Payment Methods in Acquisitions of Association of Southeast Asian Nations Bidders

This paper examines the determinants of methods of payment in M&A transactions in ASEAN countries. We take into account the effects of characteristics of bidders, targets and countries on the choice of method of payment. The findings document the importance of bidders' technology status, targets' ownership status, relative size of bidders and targets, and especially the corporate governance variables in the countries that involved in those M&A transactions. In addition, crisis periods also distinguish the choices of payment method for domestic and cross-border M&As in ASEAN countries. Keywords: Methods of payment, ASEAN countries, Mergers, Acquisitions JEL Classifications: G32, G34, G3

Numerical and experimental studies for crack detection of a beam-like structure using element stiffness index distribution method

In this paper, numerical and experimental studies for crack detection of structures using "element stiffness index distribution" are presented. The element stiffness index distribution is defined as a vector of norms of sub-matrices corresponding to element stiffness matrices calculated from the reconstructed global stiffness matrix of the beam. When there is a crack at an element, the element stiffness index of that element will be changed. By inspecting the change in the element stiffness index distribution, the crack can be detected. A significant peak in the element stiffness index distribution is the indicator of the crack existence. The crack location is determined by the location of the peak and the crack depth can be determined from the height of the peak. The global stiffness matrix is calculated from the measured frequency response functions instead of mode shapes to avoid limitations of the mode shape-based methods for crack detection. Numerical simulation results for the cases of beam-like structures are provided. The experiment is carried out to justify the efficiency of the proposed method

Rice seed varietal purity inspection using hyperspectral imaging

When distributing rice seed to farmers, suppliers strive to ensure that all seeds delivered belong to the species that was ordered and that the batch is not contaminated by unhealthy seeds or seeds of a different species. A conventional method to inspect the varietal purity of rice seeds is based on manually selecting random samples of rice seed from a batch and evaluating the physical grain properties through a process of human visual inspection. This is a tedious, laborious, time consuming and extremely inefficient task where only a very small subset of the entire batch of the rice seed can be examined. There is, therefore, a need to automate this process to make it repeatable and more efficient while allowing a larger sample of rice seeds from any batch to be analysed. This paper presents an automatic rice seed inspection method which combines hyperspectral imaging and tools from machine learning to automatically detect seeds which are erroneously contained within a batch when they actually belong to a completely different species. Image data from Near-infrared (NIR) and Visible Light (VIS) hyperspectral cameras are acquired for six common rice seed varieties. Two different classifiers are applied to the data: a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape-based features derived from the rice seeds results in an increase in the precision (PPV) of the multi-label classification to 84% compared with 74% when only visual features are used

Automatic cattle location tracking using image processing

Behavioural scientists track animal behaviour patterns through the construction of ethograms which detail the activities of cattle over time. To achieve this, scientists currently view video footage from multiple cameras located in and around a pen, which houses the animals, to extract their location and determine their activity. This is a time consuming, laborious task, which could be automated. In this paper we extend the well-known Real-Time Compressive Tracking algorithm to automatically determine the location of dairy and beef cows from multiple video cameras in the pen. Several optimisations are introduced to improve algorithm accuracy. An automatic approach for updating the bounding box which discourages the algorithm from learning the background is presented. We also dynamically weight the location estimates from multiple cameras using boosting to avoid errors introduced by occlusion and by the tracked animal moving in and out of the field of view

Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection

A conventional method to inspect the varietal purity of rice seeds is based on evaluating human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NIR) and Visible cameras are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape- based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used

Trends in, projections of, and inequalities in reproductive, maternal, newborn and child health service coverage in Vietnam 2000-2030: A Bayesian analysis at national and sub-national levels

Background: To assess the reproductive, maternal, newborn and child health (RMNCH) service coverage in Vietnam with trends in 2000-2014, projections and probability of achieving targets in 2030 at national and sub-national levels; and to analyze the socioeconomic, regional and urban-rural inequalities in RMNCH service indicators. Methods: We used national population-based datasets of 44,624 households in Vietnam from 2000 to 2014. We applied Bayesian regression models to estimate the trends in and projections of RMNCH indicators and the probabilities of achieving the 2030 targets. Using the relative index, slope index, and concentration index of inequality, we examined the patterns and trends in RMNCH coverage inequality. Findings: We projected that 9 out of 17 health service indicators (53%) would likely achieve the 2030 targets at the national level, including at least one and four ANC visits, BCG immunization, access to improved water and adequate sanitation, institutional delivery, skilled birth attendance, care-seeking for pneumonia, and ARI treatment. We observed very low coverages and zero chance of achieving the 2030 targets at national and sub-national levels in early initiation and exclusive breastfeeding, family planning needs satisfied, and oral rehydration therapy. The most deprived households living in rural areas and the Northwest, Northeast, North Central, Central Highlands, and Mekong River Delta regions would not reach the 80% immunization coverage of DPT3, Polio3, Measles and full immunization. We found socioeconomic, regional, and urban-rural inequalities in all RMNCH indicators in 2014 and no change in inequalities over 15 years in the lowest-coverage indicators. Interpretation: Vietnam has made substantial progress toward UHC. By improving the government\u27s health system reform efforts, re-allocating resources focusing on people in the most impoverished rural regions, and restructuring and enhancing current health programs, Vietnam can achieve the UHC targets and other health-related SDGs

A Hybridized Flower Pollination Algorithm and Its Application on Microgrid Operations Planning

The meta-heuristic algorithms have been applied to handle various real-world optimization problems because their approach closely resembles natural human thinking and processing relatively quickly. Flowers pollination algorithm (FPA) is one of the advanced meta-heuristic algorithms; still, it has suffered from slow convergence and insufficient accuracy when dealing with complicated problems. This study suggests hybridizing the FPA with the sine–cosine algorithm (call HSFPA) to avoid FPA drawbacks for microgrid operations planning and global optimization problems. The objective function of microgrid operations planning is constructed based on the power generation distribution system’s relevant economic costs and environmental profits. Adapting hop size, diversifying local search, and diverging agents as strategies from learning SCA are used to modify the original FPA equation for improving the HSFPA solutions in terms of diversity pollinations, increasing convergence, and preventing local optimal traps. The experimental results of the HSFPA compared with the other algorithms in the literature for the benchmark test function and microgrid operations planning problem to evaluate the proposed scheme. Compared results show that the HSFPA offers outstanding performance compared to other competitors for the testing function. The HSFPA also delivers efficient optimal performance in microgrid optimization for solving the operations planning problem

A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results

A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem

This paper proposes an improved Bat algorithm based on hybridizing a parallel and compact method (namely pcBA) for a class of saving variables in optimization problems. The parallel enhances diversity solutions for exploring in space search and sharing computation load. Nevertheless, the compact saves stored variables for computation in the optimization approaches. In the experimental section, the selected benchmark functions, and the energy balance problem in Wireless sensor networks (WSN) are used to evaluate the performance of the proposed method. Results compared with the other methods in the literature demonstrate that the proposed algorithm achieves a practical method of reducing the number of stored memory variables, and the running time consumption