has a satellite campus in Virginia Beach and also has online offerings. Hampton University is home to 16 research centers, including the Hampton University Proton Therapy Institute, the largest free-standing facility of its kind in the world. Hampton University is classified among "R2: Doctoral Universities – High research activity.
Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods.
The electroproduction of eta' mesonsfrom a( 1)H target atW = 2.13 GeV, Q(2) = 0.46 (GeV/c)( 2) , and cos theta(CM) (gamma & lowast;eta ' )approximate to -1 has been experimentally measured. The differential cross section of virtual photoproduction has been obtained as 4.4 +/- 0.8 (stat.) +/- 0.4 (sys.) nb/sr in the OnePhoton-Exchange Approximation. This value is one-sixth of that of real photoproduction at backward angles. A comparison with newly developed isobar model calculations not only showsthe validity of the theoretical framewark employed, but also imposes new constraints on coupling strength between the eta p final state and nucleon resonances
Measurements of semi-inclusive deep-inelastic scattering multiplicities for pi(+) and pi(-) from proton and deuteron targets are reported on a grid of hadron kinematic variables z, P-T, and & varphi;* for leptonic kinematic variables in the range 0.3 < x < 0.6 and 3 < Q(2) < 5GeV(2). Data were acquired in 2018 and 2019 at Jefferson Lab Hall C with a 10.6 GeV electron beam impinging on 10-cm-long liquid hydrogen and deuterium targets. Scattered electrons and charged pions were detected in the High Momentum Spectrometer and Super High Momentum Spectrometer, respectively. The multiplicities were fitted for each bin in (x, Q(2), z, Pt) to extract the & varphi;*-independent M-0 and the azimuthal modulations < cos(& varphi;*)> and < cos(2 & varphi;*)>. The Pt dependence of the M-0 results was found to be remarkably consistent for the four cases studied: ep -> e pi(+) X, ep -> e pi(-) X, ed -> e pi X+, ed -> e pi X- over the range 0 GeV < P-t < 0.4GeV, as were the multiplicities evaluated near & varphi;*=180(degrees) over the extended range 0GeV < P-t < 0.7GeV. The Gaussian widths of the P-t dependence exhibit a quadratic increase with z. The cos(& varphi;*) modulations were found to be consistent with zero for pi(+), in agreement with previous world data, while the pi(zs) moments were, in many cases, significantly greater than zero. The cos(2 & varphi;*) modulations were found to be consistent with zero. The higher statistical precision of this dataset of about 20 000 individual multiplicity values, compared with previously published data, should allow improved determinations of quark transverse momentum distributions and higher twist contributions.
We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic power flow analysis and fault characteristics. In fact, computational efficiency is the main focus to enhance, whilst we ensure the performance accuracy at the accepted level. Note that Machine Learning (ML) based schemes have a requirement of sufficient labeled data during training, which is not easily satisfied in practical applications. Also, there are unknown data due to new arrived measurements or incompatible smart devices in complex smart grid systems. These problems would be resolved by our proposed GAT based framework, which models the label dependency between the network data and learns object representations such that it could achieve the semi-supervised fault diagnosis. To create the joint label dependency, we develop the graph construction from the raw acquired signals by using CAN. Next, we develop the probabilistic graphical model of Markov random field for graph representation, which supports for the GAT based framework. We then evaluate the proposed framework in the use-case application in smart grid and make a fair comparison to the existing methods.
Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge. While traditional optimal control methods can find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as a simple, circular `no-go' zone. A mission failure is claimed if the vehicle enters this `no-go' zone at any time or does not reach a neighborhood of the destination. The DDPG agent is trained to learn a direct mapping from its current state (position and velocity) to a series of feasible actions that guide the agent to safely reach its goal. A reward function and two neural networks, critic and actor, are used to describe the environment and guide the control efforts. The DDPG trains the agent to find the largest possible set of starting points (“feasible set”) wherein a safe path to the goal is guaranteed. This provides critical information for mission planning, showing beforehand whether a task is achievable from a given starting point, assisting pre-mission planning activities. The approach is validated in simulation. A comparison between the DDPG method and a traditional optimal control (pseudo-spectral) method is carried out. The results show that the learning-based agent may produce effective paths while being significantly faster, making it a better fit for real-time applications. However, there are areas (“infeasible set”) where the DDPG agent cannot find paths to the destination, and the paths in the feasible set may not be optimal. These preliminary results guide our future research: (1) improve the reward function to enlarge the DDPG feasible set, (2) examine the feasible set obtained by the pseudo-spectral method, and (3) investigate the arc-search IPM method for the path planning problem.
