Local Wisdom: The Development Of Community Culture And Production Processes In Thailand

The objective of this research was to study the development of production processes and community culture in the Songkhla Lake Basin in southern Thailand. We used a qualitative method and did in-depth interviews with 25 local community leaders in 25 communities surrounding the lake. We found that the concept of community culture was developed through local community leaders to the agricultural production by using culture to run the production process, which is based on the concept of self-sufficiency and self-reliance. From this study, the authors found that this development has led to a decrease in residents’ use of technology and a return to the use of labor. The development also reduces the use of chemical fertilizers and insecticides. People have returned to the use of herbs for curing disease in humans and animals. In addition, the development helps people save on production costs and reduce pollution

The Favorite Cultural Places And Traditional Activities Of Travelers: A Case Study Of Songkhla Province, Thailand

The manuscript entitled The Favorite Cultural Places And Traditional Activities Of Travelers:  A Case Study Of Songkhla Province, Thailand was retracted on September 11, 2014. Please contact our office at [email protected] for more information.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.Eurpean Commission, H2020, 66210

Risk Factor of Proximal Lag Screw Cut-Out After Cephalomedullary Nail Fixation in Trochanteric Femoral Fractures: A Retrospective Analytic Study

Objective: A cephalomedullary nail is the treatment of choice for trochanteric fractures; however, a lag screw cutout is one of the most devastating complications. The lag screw cut-out rate was reported to be around 2.5%–8.3%. This study aimed to evaluate the prevalence of lag screw cut-outs and identify the associated risk factors. Materials and Methods: A retrospective review of 267 trochanteric fracture patients treated with cephalomedullary nail fixation from January 2007 to December 2017 was conducted. The demographic variables were documented, comprising age, gender, fracture pattern, and AO/OTA classification. Immediate postoperative radiographs were assessed for quality of reduction and implant position. Lag screw cut-outs or radiographic union were determined using the final follow-up radiograph. Prognostic factors associated with lag screw cut-out were determined using univariate and multivariate logistic regression analyses. Results: Of the 175 patients, 154 were successfully treated, and 21 had a lag screw cut-out. There were no significant differences in mean ages or genders of the union and cut-out groups. No lag screw cut-outs were observed in patients with AO/OTA 31-A1. Patients with AO/OTA 31-B2.1 had a higher rate of screw cut-out (OR 10.5, [3.22, 34.25] p < .001). The disintegration of basicervical fragments was significantly associated with lag screw cut-out (OR 5.51, [2.01, 15.12] p = .001). The highest cut-out rate was found in the superoanterior and superoposterior positions of the lag screw. However, the screw position did not reach the significance level in a multivariate analysis (p = .094). Conclusion: The prevalence of lag screw cut-out after cephalomedullary nail fixation for trochanteric fractures was 12%. A simple, two-part, basicervical trochanteric fracture hads a significantly higher risk of lag screw cut-out

Joint Bayesian separation and restoration of CMB from convolutional mixtures

We propose a Bayesian approach to joint source separation and restoration for astrophysical diffuse sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps in different directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques.Comment: 11 pages, 6 figures. Submitted to MNRA

An authoring tool for educators to make virtual labs

This paper focuses on the design and implementation of a tool that allows educators to author 3D virtual labs. The methodology followed is based on web 3D frameworks such as three.js and WordPress that allowed us to develop simplified interfaces for modifying Unity3D templates. Two types of templates namely one for Chemistry and one for Wind Energy labs were developed that allow to test the generalization, user-friendliness and usefulness of such an approach. Results have shown that educators are much interested on the general concept, but several improvements should be made towards the user-friendliness and the intuitiveness of the interfaces in order to allow the inexperienced educators in 3D gaming to make such an attempt.peer-reviewe

Bayesian Restoration and Reconstruction of High-Resolution Images from Low-Resolution Images with Unknown Degradations.

The research topic of this dissertation is the development of stochastic non-stationary image models as image priors used for regularization in restoration and super-resolution problems. The proposed non-stationary image models lead to spatially adaptive regularization, or in other words, non-uniform regularization along the image which depends on the local spatial activity. Furthermore, working in the stochastic framework we employ the Bayesian methodology to solve these inverse problems and in parallel to estimate the model parameters. Thus, development of Bayesian restoration and reconstruction algorithms is another major issue of this dissertation. First, we introduce a new hierarchical (two-level) Gaussian non-stationary image prior. This prior assumes that the residuals of the first order differences of the image, in four different directions, are Gaussian random variables with zero mean and variance that is spatially varying. In this way, the variances manifest the spatial adaptivity mechanism. In order to deal with the resulting over-parameterization of this model, the spatially varying variances are considered random variables (not parameters) and a Gamma hyper-prior is imposed on them, which is conjugate to the Gaussian. To learn this model and infer the image we propose two iterative algorithms. The first is based on the maximum a posteriori estimation (MAP) principle and computes explicitly both the image and the spatially varying variances in all four directions. The second is a Bayesian algorithm that marginalizes the “hidden variables”. Also, the marginalization of the hidden variables produces a Student’s-t distribution. Next, we propose a new Bayesian inference framework for image restoration using a prior in product form. This prior assumes that the outputs of local high-pass filters, (their number is arbitrary), follow again the Student’s-t distribution. Then, a Bayesian inference methodology is proposed that bypasses the difficulty of evaluating the normalization constant of product type priors. The methodology is based on a constrained variational approximation that uses the outputs of all the local high-pass filters to produce an estimate of the original image. In this manner the use of improper priors is avoided and all the parameters of the prior model are estimated from the data. As a next step, we extend the total-variation prior by introducing a new prior which has a number of novel features. More specifically, we introduce a total-variation (TV) prior with spatially varying regularization parameters. In order to avoid the over parameterization, we introduce a Gamma hyperprior for the spatially adaptive regularization parameters of the local TV priors. We also use this prior in a product form, which means that we assume that the outputs of an arbitrary number of high-pass filters are distributed according to this prior. This gives two novel features to the new prior. First, it is explicitly spatially adaptive and thus it is better suited to capture the salient features of the image. Second, it is in product form and has the ability to enforce simultaneously a number of different properties to the image. If the hidden variables of the second layer are marginalized, the resulting density function has a form similar to a Student's-t distribution; thus, we label it as Generalized Student's-t. Due to the complexity of this model, we resort to the variational approximation for Bayesian inference.Το ερευνητικό αντικείμενο της διατριβής αυτής σχετίζεται με την ανάπτυξη πρωτότυπων μεθοδολογιών για τα προβλήματα της ανόρθωσης εικόνων (image restoration) και της υπερ-ανάλυσης εικόνων (image super-resolution). Πιο συγκεκριμένα, η διατριβή επικεντρώνεται στη μελέτη χωρικά μεταβαλλόμενων στοχαστικών μοντέλων κατάλληλων για να χρησιμοποιηθούν ως εκ των προτέρων κατανομές (priors) προκειμένου να επιτευχθεί κανονικοποίηση (regularization) στα προβλήματα της ανόρθωσης και της υπερ-ανάλυσης εικόνων. Με τα προτεινόμενα μη-στατικά μοντέλα εικόνας επιτυγχάνεται τοπικά προσαρμοζόμενη κανονικοποίηση, δηλαδή ανομοιόμορφη κανονικοποίηση της εικόνας εξαρτώμενη από την τοπική χωρική δραστηριότητα. Χρησιμοποιώντας τη στοχαστική προσέγγιση για τη μοντελοποίηση των εικόνων, εφαρμόζεται η Μπεϋζιανή μεθοδολογία για τη λύση των αντίστροφων παραπάνω προβλημάτων καθώς και για την εκτίμηση των παραμέτρων του μοντέλου. Κατά συνέπεια, η ανάπτυξη Μπεϋζιανών αλγορίθμων ανόρθωσης και υπερ-ανάλυσης είναι ένα ακόμη βασικό πεδίο έρευνας της διατριβής. Στη διατριβή αυτή προτείνεται καταρχήν μια νέα ιεραρχική (δύο επιπέδων) εκ των προτέρων κατανομή που είναι Γκαουσιανή και μη-στατική. Αυτή η κατανομή θεωρεί ότι οι πρώτες διαφορές των εικόνων, σε τέσσερις διαφορετικές διευθύνσεις, είναι Γκαουσιανές τυχαίες μεταβλητές με χωρικά μεταβαλλόμενη διακύμανση. Με τον τρόπο αυτό, οι διακυμάνσεις υλοποιούν το μηχανισμό της χωρικής μεταβλητότητας. Για να αντιμετωπιστεί το ζήτημα της υπερ-παραμετροποίησης αυτού του μοντέλου, οι χωρικά μεταβαλλόμενες διακυμάνσεις θεωρούνται τυχαίες μεταβλητές (όχι παράμετροι) που ακολουθούν μια κοινή κατανομή Γάμμα. Για την εκπαίδευση του μοντέλου και την εκτίμηση της εικόνας προτείνονται δύο επαναληπτικοί αλγόριθμοι. Ο ένας βασίζεται στην αρχή της maximum a posteriori (MAP) εκτίμησης και υπολογίζει άμεσα και την εικόνα και τις χωρικά μεταβαλλόμενες διακυμάνσεις. Ο άλλος είναι ένας Μπεϋζιανός αλγόριθμος που βασίζεται στην περιθωριοποίηση των ενδιάμεσων «κρυμμένων» μεταβλητών. Στη συνέχεια, προτείνεται μια νέα Μπεϋζιανή προσέγγιση για ανόρθωση εικόνων στην οποία χρησιμοποιείται μια εκ των προτέρων κατανομή για την εικόνα η οποία έχει μορφή γινομένου. Παρουσιάζεται μια μεθοδολογία που ξεπερνά τη δυσκολία της εκτίμησης της σταθεράς κανονικοποίησης των κατανομών τύπου γινομένου και βασίζεται σε μια variational προσέγγιση με περιορισμούς (constrained variational approximation). Με τον τρόπο αυτό αποφεύγεται η χρήση μη κανονικοποιημένων (improper) κατανομών και όλες οι παράμετροι του μοντέλου εκτιμώνται από τα δεδομένα. Ως επόμενο βήμα, προτείνεται μια επέκταση της total-variation (TV) εκ των προτέρων κατανομής για την εικόνα μέσω της εισαγωγής χωρικά μεταβαλλόμενων παραμέτρων κανονικοποίησης. Για την αποφυγή της υπερ-παραμετροποίησης, επιβάλλουμε μια Γάμμα κατανομή για τις χωρικά μεταβαλλόμενες παραμέτρους των τοπικών TV κατανομών