THE PRIORITY OF BEEF CATTLE FARM DEVELOPMENT STRATEGY IN SEMARANG REGENCY USING AHP AND SWOT (A’WOT) METHOD

The development of a directed and sustainable beef cattle farm can be achieved if the development strategy plan is based on existing real problems. This study aims to determine the priority of beef cattle farm development strategies in Semarang Regency, Central Java Province, Indonesia. The A’WOT method was applied in this study, by integrating strengths, weaknesses, opportunities, and threats (SWOT) and the Analytical Hierarchy Process (AHP). After determining the strengths, weaknesses, opportunities, and threats found in beef cattle farm business, group weights and SWOT factors were calculated using the AHP method, the results of which were the three priority strategies with the highest scores. These strategies can be summarised as follows: (1) optimising the utilisation of forage through silage and hay making, (2) optimising the provision of suitable land and meeting the technical requirements of livestock, and (3) optimising farmers’ ability to access capital. It was concluded that sustainable beef cattle farm business in Semarang Regency could be improved through the application of priority strategies focusing on feed technology, land suitability, and access to capital. Findings also demonstrated that the A’WOT method is useful and effective in determining livestock sector strategies.

Analysis of Variance Models with Stochastic Group Weights

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This paper is about stochastic group weights in ANOVA models – a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects - one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided.

Analytical weight estimation of landing gear designs

Landing gear weight calculations can be carried out using statistical or analytical methods. Statistical methods were used in the past and offered quick group weights. However, they are not capable of computing accurately the weight of landing gears, which have special geometries and performance. In this work, landing gear weight is computed using analytical methods based on parametric 3D models. The procedure established by Kraus and Wille is applied as a baseline so as to create a procedure capable of dealing with landing gear weight calculations. This method is designed to be as flexible as possible, giving the user the freedom to modify many options and parameters and integrate landing gear design into Robust Aircraft Parametric Interactive Design.

Topologically Ordered Feature Extraction Based on Sparse Group Restricted Boltzmann Machines

How to extract topologically ordered features efficiently from high-dimensional data is an important problem of unsupervised feature learning domains for deep learning. To address this problem, we propose a new type of regularization for Restricted Boltzmann Machines (RBMs). Adding two extra terms in the log-likelihood function to penalize the group weights and topologically ordered factors, this type of regularization extracts topologically ordered features based on sparse group Restricted Boltzmann Machines (SGRBMs). Therefore, it encourages an RBM to learn a much smoother probability distribution because its formulations turn out to be a combination of the group weight-decay and topologically ordered factor regularizations. We apply this proposed regularization scheme to image datasets of natural images and Flying Apsara images in the Dunhuang Grotto Murals at four different historical periods. The experimental results demonstrate that the combination of these two extra terms in the log-likelihood function helps to extract more discriminative features with much sparser and more aggregative hidden activation probabilities.

A Fuzzy Linguistic Go/No-Go Decision Model in New Product Development

New product development~(NPD) is a quite risky and uncertain process. In order to reduce the risks and uncertainties, the firms need to evaluate their new product at each step carefully and make accurate decisions. This paper focuses on uncertain go/no-go decisions in the NPD process. To do so, a probabilistic approach is firstly proposed to elicit a probability distribution of the gate-team's judgement. Secondly, a probabilistic approach is proposed to perform group and multicriteria aggregation with the random interpretation of group weights and criteria weights.