Feasibility Study of Hybrid Inverse Planning with Transmission Beams and Single-energy Spread-out Bragg Peaks for Proton Flash Radiotherapy

Ultra-high dose rate (FLASH) proton planning with only transmission beams (TBs) has limitations in normal tissue sparing. The single-energy spread-out Bragg peaks (SESOBPs) of FLASH dose rate have been demonstrated feasible for proton FLASH planning. A hybrid inverse optimization method was developed to combine the TBs and SESOBPs (TB-SESOBP) for FLASH planning. The SESOBPs were generated from spreading out the BPs by pre-designed general bar ridge filters and placed at the central target by range shifters to obtain a uniform dose within the target. The SESOBPs and TBs were fully sampled field-by-field allowing automatic spot selection and weighting in the optimization process. The TB-SESOBP plans were validated in comparison with the TB only (TB-only) plans and the plans with the combination of TBs and BPs (TB-BP) regarding 3D dose and dose rate distributions for five lung cases. Comparing to the TB-only plans, the mean spinal cord D1.2cc drastically reduced 41%, the mean lung V7Gy and V7.4Gy moderately reduced by up to 17% and the target dose homogeneity slightly increased in the TB-SESOBP plans. Comparable dose homogeneity was achieved in both TB-SESOBP and TB-BP plans. Besides, prominent improvements were achieved in lung sparing for the cases of relatively large targets by the TB-SESOBP plans comparing to the TB-BP plans. The targets were fully covered with the FLASH dose rate in all the three plans. For the OARs, V40Gy/s = 100% was achieved by the TB-only plans while V40Gy/s > 85% was obtained by the other two plans. We have demonstrated that the hybrid TB-SESOBP planning was feasible to achieve FLASH dose rate for proton therapy. The hybrid TB-SESOBP planning has great potential in improving OAR sparing while maintaining high target dose homogeneity, and can be potentially implemented for adaptive radiotherapy

Optimization of Turkish Air Force SAR Units’ Forward Deployment Points for a Central Based SAR Force Structure

Many developed countries that have a combatant Air Force and Search & Rescue (SAR) assets designed for their Air Force\u27s SAR service have been struggling with locating SAR units due to limited SAR assets, constrained budgets, logistic-maintenance problems, and high-risk level of military flights. In recent years, the Turkish Air Force (TUAF) has also been researching methods to gather all SAR units into a central base and deploying the needed number of SAR units to defined Deployment Points (DPs). This research applies three location optimization models to determine the optimum locations for TUAF SAR units. The first model, Set Covering Location Problem (SCLP), defines the minimum number of SAR DPs to cover all fighter aircraft training areas (TAs). The second model, Maximal Covering Location Problem (MCLP), aims to obtain maximum coverage with a given SAR DP number and response time. A weighted MCLP models is also applied with TAs risk values obtained by this research to maximize demanded coverage of TAs. Finally the last model, P-Median Location Problem, defines the locations of SAR DPs while obtaining minimum aggregate or average response time. These three models are applied via a Visual Basic for Applications (VBA) & LINGO Optimization Software interface that allows changing each exogenous variable of the models in a flexible way. The primary objective of this research is to provide the information for the required number of SAR units and their locations. The results indicate that the response time definition is as important as the required number of DPs. Additionally; some DP locations are indispensable because they have no alternative in their sectors

Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the ith transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly

Multivariate Calibration for the Development of Vibrational Spectroscopic Methods

Vibrational spectroscopy, namely near infrared (NIR) and Raman spectroscopy, is based on the interaction between the electromagnetic radiation and matter. The technique is sensitive to chemical and physical properties and delivers a wide range of information about the analyzed sample, but in order to extract the information, multivariate calibration of the spectral data is required. The main goal of this work will be to present in detail the available multivariate calibration strategy for development of NIR and Raman spectroscopic methods, which was successfully applied in pharmaceutics

Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms

We present a simple genetic algorithm (sGA), which is developed under Genetic Rule and Classifier Construction Environment (GRaCCE) to solve feature subset selection and weighting problem to have better classification accuracy on k-nearest neighborhood (KNN) algorithm. Our hypotheses are that weighting the features will affect the performance of the KNN algorithm and will cause better classification accuracy rate than that of binary classification. The weighted-sGA algorithm uses real-value chromosomes to find the weights for features and binary-sGA uses integer-value chromosomes to select the subset of features from original feature set. A Repair algorithm is developed for weighted-sGA algorithm to guarantee the feasibility of chromosomes. By feasibility we mean that the sum of values of each gene in a chromosome must be equal to 1. To calculate the fitness values for each chromosome in the population, we use K Nearest Neighbor Algorithm (KNN) as our fitness function. The Euclidean distance from one individual to other individuals is calculated on the d-dimensional feature space to classify an unknown instance. GRaCCE searches for good feature subsets and their associated weights. These feature weights are then multiplied with normalized feature values and these new values are used to calculate the distance between features

The integration of social concerns into electricity power planning : a combined delphi and AHP approach

The increasing acceptance of the principle of sustainable development has been a major driving force towards new approaches to energy planning. This is a complex process involving multiple and conflicting objectives, in which many agents were able to influence decisions. The integration of environmental, social and economic issues in decision making, although fundamental, is not an easy task, and tradeoffsmust be made. The increasing importance of social aspects adds additional complexity to the traditional models that must now deal with variables recognizably difficult to measure in a quantitative scale. This study explores the issue of the social impact, as a fundamental aspect of the electricity planning process, aiming to give a measurable interpretation of the expected social impact of future electricity scenarios. A structured methodology, based on a combination of the Analytic Hierarchy Process and Delphi process, is proposed. The methodology is applied for the social evaluation of future electricity scenarios in Portugal, resulting in the elicitation and assignment of average social impact values for these scenarios. The proposed tool offers guidance to decision makers and presents a clear path to explicitl

A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

Abstract Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.http://deepblue.lib.umich.edu/bitstream/2027.42/112478/1/12918_2013_Article_1166.pd

Advanced spaceborne detection, tracking, and navigation systems study and analysis. volume i- summary

Midcourse guidance, lunar parking and descent orbits, lunar landing phase, lunar ascent, and lunar and earth rendezvou