Isoscaling and the symmetry energy in spectator fragmentation

Isoscaling and its relation to the symmetry energy in the fragmentation of excited residues produced at relativistic energies were studied in two experiments conducted at the GSI laboratory. The INDRA multidetector has been used to detect and identify light particles and fragments with Z <= 5 in collisions of 12C on 112,124Sn at incident energies of 300 and 600 MeV per nucleon. Isoscaling is observed, and the deduced parameters decrease with increasing centrality. Symmetry term coefficients, deduced within the statistical description of isotopic scaling, are near gamma = 25 MeV for peripheral and gamma < 15 MeV for central collisions. In a very recent experiment with the ALADIN spectrometer, the possibility of using secondary beams for reaction studies at relativistic energies has been explored. Beams of 107Sn, 124Sn, 124La, and 197Au were used to investigate the mass and isospin dependence of projectile fragmentation at 600 MeV per nucleon. The decrease of the isoscaling parameters is confirmed and extended over the full fragmentation regime covered in these reactions.Comment: Proceedings of the IWM2005, Catania, Italy, Nov 200

More heat, less light! The resource curse & HIV/AIDS: A reply to Olivier Sterck

We reported fairly robust results suggesting that resource rich countries did less well containing HIV/AIDS than resource poor states (de Soysa and Gizelis, 2013). We argued that public action to prevent the spread of disease was going to be weaker in resource rich states because rulers would have less incentive to fight disease. Olivier Sterck (this issue) criticizes our study on several grounds, arguing that resource rich states can provide anti-retroviral therapy (ART) and thereby fight the AIDS epidemic. He, however, finds no relationship between resource wealth and HIV/AIDS. We argue that his reanalyses do not fully address the theoretical association between resource wealth and the spread of HIV/AIDS and that his argument about ART is more wishful than a realistic expectation. Future research should probe more carefully why resource wealth has not been deployed more effectively for fighting disease-a point we can all agree on

Application model of k-means clustering: insights into promotion strategy of vocational high school

Admission process is required in promoting the strategy to achieve the target. Through determining the strategic promotion, minimizing the cost in the marketing process could be reached with determining the appropriate promotion strategy. Data mining techniques in this initiative were applied to achieve in determining the promotional strategy. Using Clustering K-Means algorithm, it is one method of non-hierarchical clustering data in classifying student data into multiple clusters based on similarity of the data, so that student data that have the same characteristics are grouped in one cluster and that have different characteristics grouped in another cluster. Implementation using Weka Software is used to help find accurate values where the attributes include home address, school of origin, transportation, and reasons for choosing a school. The cluster of students was classified into five clusters in the following: the first cluster 22 students, the second cluster 10 students, the third cluster 10 students, the fourth cluster a total of 33 students, and the fifth cluster 25 students. The pattern of this result is supposed to contribute to enhance the significant data mining to support the strategic promotion in gaining new prospective students

Using a machine learning model to risk stratify for the presence of significant liver disease in a primary care population

Background: Current strategies for detecting significant chronic liver disease (CLD) in the community are based on the extrapolation of diagnostic tests used in secondary care settings. Whilst this approach provides clinical utility, it has limitations related to diagnostic accuracy being predicated on disease prevalence and spectrum bias, which will differ in the community. Machine learning (ML) techniques provide a novel way of identifying significant variables without preconceived bias. As a proof-of-concept study, we wanted to examine the performance of nine different ML models based on both risk factors and abnormal liver enzyme tests in a large community cohort.Methods: Routine demographic and laboratory data was collected on 1,453 patients with risk factors for CLD, including high alcohol consumption, diabetes and obesity, in a community setting in Nottingham (UK) as part of the Scarred Liver project. A total of 87 variables were extracted. Transient elastography (TE) was used to define clinically significant liver fibrosis. The data was split into a training and hold out set. The median age of the cohort was 59, mean body mass index (BMI) 29.7 kg/m2, median TE 5.5 kPa, 49.2% had type 2 diabetes and 20.3% had a TE >8 kPa.Results: The nine different ML models, which included Random Forrest classifier, Support Vector classification and Gradient Boosting classifier, had a range of area under the curve (AUC) statistics of 0.5 to 0.75. Ensemble Stacker model showed the best performance, and this was replicated in the testing dataset (AUC 0.72). Recursive feature elimination found eight variables had a significant impact on model output. The model had superior sensitivity (74%) compared to specificity (60%).Conclusions: ML shows encouraging performance and highlights variables that may have bespoke value for diagnosing community liver disease. Optimising how ML algorithms are integrated into clinical pathways of care and exploring new biomarkers will further enhance diagnostic utility

Genomics-assisted breeding in four major pulse crops of developing countries: present status and prospects

The global population is continuously increasing and is expected to reach nine billion by 2050. This huge population pressure will lead to severe shortage of food, natural resources and arable land. Such an alarming situation is most likely to arise in developing countries due to increase in the proportion of people suffering from protein and micronutrient malnutrition. Pulses being a primary and affordable source of proteins and minerals play a key role in alleviating the protein calorie malnutrition, micronutrient deficiencies and other undernourishment-related issues. Additionally, pulses are a vital source of livelihood generation for millions of resource-poor farmers practising agriculture in the semi-arid and sub-tropical regions. Limited success achieved through conventional breeding so far in most of the pulse crops will not be enough to feed the ever increasing population. In this context, genomics-assisted breeding (GAB) holds promise in enhancing the genetic gains. Though pulses have long been considered as orphan crops, recent advances in the area of pulse genomics are noteworthy, e.g. discovery of genome-wide genetic markers, high-throughput genotyping and sequencing platforms, high-density genetic linkage/QTL maps and, more importantly, the availability of whole-genome sequence. With genome sequence in hand, there is a great scope to apply genome-wide methods for trait mapping using association studies and to choose desirable genotypes via genomic selection. It is anticipated that GAB will speed up the progress of genetic improvement of pulses, leading to the rapid development of cultivars with higher yield, enhanced stress tolerance and wider adaptability

MENGGUNAKAN METODE LINEAR PROGRAMMING DENGAN TUJUAN MEMINIMISASI BIAYA PRODUKSI KAIN TEKSTIL. PADA PT. INTI GUNAWANTEX

MENGGUNAKAN METODE LINEAR PROGRAMMING DENGAN TUJUAN MEMINIMISASI BIAYA PRODUKSI KAIN TEKSTIL. PADA PT. INTI GUNAWANTEX