Automated maneuver planning using a fuzzy logic algorithm

Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool

Geostationary Operational Environmental Satellite (GOES)-8 mission flight experience

The Geostationary Operational Environmental Satellite (GOES)-8 spacecraft was launched on April 13, 1994, at 06:04:02 coordinated universal time (UTC), with separation from the Atlas-Centaur launch vehicle occurring at 06:33:05 UTC. The launch was followed by a series of complex, intense operations to maneuver the spacecraft into its geosynchronous mission orbit. The Flight Dynamics Facility (FDF) of the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) was responsible for GOES-8 attitude, orbit maneuver, orbit determination, and station acquisition support during the ascent phase. This paper summarizes the efforts of the FDF support teams and highlights some of the unique challenges the launch team faced during critical GOES-8 mission support. FDF operations experience discussed includes: (1) The abort of apogee maneuver firing-1 (AMF-1), cancellation of AMF-3, and the subsequent replans of the maneuver profile; (2) The unexpectedly large temperature dependence of the digital integrating rate assembly (DIRA) and its effect on GOES-8 attitude targeting in support of perigee raising maneuvers; (3) The significant effect of attitude control thrusting on GOES-8 orbit determination solutions; (4) Adjustment of the trim tab to minimize torque due to solar radiation pressure; and (5) Postlaunch analysis performed to estimate the GOES-8 separation attitude. The paper also discusses some key FDF GOES-8 lessons learned to be considered for the GOES-J launch which is currently scheduled for May 19, 1995

Systematic pathological component scores for skin-containing vascularized composite allografts

Clinical management of skin-containing vascularized composite allografts (VCA) requires accurate assessment of the graft status, typically based on skin biopsies. The Banff 2007 Working Classification proposed 4 grades of acute rejection, but did not score individual features or include vascular rejection. Here we report a systematic scoring system developed from MHC-mismatched porcine skin-containing VCA. Biopsies from 20 VCA, 9 autologous skin flaps and 9 normal skin were analyzed to optimize the methodology and set thresholds. The components quantified were: perivascular cells/dermal vessel (pc), perivascular dermal infiltrate area (pa), luminal leukocytes/capillary or venule (c), epidermal infiltrate (ei), epidermal apoptosis or necrosis (e), endarteritis (v), and chronic allograft vasculopathy (cav). To evaluate prognostic value, we scored a separate group of 28 serial biopsies from 8 recipients (4 that were ultimately accepted and 4 that rejected. Parameters on the initial biopsies predicting later graft rejection included pc (p < 0.02), pa (p < 0.03), ei (p < 0.0005), e (p < 0.003) and c (p < 0.005). Reproducibility between 2 pathologists blinded to clinical data was acceptable, with weighted kappa scores for pc (0.673), pa (0.399), ei (0.464), e (0.663), v (0.766), and c (0.642). This component scoring system can be adapted clinically, since human and porcine skin are highly similar. Vascular lesions in VCA are also highlighted in this system and could impact graft outcome. The component score approach complements Banff 2007 grades and will enable the establishment of clinically significant thresholds

Defective temporal processing of sensory stimuli in DYT1 mutation carriers: a new endophenotype of dystonia?

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Fecal microbiota transplant rescues mice from human pathogen mediated sepsis by restoring systemic immunity.

Death due to sepsis remains a persistent threat to critically ill patients confined to the intensive care unit and is characterized by colonization with multi-drug-resistant healthcare-associated pathogens. Here we report that sepsis in mice caused by a defined four-member pathogen community isolated from a patient with lethal sepsis is associated with the systemic suppression of key elements of the host transcriptome required for pathogen clearance and decreased butyrate expression. More specifically, these pathogens directly suppress interferon regulatory factor 3. Fecal microbiota transplant (FMT) reverses the course of otherwise lethal sepsis by enhancing pathogen clearance via the restoration of host immunity in an interferon regulatory factor 3-dependent manner. This protective effect is linked to the expansion of butyrate-producing Bacteroidetes. Taken together these results suggest that fecal microbiota transplantation may be a treatment option in sepsis associated with immunosuppression

The Pain in Dystonia Scale (PIDS)—Development and Validation in Cervical Dystonia

BACKGROUND: A better understanding of pain in adult-onset idiopathic dystonia (AOID) is needed to implement effective therapeutic strategies. OBJECTIVE: To develop a new rating instrument for pain in AOID and validate it in cervical dystonia (CD). METHODS: Development and validation of the Pain in Dystonia Scale (PIDS) comprised three phases. In phase 1, international experts and participants with AOID generated and evaluated the preliminary items for content validity. In phase 2, the PIDS was drafted and revised by the experts, followed by cognitive interviews to ensure self-administration suitability. In phase 3, the PIDS psychometric properties were assessed in 85 participants with CD and retested in 40 participants. RESULTS: The final version of PIDS evaluates pain severity (by body-part), functional impact, and external modulating factors. Test-retest reliability showed a high-correlation coefficient for the total score (0.9, P < 0.001), and intraclass correlation coefficients were 0.7 or higher for all items in all body-parts subscores. The overall PIDS severity score showed high internal consistency (Cronbach's α, 0.9). Convergent validity analysis revealed a strong correlation between the PIDS severity score and the Toronto Western Spasmodic Torticollis Rating Scale pain subscale (0.8, P < 0.001) and the Brief Pain Inventory-short form items related to pain at time of the assessment (0.7, P < 0.001) and impact of pain on daily functioning (0.7, P < 0.001). CONCLUSION: The PIDS is the first specific questionnaire developed to evaluate pain in all patients with AOID, here, demonstrating high-level psychometric properties in people with CD. Future work will validate PIDS in other forms of AOID. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Designing Playful Games and Applications to Support Science Centers Learning Activities

In recent years there has been a renewed interest on science, technology, engineering, and mathematics (STEM) education. Following this interest, science centers\u27 staff started providing technology enhanced informal STEM education experiences. The use of well-designed mobile and ubiquitous forms of technology to enrich informal STEM education activities is an essential success factor. The goal of our research is to investigate how technology applications can be better used and developed for taking full advantage of the opportunities and challenges they provide for students learning about STEM concepts. In our approach, we have conducted a series of interviews with experts from science center curating and outdoor learning activities development, with the final goal of exploring and improving current learning environments and practices. This paper presents the development of set of design considerations for the development of STEM games and applications of young students. An initial set of best practices was first developed through semi-structures interviews with experts; and afterwards, by employing content analysis, a revised set of considerations was obtained. These results are useful for STEM education teachers, curriculum designers, curators and developers for K-12 education environments

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes, and scales linearly with the input size.Comment: Source code is available at: https://github.com/benedekrozemberczki/FEATHE