Topology Data Analysis Guided Prompt Optimization of Segment Anything Model for Zero-Shot Segmentation of Biological Images
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Wavefront Compensation Segmented Mirror Sensing and Control
The primary mirror of very large submillimeter-wave telescopes will necessarily be segmented into many separate mirror panels. These panels must be continuously co-phased to keep the telescope wavefront error less than a small fraction of a wavelength, to ten microns RMS (root mean square) or less. This performance must be maintained continuously across the full aperture of the telescope, in all pointing conditions, and in a variable thermal environment. A wavefront compensation segmented mirror sensing and control system, consisting of optical edge sensors, Wavefront Compensation Estimator/Controller Soft ware, and segment position actuators is proposed. Optical edge sensors are placed two per each segment-to-segment edge to continuously measure changes in segment state. Segment position actuators (three per segment) are used to move the panels. A computer control system uses the edge sensor measurements to estimate the state of all of the segments and to predict the wavefront error; segment actuator commands are computed that minimize the wavefront error. Translational or rotational motions of one segment relative to the other cause lateral displacement of the light beam, which is measured by the imaging sensor. For high accuracy, the collimator uses a shaped mask, such as one or more slits, so that the light beam forms a pattern on the sensor that permits sensing accuracy of better than 0.1 micron in two axes: in the z or local surface normal direction, and in the y direction parallel to the mirror surface and perpendicular to the beam direction. Using a co-aligned pair of sensors, with the location of the detector and collimated light source interchanged, four degrees of freedom can be sensed: transverse x and y displacements, as well as two bending angles (pitch and yaw). In this approach, each optical edge sensor head has a collimator and an imager, placing one sensor head on each side of a segment gap, with two parallel light beams crossing the gap. Two sets of optical edge sensors are used per segment-to-segment edge, separated by a finite distance along the segment edge, for four optical heads, each with an imager and a collimator. By orienting the beam direction of one edge sensor pair to be +45 away from the segment edge direction, and the other sensor pair to be oriented -45 away from the segment edge direction, all six degrees of freedom of relative motion between the segments can be measured with some redundancy. The software resides in a computer that receives each of the optical edge sensor signals, as well as telescope pointing commands. It feeds back the edge sensor signals to keep the primary mirror figure within specification. It uses a feed-forward control to compensate for global effects such as decollimation of the primary and secondary mirrors due to gravity sag as the telescope pointing changes to track science objects. Three segment position actuators will be provided per segment to enable controlled motions in the piston, tip, and tilt degrees of freedom. These actuators are driven by the software, providing the optical changes needed to keep the telescope phased.
Redding, David C.↗
Development and validation of an automatic method to detect the latest contracting viable left ventricular segments to assist guide CRT therapy from gated SPECT myocardial perfusion imaging
Objectives: The purpose of this study is to use ECG-gated SPECT MPI to detect the latest contracting viable left ventricular (LV) segments to help guide the LV probe placement used in CRT therapy and to validate segment selection against the visual integration method by experts. Methods: For each patient, the resting ECG-gated SPECT MPI short-axis images were sampled in 3D to generate a polar map of the perfusion distribution used to determine LV myocardial viability, and to measure LV synchronicity using our phase analysis tool. In the visual integration method, two experts visually interpreted the LV viability and mechanical dyssynchrony from the short-axis images and polar maps of viability and phase, to determine the latest contracting viable segments using the 17-segment model. In the automatic method, the apical segments, septal segments, and segments with more than 50% scar were excluded as these are not candidates for CRT LV probe placement. Amongst the remaining viable segments, the segments, whose phase angles were within 10° of the latest phase angle (the most delayed contracting segment), were identified for potential CRT LV probe placement and ranked based on the phase angles of the segments. Both methods were tested in 36 pre-CRT patients who underwent ECG-gated SPECT MPI. The accuracy was determined as the percent agreement between the visual integration and automatic methods. The automatic method was performed by a second independent operator to evaluate the inter-operator processing reproducibility. Results: In all the 36 patients, the LV lead positions of the 1st choices recommended by the automatic and visual integration methods were in the same segments in 35 patients, which achieved an agreement rate of 97.2%. In the inter-operator reproducibility test, the LV lead positions of the 1st choices recommended by the two operators were in the same segments in 25 patients, and were in the adjacent segments in 7 patients, which achieved an overall agreement of 88.8%. Conclusions: An automatic method has been developed to detect the latest contracting viable LV segments to help guide the LV probe placement used in CRT therapy. The retrospective clinical study with 36 patients suggests that this method has high agreement against the visual integration method by experts and good inter-operator reproducibility. Consequently, this method is promising to be a clinical tool to recommend the CRT LV lead positions.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Progress Update on Segmentation of Neutron Tomographic Images, 2018
The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Ground Segment Operations Concept for the Orion Artemis-2 Optical Communications System
The ACCESS Project (formerly Space Network) will implement an optical communications ground segment to support the Orion Artemis II Optical Communications (O2O) demonstration as part of the next manned human spaceflight mission to the moon, Artemis II. O2O implements laser communication (lasercomm) technology for operational use on the Orion series of spacecraft, as a development test objective (DTO), in order to demonstrate the feasibility and operational utility of lasercomm for human spaceflight missions. O2O consists of three segments: Space Segment, Ground Segment, and Operations Segment. The Space Segment consists of the Space Terminal Element and the Orion spacecraft. The Space Terminal Element effort is managed by the GSFC Laser-Enhanced Mission Communications Navigation and Operational Services (LEMNOS) project in collaboration with MIT Lincoln Laboratory. The Ground Segment consists of an optical ground terminal (GT) at the White Sands Complex (WSC), which is being developed in collaboration with MIT Lincoln Laboratory, the Ground Segment Operations and Analysis (GSOA) element and Ground Data Element (GDE), and a second optical GT in the Optical Communications Telescope Laboratory (OCTL) at the JPL Table Mountain Facility. The Operations Segment consists of the Artemis II Mission Control Center (MCC), the Lasercomm Space Terminal Console (LSTC), and the Lasercomm Link Planning & Analysis Center (LPAC), all located at the Johnson Space Center (JSC). O2O utilizes pulse-position modulation (PPM) direct-to-earth services resulting in an 80 Mbps downlink data rate from lunar orbit. The O2O concept of operations is to provide optical services for a minimum of 1 hour per day for each day of the Artemis II mission. O2O will utilize a 10-20 Mbps uplink data rate and 40-260 Mbps downlink data rate, depending on the Artemis II mission phase. The ACCESS project will also provide a centralized mission data interface for user data distribution and storage to the MCC and perform planning and scheduling of services in coordination with the Operations Segment for the O2O Ground Segment. The O2O Ground Segment will support the following O2O mission phases: Pre-Mission Planning; Daily Operations Planning; Event Execution; and Post-Pass Reporting. O2O will be used to exchange data files between Orion and the MCC and to distribute real-time video through the optical downlink service to the MCC; which would not be possible without the high-bandwidth link that O2O will provide to Orion. In this paper, I will discuss the O2O Ground Segment development approach and how it will support these critical O2O functions: plan and schedule the contact; acquire and track the optical link; flow information bidirectionally; distribute information; and control and accommodate the system.
optical communications↗
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
quantum machine learning↗
Ambient occlusion – A powerful algorithm to segment shell and skeletal intrapores in computed tomography data
During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region.Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably.Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that at were superior to or impossible to obtain with commonly used distance-map-based segmentations. The combined usage of various AO-derived fields by supervisd or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.
Computed tomography, Micro-computed tomography, Po↗
Coherent phasing of segmented mirrors
The technical issues associated with a coherently phased segmented mirror can be divided into two types. The first involves the issues of manufacturing the surface quality of the mirror segments themselves (coherent phasing of individual segments). The second involves assembly issues of initializing in 1 G and retaining in 0 G an aggregate segmented mirror (coherent phasing between individual segments). Using a rectangular coordinate system at the vertex of a mirror segment, the rigid body motions are the six translational and rotational degrees of freedom. A first cut primary mirror wavefront error budget is presented. For a minimum operational wavelength of 30 micrometers, the derived values from the budget are: segment surface quality (0.45 micrometers RMS), radius mismatch (50 PPM), segment piston error (1.3 micrometers, segment tip/tilt error (0.6 microrad). Either active or passive segmented mirrors can be addressed, but if the surface quality of the off-axis segment and the radius matching requirements can be passively met, then only segment alignment need be sensed and controlled during operation in orbit. For the active mirror case, dimensional stability of the mirror material during operation is a key factor in establishing the degree of active figure control required.
Jones, Peter A.↗
Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data
The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.
quantum machine learning↗
Space Adaptation of Active Mirror Segment Concepts
This report summarizes the results of a three year effort by Blue Line Engineering Co. to advance the state of segmented mirror systems in several separate but related areas. The initial set of tasks were designed to address the issues of system level architecture, digital processing system, cluster level support structures, and advanced mirror fabrication concepts. Later in the project new tasks were added to provide support to the existing segmented mirror testbed at Marshall Space Flight Center (MSFC) in the form of upgrades to the 36 subaperture wavefront sensor. Still later, tasks were added to build and install a new system processor based on the results of the new system architecture. The project was successful in achieving a number of important results. These include the following most notable accomplishments: 1) The creation of a new modular digital processing system that is extremely capable and may be applied to a wide range of segmented mirror systems as well as many classes of Multiple Input Multiple Output (MIMO) control systems such as active structures or industrial automation. 2) A new graphical user interface was created for operation of segmented mirror systems. 3) The development of a high bit rate serial data loop that permits bi-directional flow of data to and from as many as 39 segments daisy-chained to form a single cluster of segments. 4) Upgrade of the 36 subaperture Hartmann type Wave Front Sensor (WFS) of the Phased Array Mirror, Extendible Large Aperture (PAMELA) testbed at MSFC resulting in a 40 to 5OX improvement in SNR which in turn enabled NASA personnel to achieve many significant strides in improved closed-loop system operation in 1998. 5) A new system level processor was built and delivered to MSFC for use with the PAMELA testbed. This new system featured a new graphical user interface to replace the obsolete and non-supported menu system originally delivered with the PAMELA system. The hardware featured Blue Line's new stackable processing system which included fiber optic data links, a WFS digital interface, and a very compact and reliable electronics package. The project also resulted in substantial advances in the evolution of concepts for integrated structures to be used to support clusters of segments while also serving as the means to distribute power, timing, and data communications resources. A prototype cluster base was built and delivered that would support a small array of 7 cm mirror segments. Another conceptual design effort led to substantial progress in the area of laminated silicon mirror segments. While finished mirrors were never successfully produced in this exploratory effort, the basic feasibility of the concept was established through a significant amount of experimental development in microelectronics processing laboratories at the University of Colorado in Colorado Springs. Ultimately lightweighted aluminum mirrors with replicated front surfaces were produced and delivered as part of a separate contract to develop integrated segmented mirror assemblies. Overall the project was very successful in advancing segmented mirror system architectures on several fronts. In fact, the results of this work have already served as the basic foundation for the system architectures of several projects proposed by Blue Line for different missions and customers. These include the NMSD and AMSD procurements for NASA's Next Generation Space Telescope, the HET figure maintenance system, and the 1 meter FAST telescope project.
Ames, Gregory H.↗
Method of Adjusting Acoustic Impedances for Impedance-Tunable Acoustic Segments
A method is provided for making localized decisions and taking localized actions to achieve a global solution. In an embodiment of the present invention, acoustic impedances for impedance-tunable acoustic segments are adjusted. A first acoustic segment through an N-th acoustic segment are defined. To start the process, the first acoustic segment is designated as a leader and a noise-reducing impedance is determined therefor. This is accomplished using (i) one or more metrics associated with the acoustic wave at the leader, and (ii) the metric(s) associated with the acoustic wave at the N-th acoustic segment. The leader, the N-th acoustic segment, and each of the acoustic segments exclusive of the leader and the N-th acoustic segment, are tuned to the noise-reducing impedance. The current leader is then excluded from subsequent processing steps. The designation of leader is then given one of the remaining acoustic segments, and the process is repeated for each of the acoustic segments through an (N-1)-th one of the acoustic segments.
Jones, Kennie H↗
Intradomain phase transitions in flexible block copolymers with self-aligning segments
We study a model of flexible block copolymers (BCPs) in which there is an enlthalpic preference for orientational order, or local alignment, among like-block segments. We describe a generalization of the self-consistent field theory of flexible BCPs to include inter-segment orientational interactions via a Landau-de Gennes free energy associated with a polar or nematic order parameter for segments of one component of a diblock copolymer. We study the equilibrium states of this model numerically, using a pseudo-spectral approach to solve for chain conformation statistics in the presence of a self-consistent torque generated by inter-segment alignment forces. Applying this theory to the structure of lamellar domains composed of symmetric diblocks possessing a single block of “self-aligning” polar segments, we show the emergence of spatially complex segment order parameters (segment director fields) within a given lamellar domain. Because BCP phase separation gives rise to spatially inhomogeneous orientation order of segments even in the absence of explicit intra-segment aligning forces, the director fields of BCPs, as well as thermodynamics of lamellar domain formation, exhibit a highly non-linear dependence on both the inter-block segregation ( χ N ) and the enthalpy of alignment (ε). Specifically, we predict the stability of new phases of lamellar order in which distinct regions of alignment coexist within the single mesodomain and spontaneously break the symmetries of the lamella (or smectic) pattern of composition in the melt via in-plane tilt of the director in the centers of the like-composition domains. We further show that, in analogy to Freedericksz transition confined nematics, the elastic costs to reorient segments within the domain, as described by the Frank elasticity of the director, increase the threshold value ε needed to induce this intra-domain phase transition .
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
RSRM Segment Train Derailment and Recovery
On May 2, 2007, a freight train carrying segments of the space shuttle's solid rocket boosters derailed in Myrtlewood, Alabama, after a rail trestle collapsed. The train was carrying Reusable Solid Rocket Motors (RSRM) 98 center and forward segments (STS-120) and RSRM 99 aft segments (STS-122). Initially, it was not known if the segments had been seriously damaged. Four segments dropped approximately 10 feet when the trestle collapsed and one of those four rolled off the track onto its side. The exit cones and the other four segments, not yet on the trestle, remained on solid ground. ATK and NASA immediately dispatched an investigation and recovery team to determine the safety of the situation and eventually the usability of the segments and exit cones for flight. Instrumentation on each segment provided invaluable data to determine the acceleration loads imparted into each loaded segment and exit cone. This paper details the incident, recovery plan and the team work that created a success story that ended with the safe launch of STS120 using the four center segments and the launch of STS122 using the Aft exit cones assemblies.
Taylor Jr., Robert H.↗
Dynamic Pupil Masking for Phasing Telescope Mirror Segments
A method that would notably include dynamic pupil masking has been proposed as an enhanced version of a prior method of phasing the segments of a primary telescope mirror. The method would apply, more specifically, to a primary telescope mirror that comprises multiple segments mounted on actuators that can be used to tilt the segments and translate them along the nominal optical axis to affect wavefront control in increments as fine as a fraction of a wavelength of light. An apparatus (see figure) for implementing the proposed method would be denoted a dispersed-fringe-sensor phasing camera system (DPCS). The prior method involves the use of a dispersed-fringe sensor (DFS). The prior method was reported as part of a more comprehensive method in Coarse Alignment of a Segmented Telescope Mirror (NPO-20770), NASA Tech Briefs, Vol. 25, No. 4 (April 2001), page 15a. The pertinent parts of the prior method are the following: The telescope would be aimed at a bright distant point source of light (e.g., a star) and form a broadband image on an imaging detector array placed at the telescope focal plane. The construction and use of a dispersed-fringe sensor would begin with insertion of a grism (a right-angle prism with a transmission grating on the hypotenuse face) into the optical path. With other segments tilted away from the investigating region of the detector, a dispersed-fringe image would be formed by use of a designated reference segment and a selected mirror segment. The modulation period and orientation of the fringe would be analyzed to determine the magnitude and sign of the piston error (displacement along the nominal optical axis) between the two segments. The error would be used to perform a coarse-phase piston adjustment of the affected mirror segment. This determination and removing of piston error is what is meant by phasing as used above. The procedure as described thus far would be repeated until all segments had been phased.
Shi, Fang↗
Segmented face seal assembly and an associated method thereof
A turbomachine and a method of operating the turbomachine are disclosed. The turbomachine includes a stator, a rotor including a rotor bearing face, and a face seal assembly including a first segmented seal ring and a second segmented seal ring. The first segmented seal ring includes a plurality of joints and a first flat-contact surface and the second segmented seal ring includes a plurality of segment ends and a second flat-contact surface. One of the first and second segmented seal rings includes a seal bearing face. The second segmented seal ring is coupled to the first segmented seal ring such that the second flat-contact surface is in contact with the first flat-contact surface. The plurality of segment ends is circumferentially offset from the plurality of joints. The first segmented seal ring is slidably coupled to the stator and defines a face seal clearance between the rotor and seal bearing faces.
42 ENGINEERING↗
Compatibility of segmented thermoelectric generators
It is well known that power generation efficiency improves when materials with appropriate properties are combined either in a cascaded or segmented fashion across a temperature gradient. Past methods for determining materials used in segmentation weremainly concerned with materials that have the highest figure of merit in the temperature range. However, the example of SiGe segmented with Bi2Te3 and/or various skutterudites shows a marked decline in device efficiency even though SiGe has the highest figure of merit in the temperature range. The origin of the incompatibility of SiGe with other thermoelectric materials leads to a general definition of compatibility and intrinsic efficiency. The compatibility factor derived as = (Jl+zr - 1) a~ is a function of only intrinsic material properties and temperature, which is represented by a ratio of current to conduction heat. For maximum efficiency the compatibility factor should not change with temperature both within a single material, and in the segmented leg as a whole. This leads to a measure of compatibility not only between segments, but also within a segment. General temperature trends show that materials are more self compatible at higher temperatures, and segmentation is more difficult across a larger -T. The compatibility factor can be used as a quantitative guide for deciding whether a material is better suited for segmentation orcascading. Analysis of compatibility factors and intrinsic efficiency for optimal segmentation are discussed, with intent to predict optimal material properties, temperature interfaces, and/or currentheat ratios.
Thermoelectric power generation segmented↗
NASA Today - Mars Observer Segment (Part 4 of 6)
This videotape consists of eight segments from the NASA Today News program. The first segment is an announcement that there was no date set for the launch of STS-51, which had been postponed due to mechanical problems. The second segment describes the MidDeck Dynamic Experiment Facility. The third segment is about the scheduled arrival of the Mars Observer at Mars, it shows an image of Mars as seen from the approaching Observer spacecraft, and features an animation of the approach to Mars, including the maneuvers that are planned to put the spacecraft in the desired orbit. The fourth segment describes a discovery from an infrared spectrometer that there is nitrogen ice on Pluto. The fifth segment discusses the Aerospace for Kids (ASK) program at the Goddard Space Flight Center (GSFC). The sixth segment is about the high school and college summer internship programs at GSFC. The seventh segment announces a science symposium being held at Johnson Space Center. The last segment describes the National Air and Space Museum and NASA's cooperation with the Smithsonian Institution.
Source record↗
Spatial scale of motion segmentation from speed cues
For the accurate perception of multiple, potentially overlapping, surfaces or objects, the visual system must distinguish different local motion vectors and selectively integrate similar motion vectors over space to segment the retinal image properly. We recently showed that large differences in speed are required to yield a percept of motion transparency. In the present study, to investigate the spatial scale of motion segmentation from speed cues alone, we measured the speed-segmentation threshold (the minimum speed difference required for 75% performance accuracy) for 'corrugated' random-dot patterns, i.e. patterns in which dots with two different speeds were alternately placed in adjacent bars of variable width. In a first experiment, we found that, at large bar widths, a smaller speed difference was required to segment and perceive the corrugated pattern of moving dots, while at small bar-widths, a larger speed difference was required to segment the two speeds and perceive two transparent surfaces of moving dots. Both the perceptual and segmentation performance transitions occurred at a bar width of around 0.4 degrees. In a second experiment, speed-segmentation thresholds were found to increase sharply when dots with different speeds were paired within a local pooling area. The critical pairing distance was about 0.2 degrees in the fovea and increased linearly with stimulus eccentricity. However, across the range of eccentricities tested (up to 15 degrees ), the critical pairing distance did not change much and remained close to the receptive field size of neurons within the primate primary visual cortex. In a third experiment, increasing dot density changed the relationship between speed-segmentation thresholds and bar width. Thresholds decreased for large bar widths, but increased for small bar widths. All of these results are well fit by a simple stochastic model, which estimates the probabilities of having identical or different motion vectors within a local pooling area whose size is the same as that of primate V1 neurons. Altogether, these results demonstrate that speed-based segmentation can function well, even at small spatial scales (i.e. high-spatial frequencies of spatial corrugation) and thereby emphasizes the critical role of a local pooling process early in the cortical motion-processing pathway.
NASA Discipline Neuroscience↗