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Abstract images with high self-similarity could be used for drug-free stress therapy. This based on the fact that a complex visual environment has a high affective appraisal. To create such an image we can use the setup based on the three laser sources of small power and different colors (Red, Green, Blue), the image is the pattern resulting from the reflecting and refracting by the complicated form object placed into the laser ray paths. The images were obtained experimentally which showed the good therapy effect. However, to find and to choose the object which gives needed image structure is very difficult and requires many trials. The goal of the work is to develop a method and a procedure of finding the object form which if placed into the ray paths can provide the necessary structure of the image In fact the task means obtaining the necessary irradiance distribution on the given surface. Traditionally such problems are solved using the non-imaging optics methods. In the given case this task is very complicated because of the complicated structure of the illuminance distribution and its high non-linearity. Alternative way is to use the projected image of a mask with a given structure. We consider both ways and discuss how they can help to speed up the synthesis procedure for the given abstract image of the high self-similarity for the setups of drug-free therapy.
Groundwater is the world's largest accessible freshwater resource and is of critical importance for irrigation, and thus for global food security. For regions with high demands, groundwater abstractions often exceed recharge and persistent groundwater depletion occurs. The direct effects of depletion are falling groundwater levels, increased pumping costs, land subsidence, and reduced baseflows to rivers. Water demands are expected to increase further due to growing population, economic development, and climate change, posing the urgent question how sustainable current water abstractions are worldwide and where and when these abstractions approach conceivable economic and environmental limits. In this study we estimated trends over 1960-2100 in groundwater levels, resulting from changes in demand and climate. We explored the limits of groundwater abstraction by predicting where and when groundwater levels drop that deep that groundwater gets unattainable for abstraction (economic limit) or, that groundwater baseflows to rivers drop below environmental requirements (environmental limit). We used a global hydrological model coupled to a groundwater model, meaning lateral groundwater flows, river infiltration and drainage, and infiltration and capillary-rise are simulated dynamically. Historical data and projections are used to prescribe water demands and climate forcing to the model. For the near future we used RCP8.5 and applied globally driest, average, and wettest GCM to test climate sensitivity. Results show that in general environmental limits are reached before economic limits, for example starting as early as the 1970s compared to the 1980s for economic limits in the upper Ganges basin. Economic limits are mostly related to regions with depletion, while environmental limits are reached also in regions were groundwater and surface water withdrawals are significant but depletion is not taking place (yet), for example in Spain and Portugal. In the near future
Hydrogen Energy is a continuing bibliographic summary with abstracts of research and projections on the subject of hydrogen as a secondary fuel and as an energy carrier. This update to Hydrogen Energy cites additional references identified during the fourth quarter of 1978. It is the fourth in a 1978 quarterly series intended to provide current awareness to those interested in hydrogen energy. A series of cross indexes are included which track directly with those of the cumulative volume.
Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW's web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.
The general lack of knowledge about the current rates of water abstraction/use is a challenge to sustainable water resources management in many countries, including Uganda. Estimates of water abstraction/use rates over Uganda, currently available from the FAO are not disaggregated according to source, making it difficult to understand how much is taken out of individual water stores, limiting effective management. Modelling efforts have disaggregated water use rates according to source (i.e. groundwater and surface water). However, over Sub-Saharan Africa countries, these model use estimates are highly uncertain given the scale limitations in applying water use (i.e. point versus regional), thus influencing model calibration/validation. In this study, we utilize data from the water supply atlas project over Uganda to estimate current rates of groundwater abstraction across the country based on location, well type and other relevant information. GIS techniques are employed to demarcate areas served by each water source. These areas are combined with past population distributions and average daily water needed per person to estimate water abstraction/use through time. The results indicate an increase in groundwater use, and isolate regions prone to groundwater depletion where improved management is required to sustainably management groundwater use.
A project scheduling problem consists of a finite set of jobs, each with fixed integer duration, requiring one or more resources such as personnel or equipment, and each subject to a set of precedence relations, which specify allowable job orderings, and a set of mutual exclusion relations, which specify jobs that cannot overlap. No job can be interrupted once started. The objective is to minimize project duration. This objective arises in nearly every large construction project--from software to hardware to buildings. Because such project scheduling problems are NP-hard, they are typically solved by branch-and-bound algorithms. In these algorithms, lower-bound duration estimates (admissible heuristics) are used to improve efficiency. One way to obtain an admissible heuristic is to remove (abstract) all resources and mutual exclusion constraints and then obtain the minimal project duration for the abstracted problem; this minimal duration is the admissible heuristic. Although such abstracted problems can be solved efficiently, they yield inaccurate admissible heuristics precisely because those constraints that are central to solving the original problem are abstracted. This paper describes a method to reconstitute the abstracted constraints back into the solution to the abstracted problem while maintaining efficiency, thereby generating better admissible heuristics. Our results suggest that reconstitution can make good admissible heuristics even better.
Cardiopulmonary resuscitation (CPR) process measures research and quality assurance has traditionally been limited to the first 5 minutes of resuscitation due to significant costs in time, resources, and personnel from manual data abstraction. CPR performance may change over time during prolonged resuscitations, which represents a significant knowledge gap. Moreover, currently available commercial software output of CPR process measures are difficult to analyze. The objective was to develop and validate a software program to help automate the abstraction and transfer of CPR process measures data from electronic defibrillators for complete episodes of cardiac arrest resuscitation. We developed a software program to facilitate and help automate CPR data abstraction and transfer from electronic defibrillators for entire resuscitation episodes. Using an intermediary Extensible Markup Language export file, the automated software transfers CPR process measures data (electrocardiogram [ECG] number, CPR start time, number of ventilations, number of chest compressions, compression rate per minute, compression depth per minute, compression fraction, and end-tidal CO 2 per minute). We performed an internal validation of the software program on 50 randomly selected cardiac arrest cases with resuscitation durations between 15 and 60 minutes. CPR process measures were manually abstracted and transferred independently by two trained data abstractors and by the automated software program, followed by manual interpretation of raw ECG tracings, treatment interventions, and patient events. Error rates and the time needed for data abstraction, transfer, and interpretation were measured for both manual and automated methods, compared to an additional independent reviewer. A total of 9,826 data points were each abstracted by the two abstractors and by the software program. Manual data abstraction resulted in a total of six errors (0.06%) compared to zero errors by the software program 153554b96e