VCU Pathfinder

Imagine this, you are planning to enroll in an institution of higher learning. However, your schedule can\u27t possibly match your institutions recommended path. Maybe you\u27re transferring from a different institution, maybe you just picked up additional hours on the job, or maybe you just want to change to a different major. Getting all the information you need in one place to plan a personalized schedule can be a difficult proposition, especially if you don\u27t have easy access to an on-site adviser. VCU Pathfinder is here to make this process considerably less stressful and more efficient. Pathfinder is a schedule assistance tool available through a web browser. All the information that a student needs to know in regard to planning their schedule, such as degree requirements, course prerequisites, credit amount, and individual semester availability of courses, is handled by the website. By utilizing an intuitive and flexible interface that color codes courses according to what a student can take according to the degree they\u27ve chosen, Pathfinder answers the question of Can I take this course at this date? . It\u27s as simple as that. There\u27s no need to dive through bulletins to understand your course flow or when courses are available. All the information used by the service is maintained in a Maria database that can be easily edited by an adviser with no prior programming experience.https://scholarscompass.vcu.edu/capstone/1177/thumbnail.jp

Models for high dimensional spatially correlated risks and application to thunderstorm loss data in Texas

Insurance claims caused by natural disasters exhibit spatial dependence with the strength of dependence being based on factors such as physical distance and population density, to name a few. Accounting for spatial dependence is therefore of crucial importance when modeling these types of claims. In this work, we present an approach to assess spatially dependent insurance risks using a combination of linear regression and factor copula models. Specifically, in loss modeling, observed dependence patterns are highly nonlinear, thus copula-based models seem appropriate since they can handle both linear and nonlinear dependence. The factor copula approach for estimating the spatial dependence reduces a complex dependence structure into a relatively easier task of estimating a spatial dependence parameter. Hence, we use a weighted sum of radial basis functions to model a spatial dependence parameter that determines the influence of each location. The methodology is illustrated using a thunderstorm wind loss dataset of Texas. Extensions to Matérn covariance functions and spatiotemporal models are briefly discussed --Abstract, page iii

Demand driven salt clean-up in a molten salt fast reactor – Defining a priority list

<div><p>The PUREX technology based on aqueous processes is currently the leading reprocessing technology in nuclear energy systems. It seems to be the most developed and established process for light water reactor fuel and the use of solid fuel. However, demand driven development of the nuclear system opens the way to liquid fuelled reactors, and disruptive technology development through the application of an integrated fuel cycle with a direct link to reactor operation. The possibilities of this new concept for innovative reprocessing technology development are analysed, the boundary conditions are discussed, and the economic as well as the neutron physical optimization parameters of the process are elucidated. Reactor physical knowledge of the influence of different elements on the neutron economy of the reactor is required. Using an innovative study approach, an element priority list for the salt clean-up is developed, which indicates that separation of Neodymium and Caesium is desirable, as they contribute almost 50% to the loss of criticality. Separating Zirconium and Samarium in addition from the fuel salt would remove nearly 80% of the loss of criticality due to fission products. The theoretical study is followed by a qualitative discussion of the different, demand driven optimization strategies which could satisfy the conflicting interests of sustainable reactor operation, efficient chemical processing for the salt clean-up, and the related economic as well as chemical engineering consequences. A new, innovative approach of balancing the throughput through salt processing based on a low number of separation process steps is developed. Next steps for the development of an economically viable salt clean-up process are identified.</p></div

Summary report on Workshop 1 laypersons’ perceptions of marine CDR, Deliverable 3.1

This deliverable reports about the successful completion of three group discussions on marine carbon dioxide removal (CDR) with laypersons in Germany. The 2-hour group discussions were held online. 5 participants discussed these three topics: (1) the environmental state of the oceans, (2) four selected marine CDR approaches, and (3) responsible research and innovation. The four approaches were ocean fertilization, ocean alkalinization via ocean liming and electrochemical weathering in desalination plants, artificial upwelling, and blue carbon management via kelp forests, mangroves and seagrass meadows

Implementation of TALIF on negative ion sources for the determination of the properties of atomic hydrogen

The production of negative hydrogen ions in a negative ion source used for future neutral beam injection systems in nuclear fusion is closely tied to the density and energy distribution function (EDF) of neutral hydrogen atoms in the plasma in negative ion sources. In this work, a diagnostic (two-photon absorption laser induced fluorescence, TALIF) is installed at a negative ion source which can determine both density and EDF of H atoms in the relevant plasma region. In an intermediate step, TALIF is set up at a reference plasma source for characterization and first measurements. The gained knowledge is then used for the installation of TALIF at the negative ion source at the test stand BATMAN Upgrade where the very first TALIF measurements at such an ion source are performed

Bewegungsdekodierung für elektrophysiologisch gestützte intelligente adaptive tiefen Hirnstimulation bei der Parkinson-Krankheit

Deep Brain stimulation is an effective treatment for movement disorders such as Parkinson’s disease or essential tremor. Current therapy protocols do not adjust in real-time to the present need for treatment but instead rely on constant stimulation parameters. A novel concept called intelligent adaptive deep brain stimulation triggers stimulation based on decoding of a predefined state, such as movement, in a demand-driven way. Invasive Brain Computer Interfaces were previously presented for decoding behavioral states both using local field potential recordings from depth electrodes, primarily in movement disorder patients, and using electrocorticographic signals in epilepsy patients. Future brain implants may successfully treat different movement disorders using both modalities. A systematic brain signal decoding comparison of the two recording sites within patients was lacking. In this work, we analyzed invasive intraoperative recordings from Parkinson’s disease patients undergoing deep brain stimulation therapy. Subthalamic local field potentials and simultaneous electrocorticographic signals were recorded while the patients were performing a hand-gripping force task. We used these signals to develop a real-time-enabled feature estimation and decoding framework and investigated different hyperparameter-optimized machine learning approaches for the prediction of movement strength. We identified optimal temporal, spatial, and oscillatory decoding components. Our analysis showed for the first time that movement decoding performances of cortical recordings were superior to subcortical ones using different machine learning methods. We found that gradient-boosted decision trees showed the best performances for electrocorticographic recordings, while Wiener filters were optimal for subthalamic signals. Models from single electrode contacts were better performing than methods that combine data from multiple contacts. Decoding performances were negatively correlated to Parkinson's disease-specific symptom scores. Previously, subthalamic beta oscillations were reported to reflect Parkinson’s disease symptom severity, here we found that decoding performances were negatively correlated to elevated subthalamic beta oscillations. Additionally, we developed a movement decoding network that predicted contact-specific movement decoding performances using functional and structural connectivity profiles. In conclusion, we propose a computational framework based on invasive neurophysiology for brain signal decoding and highlight interactions of decoding performances with Parkinson’s disease symptom states, pathological symptom biomarkers, and whole-brain connectivity. This thesis, therefore, constitutes a significant contribution to the development of intelligent personalized medicine for adaptive deep brain stimulation.Tiefe Hirnstimulation ist eine effektive Behandlung von Bewegungsstörungen wie bei der Parkinson-Krankheit oder dem Essentiellen Tremor. Derzeitige Protokolle passen sich nicht in Echtzeit dem aktuellen Behandlungsbedarf an, sondern beruhen auf konstanten Stimulationsparametern. In einem neuen Therapieverfahren, der „intelligenten adaptiven tiefen Hirnstimulation“, wird die Stimulation bedarfsgerecht anhand eines vordefinierten Zustands, wie beispielsweise der Bewegung, angepasst. Invasive Brain Computer Interfaces konnten in vorigen Studien Verhaltenszustände mit elektrophysiologischen Aufnahmen dekodieren. Hier wurden entweder lokale Feldpotentiale, abgeleitet von Elektroden in tiefen Hirnregionen bei Patient*innen mit Bewegungsstörungen, oder elektrokortikographische Signale, bei Epilepsie-Patient*innen, verwendet. Beide Signal-Modalitäten könnten für zukünftige Hirnimplantate genutzt werden. Ein systematischer Vergleich der jeweiligen Dekodierleistung wurde bei denselben Patient*innen bisher nicht durchgeführt. Hier analysierten wir deshalb intraoperative Aufzeichnungen subthalamischer lokaler Feldpotentiale und gleichzeitige elektrokortikographische Ableitungen von Parkinson-Patient*innen während der Implantation des tiefen Hirnstimulators. Die Patient*innen führten Handbewegungen mit unterschiedlicher Greifkraft aus. Mittels echtzeitfähiger Feature Berechnung und Dekodierung untersuchten wir verschiedene Hyperparameter-optimierte maschinelle Lernverfahren zur Vorhersage der Bewegungsstärke. Wir identifizierten optimale temporale, oszillatorische und lokalisationsspezifische Parameter der Dekodierung. Unsere Studie zeigt zum ersten Mal, dass die Dekodierleistung von kortikalen gegenüber subkortikalen Signalen anhand von verschiedenen maschinellen Lernmethoden deutlich überlegen war. Gradient-boosted decision trees waren für elektrokortikographische Aufzeich-nungen die beste Dekodiermethode, während Wiener Filter für subthalamische Signale am geeignetsten waren. Modelle aus einzelnen Elektrodenkontakten zeigten bessere Dekodierleistungen als Modelle die Daten mehrerer Kontakte kombinierten. Die Dekodierleistung korrelierte negativ mit der Parkinson-Symptomschwere, und korrelierte zusätzlich negativ mit erhöhten subthalamischen Beta-Oszillationen, von denen bereits berichtet wurde, dass sie den Parkinson-Schweregrad widerspiegeln. Zusätzlich entwickelten wir ein Netzwerk für die Vorhersage der kontaktspezifischen Dekodierleistungen anhand von funktionellen und strukturellen Konnektivitätsprofilen. Zusammenfassend stellen wir ein computerbasiertes, neurophysiologisches Framework für die invasive Hirnsignal-Dekodierung vor. Wechselwirkungen der Dekodierleistung wurden mit der Parkinson-Symptomschwere, elektrophysiologischen Biomarkern pathologischer Symptome und der Konnektivität des gesamten Gehirns identifiziert. Diese Dissertation unterstützt daher die Entwicklung intelligenter, personalisierter Medizin für die adaptive tiefe Hirnstimulation