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This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers' demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand's current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide. [ABSTRACT FROM AUTHOR]

Educational data classification has become an effective tool for exploring the hidden pattern or relationship in educational data and predicting students' performance or teachers' competency. This study proposes a new method based on machine learning algorithms to predict the technology-integrated competency of pre-service mathematics teachers. In this paper, we modified the inertial subgradient extragradient algorithm for pseudomonotone equilibrium and proved the weak convergence theorem under some suitable conditions in Hilbert spaces. We then applied to solve data classification by extreme learning machine using the dataset comprised of the technology-integrated competency of 954 pre-service mathematics teachers in a university in northern Thailand, longitudinally collected for five years. The flexibility of our algorithm was shown by comparisons of the choice of different parameters. The performance was calculated and compared with the existing algorithms to be implemented for prediction. The results show that the proposed method achieved a classification accuracy of 81.06%. The predictions were implemented using ten attributes, including demographic information, skills, and knowledge relating to technology developed throughout the teacher education program. Such data driven studies are significant for establishing a prospective teacher competency analysis framework in teacher education and contributing to decision-making for policy design. [ABSTRACT FROM AUTHOR]

This study aims to select the ideal mixture of small and medium-sized destinations and attractions in Thailand's Ubon Ratchathani Province in order to find potential wellness destinations and attractions. In the study region, 46 attractions and destinations were developed as the service sectors for wellness tourism using the designed wellness framework and the quality level of the attractions and destinations available on social media. Distinct types of tourists, each with a different age and gender, comprise a single wellness tourist group. Due to them, even with identical attractions and sites, every traveler has a different preference. A difficult task for travel agencies is putting together combinations of attractions and places for each tourist group. In this paper, the mathematical formulation of the suggested problem is described, and the optimal solution is achieved using Lingo v.16. Unfortunately, the large size of test instances cannot be solved with Lingo v16. However, the large-scale problem, particularly the case study in the target area, has been solved using a metaheuristic method called AMIS. According to the computation in the final experiment, AMIS can raise the solution quality across all test instances by an average of 3.83 to 8.17 percent. Therefore, it can be concluded that AMIS outperformed all other strategies in discovering the ideal solution. AMIS, GA and DE may lead visitors to attractions that generate 29.76%, 29.58% and 32.20%, respectively, more revenue than they do now while keeping the same degree of preference when the number of visitors doubles. The attractions' and destinations' utilization has increased by 175.2 percent over the current situation. This suggests that small and medium-sized enterprises have a significantly higher chance of flourishing in the market. [ABSTRACT FROM AUTHOR]

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. [ABSTRACT FROM AUTHOR]

The central issue of this paper is that one can develop emergent macro-dynamics from micro agent models and that the resulting models fall very much in the complexity-chaos line of the development of theory. The core theoretical contribution is a presentation of a clear, sensical, and potentially very powerful architecture for developing algorithms for the embedding of levels—the so-called master equation approach—and its comparison with alternative architectures. Finally, to illustrate the strategy and to demonstrate that the approach produces interesting and useful results, we give an application: a multilevel simulation model of military coups d'état, which is tested using data from Thailand between 1932 and 1992. [ABSTRACT FROM AUTHOR]

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO. [ABSTRACT FROM AUTHOR]

Study design:A cross-sectional study.Objectives:To study prevalence of pressure ulcers (PrUs), quality of life (QoL) and effect of wheelchair cushions used by Thai wheelchair users with chronic spinal cord injury (SCI).Setting:Maharaj Hospital, Chiang Mai, Thailand.Methods:Thai chronic SCI wheelchair users, aged over 18 years and non-ambulatory with ASIA impairment scale A, B or C were recruited. They completed the PrUs questionnaire and rated the EuroQoL-5D and their health status with a visual analog scale (VAS). Demographic data of each participant were extracted from medical records. The EQ-5D health states were transformed to utility scores by using the Thai algorithm and the prevalence of PrUs was reported. The EQ-5D, the utility scores and the health status VAS were compared between those with and without current PrUs and between those participants using foam and air-filled cushions.Results:Of 129 participants, 26.4% had current PrUs at the time of the study, 27.9% had healed PrUs and 45.7% never had PrUs. The median VAS score for health status was 70 (Q1=50, Q3=80). Based on the EQ-5D, only one dimension (anxiety/depression) was significantly different between those with and those without current PrUs (P=0.015). Those using an air-filled cushions had a mean utility score four times higher than of those using a foam cushion (0.131 vs 0.032, P=0.089) but not statistically significant.Conclusions:PrUs are still prevalent among Thai wheelchair users with chronic SCI. Anxiety/depression is associated with current ulcers. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study reported here was to validate the risk-scoring algorithm for prognostication of scrub typhus severity. Methods: The risk-scoring algorithm for prognostication of scrub typhus severity developed earlier from two general hospitals in Thailand was validated using an independent dataset of scrub typhus patients in one of the hospitals from a few years later. The predictive performances of the two datasets were compared by analysis of the area under the receiver-operating characteristic curve (AuROC). Classification of patients into non-severe, severe, and fatal cases was also compared. Results: The proportions of non-severe, severe, and fatal patients by operational definition were similar between the development and validation datasets. Patient, clinical, and laboratory profiles were also similar. Scores were similar in both datasets, both in terms of discriminating non-severe from severe and fatal patients (AuROC =88.74% versus 91.48%, P=0.324), and in discriminating fatal from severe and non-severe patients (AuROC =88.66% versus 91.22%, P=0.407). Over- and under-estimations were similar and were clinically acceptable. Conclusion: The previously developed risk-scoring algorithm for prognostication of scrub typhus severity performed similarly with the validation data and the first dataset. The scoring algorithm may help in the prognostication of patients according to their severity in routine clinical practice. Clinicians may use this scoring system to help make decisions about more intensive investigations and appropriate treatments. [ABSTRACT FROM AUTHOR]

Purpose: To develop a simple risk-scoring system to forecast scrub typhus severity. Patients and methods: Seven years' retrospective data of patients diagnosed with scrub typhus from two university-affiliated hospitals in the north of Thailand were analyzed. Patients were categorized into three severity groups: nonsevere, severe, and dead. Predictors for severity were analyzed under multivariable ordinal continuation ratio logistic regression. Significant coefficients were transformed into item score and summed to total scores. Results: Predictors of scrub typhus severity were age >15 years, (odds ratio [OR] =4.09), pulse rate >100/minute (OR 3.19), crepitation (OR 2.97), serum aspartate aminotransferase >160 IU/L (OR 2.89), serum albumin ⩽3.0 g/dL (OR 4.69), and serum creatinine >1.4 mg/dL (OR 8.19). The scores which ranged from 0 to 16, classified patients into three risk levels: non-severe (score ⩽5, n=278, 52.8%), severe (score 6-9, n=143, 27.2%), and fatal (score ⩾10, n=105, 20.0%). Exact severity classification was obtained in 68.3% of cases. Underestimations of 5.9% and overestimations of 25.8% were clinically acceptable. Conclusion: The derived scrub typhus severity score classified patients into their severity levels with high levels of prediction, with clinically acceptable under- and overestimations. This classification may assist clinicians in patient prognostication, investigation, and management. The scoring algorithm should be validated by independent data before adoption into routine clinical practice. [ABSTRACT FROM AUTHOR]

We consider the problem of designing truthful mechanisms for scheduling n tasks on a set of m parallel related machines in order to minimize the makespan. In what follows, we consider that each task is owned by a selfish agent. This is a variant of the KP-model introduced by Koutsoupias and Papadimitriou (Proc. of STACS 1999, pp. 404–413, ) (and of the CKN-model of Christodoulou et al. in Proc. of ICALP 2004, pp. 345–357, ) in which the agents cannot choose the machine on which their tasks will be executed. This is done by a centralized authority, the scheduler. However, the agents may manipulate the scheduler by providing false information regarding the length of their tasks. We introduce the notion of increasing algorithm and a simple reduction that transforms any increasing algorithm into a truthful one. Furthermore, we show that some of the classical scheduling algorithms are indeed increasing: the LPT algorithm, the PTAS of Graham (SIAM J. Appl. Math. 17(2):416–429, ) in the case of two machines, as well as a simple PTAS for the case of m machines, with m a fixed constant. Our results yield a randomized r(1+ ε)-approximation algorithm where r is the ratio between the largest and the smallest speed of the related machines. Furthermore, by combining our approach with the classical result of Shmoys et al. (SIAM J. Comput. 24(6):1313–1331, ), we obtain a randomized 2 r(1+ ε)-competitive algorithm. It has to be noticed that these results are obtained without payments, unlike most of the existing works in the field of Mechanism Design. Finally, we show that if payments are allowed then our approach gives a (1+ ε)-algorithm for the off-line case with related machines. [ABSTRACT FROM AUTHOR]