Research review : searching for a new framework for Thailand's foreign policy in the post-cold war era

Thai foreign policy in the 1990s has been said to be contingent on the government in power, which changes between (or within) these groups and vacillates between pro-democratic reformists/principle-pursuers and the conservatives/profit-seekers. In these studies, Thailand’s Indochinese policy has often been referred to as a typical consequence of politics between the pragmatists and the reformists. However, whether or not domestic oppositional politics is the key determinant of foreign policy in the post-Cold War era still requires further examination, precisely because the model is now facing serious challenges between theory and reality. In this paper, I review the existing arguments concerning Thailand’s foreign policy in the post-Cold War Era and point out their limitations and questions for future study.

Negative skill sorting across production chains

Previous literature generally predicts that individuals with higher skills work in industries with longer production chains. However, the opposite skill-sorting pattern, a "negative skill-sorting" phenomenon, is also observed in reality. This paper proposes a possible mechanism by which both cases can happen and shows that negative skill sorting is more likely to occur when the quality of intermediate inputs degrade rapidly (or improves slowly) along the production chain. We empirically confirm our theoretical prediction by using country-industry panel data. The results are robust regardless of estimation method, control variables, and industry coverage. This study has important implications for understanding countries' comparative advantages and development patterns.

PubDNA Finder in a Nutshell. Searching the Life Sciences Literature with Sequences of Nucleic Acids

Biomedical researchers and clinicians working with molecular technologies in routine clinical practice often need to review the available literature to gather information regarding specific sequences of nucleic acids. This includes, for instance, finding articles related to a concrete DNA sequence, or identifying empirically-validated primer/probe sequences to evaluate the presence of different micro-organisms. Unfortunately, these hard and time-consuming tasks often need to be manually performed by researchers themselves since no publicly available biomedical literature search engine, e.g. PubMed, PubMed Central (PMC), etc., provides the required search functionalities. In this article, we describe PubDNA Finder, a web service that enables users to perform advanced searches on PubMed Central-indexed full text articles with sequences of nucleic acids

PSO and neural networks: Optimal combination to solve non linear complex problems

Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Models for Internal Quality Fruit Sorting, Based on Time- Domain Laser Reflectance Spectroscopy (TDRS)

Time domain laser reflectance spectroscopy (TDRS) was applied for the first time to evaluate internal fruit quality. This technique, known in medicine-related knowledge areas, has not been used before in agricultural or food research. It allows the simultaneous non-destructive measuring of two optical characteristics of the tissues: light scattering and absorption. Models to measure firmness, sugar & acid contents in kiwifruit, tomato, apple, peach, nectarine and other fruits were built using sequential statistical techniques: principal component analysis, multiple stepwise linear regression, clustering and discriminant analysis. Consistent correlations were established between the two parameters measured with TDRS, i.e. absorption & transport scattering coefficients, with chemical constituents (sugars and acids) and firmness, respectively. Classification models were built to sort fruits into three quality grades, according to their firmness, soluble solids and acidity.

Mechanical segregation of apricot varieties and their ripeness levels at harvest

Some laboratory tests consisting on quasi-static compression and puncture forces carried out on twelve varieties of apricot during 1990 and 1991 were effective in sorting them. These mechanical properties show a high correlation w i th the ethylene production rate per fruit, so allowing to discriminate between ripeness levels at harvest. In this study it is also demonstrated that puncture seems to be the less variable mechanical test. The values (N/mm) obtained with it show a highly significant correlation with compression resistance and with quasi-static compression damage of the fruits.

Insect growth regulators as potential insecticides to control olive fruit fly (Bactrocera oleae Rossi): insect toxicity bioassays and molecular docking approach

Olive fruit fly, Bactrocera oleae (Rossi), is a key pest in olive orchards, causing serious economic damage. To date, the pest has already developed resistance to the insecticides commonly applied to control it. Thus, in searching for new products for an accurate resistance management programme, targeting the ecdysone receptor (EcR)might provide alternative compounds for use in such programmes. RESULTS: Residual contact and oral exposure in the laboratory of B. oleae adults to the dibenzoylhydrazine-based compounds methoxyfenozide, tebufenozide and RH-5849 showed different results. Methoxyfenozide and tebufenozide did not provoke anynegative effectsontheadults,but RH-5849 killed98-100%of the treated insects15 days after treatment. Theligand-binding domain (LBD) of the EcR of B. oleae (BoEcR-LBD) was sequenced, and a homology protein model was constructed. Owing to a restricted extent of the ligand-binding cavity of the BoEcR-LBD, docking experiments with the three tested insecticides showed a severe steric clash in the case of methoxyfenozide and tebufenozide, while this was not the case with RH-5849. CONCLUSION: IGR molecules similar to the RH-5849 molecule, and different from methoxyfenozide and tebufenozide, might have potential in controlling this pest.

Hybrid choice model for propensity to travel and tour complexity

During the last years cities around the world have invested important quantities of money in measures for reducing congestion and car-trips. Investments which are nothing but potential solutions for the well-known urban sprawl phenomenon, also called the “development trap” that leads to further congestion and a higher proportion of our time spent in slow moving cars. Over the path of this searching for solutions, the complex relationship between urban environment and travel behaviour has been studied in a number of cases. The main question on discussion is, how to encourage multi-stop tours? Thus, the objective of this paper is to verify whether unobserved factors influence tour complexity. For this purpose, we use a data-base from a survey conducted in 2006-2007 in Madrid, a suitable case study for analyzing urban sprawl due to new urban developments and substantial changes in mobility patterns in the last years. A total of 943 individuals were interviewed from 3 selected neighbourhoods (CBD, urban and suburban). We study the effect of unobserved factors on trip frequency. This paper present the estimation of an hybrid model where the latent variable is called propensity to travel and the discrete choice model is composed by 5 alternatives of tour type. The results show that characteristics of the neighbourhoods in Madrid are important to explain trip frequency. The influence of land use variables on trip generation is clear and in particular the presence of commercial retails. Through estimation of elasticities and forecasting we determine to what extent land-use policy measures modify travel demand. Comparing aggregate elasticities with percentage variations, it can be seen that percentage variations could lead to inconsistent results. The result shows that hybrid models better explain travel behavior than traditional discrete choice models.

Radio propagation modeling and measurements for ZigBee based indoor wireless sensor networks

The deployment of nodes in Wireless Sensor Networks (WSNs) arises as one of the biggest challenges of this field, which involves in distributing a large number of embedded systems to fulfill a specific application. The connectivity of WSNs is difficult to estimate due to the irregularity of the physical environment and affects the WSN designers? decision on deploying sensor nodes. Therefore, in this paper, a new method is proposed to enhance the efficiency and accuracy on ZigBee propagation simulation in indoor environments. The method consists of two steps: automatic 3D indoor reconstruction and 3D ray-tracing based radio simulation. The automatic 3D indoor reconstruction employs unattended image classification algorithm and image vectorization algorithm to build the environment database accurately, which also significantly reduces time and efforts spent on non-radio propagation issue. The 3D ray tracing is developed by using kd-tree space division algorithm and a modified polar sweep algorithm, which accelerates the searching of rays over the entire space. Signal propagation model is proposed for the ray tracing engine by considering both the materials of obstacles and the impact of positions along the ray path of radio. Three different WSN deployments are realized in the indoor environment of an office and the results are verified to be accurate. Experimental results also indicate that the proposed method is efficient in pre-simulation strategy and 3D ray searching scheme and is suitable for different indoor environments.

Influence of loudspeaker directivity and measurement geometry on direct acoustic levels over façades for acoustic insulation tests with the International Standard ISO 140-5

The International Standard ISO 140-5 on field measurements of airborne sound insulation of façades establishes that the directivity of the measurement loudspeaker should be such that the variation in the local direct sound pressure level (ΔSPL) on the sample is ΔSPL < 5 dB (or ΔSPL < 10 dB for large façades). This condition is usually not very easy to accomplish nor is it easy to verify whether the loudspeaker produces such a uniform level. Direct sound pressure levels on the ISO standard façade essentially depend on the distance and directivity of the loudspeaker used. This paper presents a comprehensive analysis of the test geometry for measuring sound insulation and explains how the loudspeaker directivity, combined with distance, affects the acoustic level distribution on the façade. The first sections of the paper are focused on analysing the measurement geometry and its influence on the direct acoustic level variations on the façade. The most favourable and least favourable positions to minimise these direct acoustic level differences are found, and the angles covered by the façade in the reference system of the loudspeaker are also determined. Then, the maximum dimensions of the façade that meet the conditions of the ISO 140-5 standard are obtained for the ideal omnidirectional sound source and the piston radiating in an infinite baffle, which is chosen as the typical radiation pattern for loudspeakers. Finally, a complete study of the behaviour of different loudspeaker radiation models (such as those usually utilised in the ISO 140-5 measurements) is performed, comparing their radiation maps on the façade for searching their maximum dimensions and the most appropriate radiation configurations.