Certification-Based Process Analysis

Space mission architects are often challenged with knowing which investment in technology infusion will have the highest return. Certification-based analysis (CBA) gives architects and technologists a means to communicate the risks and advantages of infusing technologies at various points in a process. Various alternatives can be compared, and requirements based on supporting streamlining or automation can be derived and levied on candidate technologies. CBA is a technique for analyzing a process and identifying potential areas of improvement. The process and analysis products are used to communicate between technologists and architects. Process means any of the standard representations of a production flow; in this case, any individual steps leading to products, which feed into other steps, until the final product is produced at the end. This sort of process is common for space mission operations, where a set of goals is reduced eventually to a fully vetted command sequence to be sent to the spacecraft. Fully vetting a product is synonymous with certification. For some types of products, this is referred to as verification and validation, and for others it is referred to as checking. Fundamentally, certification is the step in the process where one insures that a product works as intended, and contains no flaws

Planning Coverage Campaigns for Mission Design and Analysis: CLASP for DESDynl

Mission design and analysis presents challenges in that almost all variables are in constant flux, yet the goal is to achieve an acceptable level of performance against a concept of operations, which might also be in flux. To increase responsiveness, automated planning tools are used that allow for the continual modification of spacecraft, ground system, staffing, and concept of operations, while returning metrics that are important to mission evaluation, such as area covered, peak memory usage, and peak data throughput. This approach was applied to the DESDynl mission design using the CLASP planning system, but since this adaptation, many techniques have changed under the hood for CLASP, and the DESDynl mission concept has undergone drastic changes. The software produces mission evaluation products, such as memory highwater marks, coverage percentages, given a mission design in the form of coverage targets, concept of operations, spacecraft parameters, and orbital parameters. It tries to overcome the lack of fidelity and timeliness of mission requirements coverage analysis during mission design. Previous techniques primarily use Excel in ad hoc fashion to approximate key factors in mission performance, often falling victim to overgeneralizations necessary in such an adaptation. The new program allows designers to faithfully represent their mission designs quickly, and get more accurate results just as quickly

Geometric Reasoning for Automated Planning

An important aspect of mission planning for NASA s operation of the International Space Station is the allocation and management of space for supplies and equipment. The Stowage, Configuration Analysis, and Operations Planning teams collaborate to perform the bulk of that planning. A Geometric Reasoning Engine is developed in a way that can be shared by the teams to optimize item placement in the context of crew planning. The ISS crew spends (at the time of this writing) a third or more of their time moving supplies and equipment around. Better logistical support and optimized packing could make a significant impact on operational efficiency of the ISS. Currently, computational geometry and motion planning do not focus specifically on the optimized orientation and placement of 3D objects based on multiple distance and containment preferences and constraints. The software performs reasoning about the manipulation of 3D solid models in order to maximize an objective function based on distance. It optimizes for 3D orientation and placement. Spatial placement optimization is a general problem and can be applied to object packing or asset relocation

Translating MAPGEN to ASPEN for MER

This software translates MAPGEN (Europa and APGEN) domains to ASPEN, and the resulting domain can be used to perform planning for the Mars Exploration Rover (MER). In other words, this is a conversion of two distinct planning languages (both declarative and procedural) to a third (declarative) planning language in order to solve the problem of faithful translation from mixed-domain representations into the ASPEN Modeling Language. The MAPGEN planning system is an example of a hybrid procedural/declarative system where the advantages of each are leveraged to produce an effective planner/scheduler for MER tactical planning. The adaptation of the planning system (ASPEN) was investigated, and, with some translation, much of the procedural knowledge encoding is amenable to declarative knowledge encoding. The approach was to compose translators from the core languages used for adapting MAGPEN, which consists of Europa and APGEN. Europa is a constraint- based planner/scheduler where domains are encoded using a declarative model. APGEN is also constraint-based, in that it tracks constraints on resources and states and other variables. Domains are encoded in both constraints and code snippets that execute according to a forward sweep through the plan. Europa and APGEN communicate to each other using proxy activities in APGEN that represent constraints and/or tokens in Europa. The composition of a translator from Europa to ASPEN was fairly straightforward, as ASPEN is also a declarative planning system, and the specific uses of Europa for the MER domain matched ASPEN s native encoding fairly closely. On the other hand, translating from APGEN to ASPEN was considerably more involved. On the surface, the types of activities and resources one encodes in APGEN appear to match oneto- one to the activities, state variables, and resources in ASPEN. But, when looking into the definitions of how resources are profiled and activities are expanded, one sees code snippets that access various information available during planning for the moment in time being planned to decide at the time what the appropriate profile or expansion is. APGEN is actually a forward (in time) sweeping discrete event simulator, where the model is composed of code snippets that are artfully interleaved by the engine to produce a plan/schedule. To solve this problem, representative code is simulated as a declarative series of task expansions. Predominantly, three types of procedural models were translated: loops, if statements, and code blocks. Loops and if statements were handled using controlled task expansion, and code blocks were handled using constraint networks that maintained the generation of results based on what the order of execution would be for a procedural representation. One advantage with respect to performance for MAPGEN is the use of APGEN s GUI. This GUI is written in C++ and Motif, and performs very well for large plans

Real-Time Projection to Verify Plan Success During Execution

The Mission Data System provides a framework for modeling complex systems in terms of system behaviors and goals that express intent. Complex activity plans can be represented as goal networks that express the coordination of goals on different state variables of the system. Real-time projection extends the ability of this system to verify plan achievability (all goals can be satisfied over the entire plan) into the execution domain so that the system is able to continuously re-verify a plan as it is executed, and as the states of the system change in response to goals and the environment. Previous versions were able to detect and respond to goal violations when they actually occur during execution. This new capability enables the prediction of future goal failures; specifically, goals that were previously found to be achievable but are no longer achievable due to unanticipated faults or environmental conditions. Early detection of such situations enables operators or an autonomous fault response capability to deal with the problem at a point that maximizes the available options. For example, this system has been applied to the problem of managing battery energy on a lunar rover as it is used to explore the Moon. Astronauts drive the rover to waypoints and conduct science observations according to a plan that is scheduled and verified to be achievable with the energy resources available. As the astronauts execute this plan, the system uses this new capability to continuously re-verify the plan as energy is consumed to ensure that the battery will never be depleted below safe levels across the entire plan

Waiting time variation in Early Intervention Psychosis services: longitudinal evidence from the SEPEA naturalistic cohort study

PURPOSE: Early Intervention Psychosis [EIP] services have gained traction internationally, but are currently undergoing various forms of reconfiguration. In England, such services are now mandated to ensure 50% of accepted referrals commence care within 14 days, but no empirical evidence exists. We sought to estimate waiting times to EIP services in a large, representative epidemiological cohort in England, and investigate possible reasons for any variation. METHODS: We estimated median waiting time from referral to acceptance by EIP services and investigated whether this varied by clinical, demographic or neighbourhood-level factors, amongst 798 participants, 16-35 years old, presenting to six EIP services over 3.5 years in a defined catchment area serving 2.5 million people. We used parametric survival analysis to inspect variation in waiting times (in days). RESULTS: Median waiting time was 15 days (interquartile range 7-30), although this varied across services (p < 0.01). Waiting times increased over the case ascertainment period by an average of 4.3 days (95% CI 1.3, 6.2; p < 0.01). Longer waiting times were associated with greater diagnostic uncertainty, indexed by an organic presentation (+ 9.1 days; 95% CI 1.9, 16.6; p < 0.01), polysubstance abuse (+ 2.6; 0.6, 3.9; p < 0.01), absence of psychotic disorder (+1.8; -0.1, 3.0; p = 0.05) and insidious onset (+1.8; -0.1, 3.0; p = 0.06). Waiting times did not vary by most demographic or neighbourhood-level characteristics. CONCLUSIONS: EIP services operate close to new waiting time standards in England, with little systematic variation by sociodemographic position. However, waiting times increased over the study period, coinciding with substantial service reorganisation. Longer waiting times associated with greater diagnostic uncertainty highlight opportunities to reduce delays in certain clinical groups at initial referral.The work for this paper was supported by: a Sir Henry Wellcome Research Fellowship from the Wellcome Trust (Grant No. WT085540) to Dr James Kirkbride, a Sir Henry Dale Fellowship to Dr James Kirkbride, jointly funded by the Wellcome Trust and the Royal Society (Grant No. 101272/Z/13/Z), and an NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) grant for Cambridgeshire and Peterborough (Grant No. RP-PG-0606-1335) to Prof Peter Jones. The funders had no involvement in any aspect of the design of this study, preparation of results, or decision to submit for publication. We thank the Cambridgeshire and Peterborough (CPFT) and Norfolk and Suffolk Foundation Trusts (NSFT) for sponsoring this research

Design and optimization of index-guiding photonic crystal fiber gas sensor

Globalization is becoming an important issue for most businesses in the world. Since globalization changes business trends and shortens product life cycles, it requires companies to be more innovative in developing new ideas, products and processes. Clustering is one of ways to promote innovation by facilitating sharing information and ideas between firms, attracting buyers and suppliers, and providing opportunities for joint training. Many researches in developed countries found that the proximity between companies facilitated collaboration and provided a more conducive environment for R&amp;D and knowledge sharing which can develop culture of entrepreneurship and innovation. Then, the success of clusters in developed countries has led many government and companies to establish new clusters.Since products from China have been dominated Indonesia's market share with lower price, it is very difficult for Indonesian Small and Medium Enterprises to compete with lower price also. Therefore, to face the competition, innovation is perhaps as an alternative strategy for Indonesian SMEs. In facts, more than 50% of small and medium enterprises in Indonesia are located in clusters and most of them are located in Java, Bali and Nusa Tenggara. Even though they located in cluster but their innovations still very low and judging from technology perspective, most of them have low level of technologies and still remain in the underdeveloped stage. Therefore, in this research, the author tries to find (1). To what extend do cluster Indonesia promote innovation, (2). To find the reasons why clusters in Indonesia has not been working well in promoting innovation and (3). To investigate what aspects can be improved by Indonesian SMEs to boost their innovation

The health and cost burden of antibiotic resistant and susceptible Escherichia coli bacteraemia in the English hospital setting: A national retrospective cohort study.

INTRODUCTION: Antibiotic resistance poses a threat to public health and healthcare systems. Escherichia coli causes more bacteraemia episodes in England than any other bacterial species. This study aimed to estimate the burden of E. coli bacteraemia and associated antibiotic resistance in the secondary care setting. MATERIALS AND METHODS: This was a retrospective cohort study, with E. coli bacteraemia as the main exposure of interest. Adult hospital in-patients, admitted to acute NHS hospitals between July 2011 and June 2012 were included. English national surveillance and administrative datasets were utilised. Cox proportional hazard, subdistribution hazard and multistate models were constructed to estimate rate of discharge, rate of in-hospital death and excess length of stay, with a unit bed day cost applied to the latter to estimate cost burden from the healthcare system perspective. RESULTS: 14,042 E. coli bacteraemia and 8,919,284 non-infected inpatient observations were included. E. coli bacteraemia was associated with an increased rate of in-hospital death across all models, with an adjusted subdistribution hazard ratio of 5.88 (95% CI: 5.62-6.15). Resistance was not found to be associated with in-hospital mortality once adjusting for patient and hospital covariates. However, resistance was found to be associated with an increased excess length of stay. This was especially true for third generation cephalosporin (1.58 days excess length of stay, 95% CI: 0.84-2.31) and piperacillin/tazobactam resistance (1.23 days (95% CI: 0.50-1.95)). The annual cost of E. coli bacteraemia was estimated to be £14,346,400 (2012 £), with third-generation cephalosporin resistance associated with excess costs per infection of £420 (95% CI: 220-630). CONCLUSIONS: E. coli bacteraemia places a statistically significant burden on patient health and the hospital sector in England. Resistance to front-line antibiotics increases length of stay; increasing the cost burden of such infections in the secondary care setting

Electric current circuits in astrophysics

Cosmic magnetic structures have in common that they are anchored in a dynamo, that an external driver converts kinetic energy into internal magnetic energy, that this magnetic energy is transported as Poynting fl ux across the magnetically dominated structure, and that the magnetic energy is released in the form of particle acceleration, heating, bulk motion, MHD waves, and radiation. The investigation of the electric current system is particularly illuminating as to the course of events and the physics involved. We demonstrate this for the radio pulsar wind, the solar flare, and terrestrial magnetic storms