Effects of Growth Temperatures on the Fatty Acid Composition of Isolated Chloroplasts From Two Species Differing in Heat Sensitivity

Lipid analyses of chloroplasts isolated from wheat (Triticum aestivum L. cv. Arthur) and milo (Sorghum bicolor cv. Funk\u27s hybrid 522) suggest no major heat effect on lipid class distribution. Assuming milo is more heat tolerant than wheat and that increased saturated/unsaturated fatty acid values increase thermal stability, changes in sulfoquinovosyldiglyceride (SL) appear to be more important than phosphatidylglycerol (PG) in conferring thermal stability to isolated chloroplasts

Milo stover for growing heifers

Five rations (involving 4 forage treatments) were compared: (1) forage sorghum silage, (2) forage sorghum silage ensiled with organic acids , (3) milo stover pellets, (4) milo stover silage and (5) milo stover silage plus rolled milo. Each ration was fed to 13 heifer calves for 114 days. No differences were observed in gain, intake or feed efficiency between heifers fed untreated and organic acid-treated forage sorghum silage. Pelleting milo stover increased dry matter consumption over milo stover silage but resulted in a poorer feed conversion. Adding rolled milo to stover silage improved gain and feed conversion compared to stover silage or pellets . Results indicate that growing heifers can make substantial winter gains on properly supplemented milo stover rations. The feeding value of forage sorghum silage was not improved by adding organic acids

Evaluating brand personality of Milo towards customers’ brand loyalty / Mohammad Raffi Fhidzon and Muhammad Mawardi Masling

This purpose of this study is to determine the influence of brand personality of Milo towards customers’ brand loyalty. There are several factors in brand personality which are sincerity, excitement, competence, sophistication and ruggedness that influence the loyalty of customers towards Milo. At the same time, this study also focuses on determining the strengths, weaknesses, opportunities and threats faced by Milo brand. Furthermore, strategies that can improve Milo in terms of brand personality for Milo to enhance customers brand loyalty are proposed. The quantitative research is conducted to assess the correlation between the dependent and all the independent variables. The population for this study are consumers of Milo in Petaling Jaya, who are regular customers of this brand. In conducting this study, the sampling technique used is convenience sampling where the most accessible customers are chosen as the subject. Research design involves distributing questionnaire to 100 consumers of Milo product. Our analysis finds that the ANOVA result supported the research hypotheses. Nevertheless, result for the regression analysis of coefficient which is used to test the relationship between brand loyalty and sincerity, excitement, competence, sophistication and ruggedness indicate the strongest influence of competence, sincerity and sophistication of the brand. Thus, based on the outcome of study, sincerity, competence and sophistication are the main brand personalities which can contribute to successful loyalty towards Milo brand

The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization

We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on the maximal absolute correlation between the features and residuals. Motivated by the significant advances in integer optimization over the past 10-15 years, we present a Mixed Integer Linear Optimization (MILO) approach to obtain certifiably optimal global solutions to this nonconvex optimization problem. The current state of algorithmics in integer optimization makes our proposal substantially more computationally attractive than the least squares subset selection framework based on integer quadratic optimization, recently proposed in [8] and the continuous nonconvex quadratic optimization framework of [33]. We propose new discrete first-order methods, which when paired with state-of-the-art MILO solvers, lead to good solutions for the Discrete Dantzig Selector problem for a given computational budget. We illustrate that our integrated approach provides globally optimal solutions in significantly shorter computation times, when compared to off-the-shelf MILO solvers. We demonstrate both theoretically and empirically that in a wide range of regimes the statistical properties of the Discrete Dantzig Selector are superior to those of popular $\ell_{1}$-based approaches. We illustrate that our approach can handle problem instances with p = 10,000 features with certifiable optimality making it a highly scalable combinatorial variable selection approach in sparse linear modeling

Multispectral determination of soil moisture

The edited Guymon soil moisture data collected on August 2, 5, 14, 17, 1978 were grouped into four field cover types for statistical analysis. These are the bare, milo with rows parallel to field of view, milo with rows perpendicular to field of view and alfalfa cover groups. There are 37, 22, 24 and 14 observations respectively in each group for each sensor channel and each soil moisture layer. A subset of these data called the 'five cover set' (VEG5) limited the scatterometer data to the 15 deg look angle and was used to determine discriminant functions and combined group regressions

Forecasting Crop Basis Using Historical Averages Supplemented with Current Market Information

This research compares practical methods of forecasting basis, using current market information for wheat, soybeans, corn, and milo (grain sorghum) in Kansas. Though generally not statistically superior, an historical one-year average was optimal for corn, milo, and soybean harvest and post-harvest basis forecasts. A one-year average was also best for wheat post-harvest basis forecasts, whereas a five-year average was the best method for forecasting wheat harvest basis. Incorporating current market information, defined as basis deviation from historical average, improved the accuracy of post-harvest basis forecasts. A naive forecast incorporating current information was often the most accurate for post-harvest basis forecasts.basis forecast, crop basis, current information, naive forecast, Marketing,

Sources of roughage and milo for finishing steers

We used 75 yearling steers in a 92-day trial to evaluate three sources of roughage: (1) chopped prairie hay; (2) milo stover silage; and (3) milo stover pellets; and five milo treatments: (1) dry, 85.5% dry matter (DM); (2) field harvested, high moisture (F-HM), 72.6% DM, ensiled in an O2-limiting structure; (3) F-HM, 79.5% DM, treated with 1.75% ammonium isobutyra1te on a wet basis and stored in a metal bin; (4) F-HM, 73.6% DM, rolled and ensiled in a 10 ft. x 50 ft. concrete stave silo; and (5) harvested at 85.5% DM and reconstituted to 73.3% DM, rolled and ensiled in a 10 ft. x 50 ft. concrete stave silo. Neither performance or carcass characteristic differences could be attributed to source of roughage when it was fed at 15% of the ration dry matter, which indicates that milo stover can be effectively used in finishing rations. Steers fed high-moisture milo treated with AIB or stored in an O2 -limiting structure performed similarly and gained faster (P\u3c.05) and more efficiently (P\u3c.05) than steers fed dry milo

A linear optimization based method for data privacy in statistical tabular data

National Statistical Agencies routinely disseminate large amounts of data. Prior to dissemination these data have to be protected to avoid releasing confidential information. Controlled tabular adjustment (CTA) is one of the available methods for this purpose. CTA formulates an optimization problem that looks for the safe table which is closest to the original one. The standard CTA approach results in a mixed integer linear optimization (MILO) problem, which is very challenging for current technology. In this work we present a much less costly variant of CTA that formulates a multiobjective linear optimization (LO) problem, where binary variables are pre-fixed, and the resulting continuous problem is solved by lexicographic optimization. Extensive computational results are reported using both commercial (CPLEX and XPRESS) and open source (Clp) solvers, with either simplex or interior-point methods, on a set of real instances. Most instances were successfully solved with the LO-CTA variant in less than one hour, while many of them are computationally very expensive with the MILO-CTA formulation. The interior-point method outperformed simplex in this particular application.Peer ReviewedPreprin