cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7079065
title: The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.

description: This dataset is related to paper journal paper with DOI: 10.1016/j.nima.2022.167083. The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. Data files were prepared by agnieszka.gierej@vub.be
DOI: 10.5281/zenodo.7078874
title: The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.

description: This dataset is related to paper journal paper with DOI: 10.1016/j.nima.2022.167083. The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. Data files were prepared by agnieszka.gierej@vub.be
DOI: 10.5281/zenodo.6934169
title: In Situ Diversity of Metabolism and Carbon Use Efficiency among Soil Bacteria

description: Dataset for the MS
DOI: 10.5281/zenodo.6934168
title: In Situ Diversity of Metabolism and Carbon Use Efficiency among Soil Bacteria

description: Dataset for the MS
DOI: 10.34740/kaggle/dsv/112810
title: Android Device Jittering
DOI: 10.5281/zenodo.7057558
title: Extensive literature search on organic arsenic in food

description: This record is a supplement to the external scientific report titled <em>Extensive literature search on organic arsenic in food </em>available at https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2022.EN-7565 <strong>Annex A Summary tables</strong> <strong>Annex B - Evaluated references</strong> The archive contains references grouped in 2 folders: included references and excluded references. <strong>Annex C – Endnote library files</strong> The archive contains: - the EndNoteTM file “EFSA_Arsen_complete” with all retrieved references after duplicate check organised on the one hand by individual literature databases and on the other hand by areas and substance group, as well as screening for relevance. - the EndNote file “EFSA_EndNote_summary tables” with all assigned relevant references for the summary tables which are organised by relevant area and substance group.
description: EU; xlsx; feedco@efsa.europa.eu
DOI: 10.5281/zenodo.7057559
title: Extensive literature search on organic arsenic in food

description: This record is a supplement to the external scientific report titled <em>Extensive literature search on organic arsenic in food </em>available at https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2022.EN-7565 <strong>Annex A Summary tables</strong> <strong>Annex B - Evaluated references</strong> The archive contains references grouped in 2 folders: included references and excluded references. <strong>Annex C – Endnote library files</strong> The archive contains: - the EndNoteTM file “EFSA_Arsen_complete” with all retrieved references after duplicate check organised on the one hand by individual literature databases and on the other hand by areas and substance group, as well as screening for relevance. - the EndNote file “EFSA_EndNote_summary tables” with all assigned relevant references for the summary tables which are organised by relevant area and substance group.
description: EU; xlsx; feedco@efsa.europa.eu
DOI: 10.14457/tu.the.2021.582
title: Reverse logistics network design with a 3-phase interactive intuitionistic fuzzy goal programming approach: a case study of COVID-19 in Pathum Thani, Thailand

description: During outbreaks, a vast quantity of Infected Medical Waste (IMW) can be substantially generated in a short period of time. Without proper management, the IMW rapidly accumulates and poses a huge risk to medical personnel and surrounding communities. Thus, an effective IMW reverse logistics network design plays an essential role in controlling the spreading of disease. This study proposes an Intuitionistic Fuzzy Multi-Objective Multi-Period Mixed-Integer Linear Programming (IFMOMILP) model for an effective IMW management in outbreaks under uncertainty, taking into account financial and risk factors subject to a priority from Decision Makers (DMs). The primary emphasis is on determining the best optimal locations and capacity levels for temporary facilities, which include temporary storage and treatment centers, as well as the optimal transportation routes. A 3-phase interactive Intuitionistic Fuzzy Goal Programming (i-IFGP) approach is developed to solve this IFMOMILP model. First, Jiménez approach is applied to handle the uncertainties. Then, the problem is solved by Intuitionistic Fuzzy Goal Programming (IFGP) and a comparison between the fuzzy goal programming and the intuition fuzzy goal programming under an optimal solution computation is done to present the best outcome subject to uncertainty in information and hesitation in a decision-making process. A real case study of the COVID-19 outbreak in Pathum Thani province in Thailand was carried out to demonstrate the effectiveness of the proposed approach. The proposed approach yields solutions with varying feasibility degrees and scaling factors, providing alternatives for DMs. It also provides the details about the acceptable degree (µ), rejective degree (ν), and the hesitation index (π) of the outcomes, and the score function is utilized to imply DMs’ satisfactory with the outcomes, which is a concrete measure since it can reflect the intention of the DMs.
DOI: 10.14457/tu.the.2021.583
title: Financial performance prediction tool based on firms’ internal capability determinants: a case of listed firms in Thailand during the transition period of going public

description: This study attempts to develop a financial performance prediction tool for listed firm in the Stock Exchange of Thailand (SET) during the transition period from one year before to four years after going public. Logistic regression is employed in this tool to build the financial prediction models based on two internal capability determinants, namely Firm Financial Strength (Liquidity, Leverage and Cost of Goods Sold) and Firm Characteristics (Asset Utilization, Tangibility and Company Size). The analysis is based on 111 listed firms that joined the Stock Exchange of Thailand (SET) between 2003 and 2014, inclusively. Decision makers can make use of the developed models in this tool to predict their firms’ financial performance in relative groups (high, medium and low performance groups) during the transition years when they become public. In addition, they can use the results of the prediction to prepare the most effective investment plan with the best strategy of when and how much should be invested for each relative type of firms in order to upgrade its financial performance in relative to other firms in the market.
DOI: 10.14457/tu.the.2021.584
title: Vehicle route problem in cold chain logistics

description: Nowadays, the logistic system is becoming very important part for all businesses. To understand the problems about in with Vehicle routing problem with load in each node to help in planning the route that increases the efficiency and shorted of transportation, this study is presenting a vehicle routing problem in cold chain logistic. We conduct our computational experiments to minimize total transportation distance. Therefore, this paper will adapt from existing method computing a linear programing to reduce logistics enterprises’ operation costs by determine the reasonable vehicle route planning. Using Saving algorithm to construct the route of customer visit to avoid the limitation of commercial solver in large problem and using optimization model to optimal set of routes. The solution also confirms by multiple testing.
DOI: 10.14457/tu.the.2021.585
title: Canned fruit brix prediction using machine learning using deep neural network

description: Fruit grading procedures are delicate processes that require time, skills, and high investment. This is due to the grading process that needs appropriate equipment to measure the specific quality of the fruits, which is the key factor in deciding whether the fruit is of high quality or not. Therefore, to minimize these requirements, this paper proposes an artificial neural network model to help in predicting the Brix value of the fruit from numerical parameters. The main objective of this paper is to be able to accurately predict the Brix value.
DOI: 10.14457/tu.the.2021.586
title: Using design of experiments during the process of tuning hyperparameters in machine learning algorithms

description: Hyperparameter tuning is a very important process to maximize the performance of any machine learning model. Currently, there is still no general agreement on how to fine tune hyperparameter. In this study, Design of Experiment (DOE) is proposed as a methodology for the process of hyperparameters tuning in machine learning algorithm. A dataset from lime-sorting computer vision model using Convolutional Neural Network (CNN) are used in this study. Five hyperparameters of the CNN model were focused on this research, including Batch size, Epoch, Learning rate, Decaying rate, and Momentum. The performance of the CNN model is observed from the weighted average validate F1-score which is used as a response variable in our experimental design. Sequential experimentation is used in hyperparameter tuning process. A factorial design was performed to screen out unimportant factors. The fitted model is then used to determine a path of steepest ascent. Then, a Central Composite Design (CCD) was performed to determine the best settings of the hyperparameters of every model. A methodology and discussion are detailed throughout the paper. Using design of experiments to tune hyperparameters not only helps screening out the unimportant factors in the initial step of experimentation, but it also considered the importance of the main effects and their interactions, produce fewer training runs, and help decrease the complexity of machine learning model.
DOI: 10.14457/tu.the.2021.587
title: Classifying saleability of lime by using convolutional neural network (CNN) approach

description: Nowadays, there are many applications that have been used in agriculture field to facilitate human workers. Many researches have been focused on fruits grading based on appearance features. Since lime is one of Thai economic crop that have high demand and price all the year, a lot of limes are continually exported into the market. The appearance of limes strongly impact the market value. So, the quality inspection is needed to avoid selling bad limes to customers which cause greatly affects to the customer base. Determining saleability of lime can be done by using humans or machines. Using human workers is time-consuming, high cost and not accurate. Although there are fruit grading machines, they are expensive and difficult to reach for general farmers. This research has proposed method to classify saleability of lime into three categories which are buy, maybe, and not buy by using Convolutional Neural Network (CNN) because it is a deep neural network that has high performance in terms of images classification tasks. Hyperparameters tuning process has been used to search for the best model performance by using full factorial design. The hyperparameters that have been considered in the model are Epoch, Learning Rate, Decaying Rate and Momentum. Two experiments of hyperparameters tuning were constructed. The first experiment constructed by initial 2^5 full factorial by setting low and high value. After that, the result from first experiment was analysed by using Minitab to remove unimportant factors. The model performance in this research has been evaluated by F1-score which the first experiment gave the highest results as 76%, 79%, 80% in training model, 73%, 77%, 77% in validation and 74%, 77%, 77% of testing for buy, maybe and not buy respectively.
DOI: 10.14457/tu.the.2021.588
title: Factors that affecting food delivery application usage of the customer during COVID-19 situation

description: This research aims to study customer’s intention to use food delivery applications (FDAs) of Foodpanda, Grab Food, Happy Fresh, Line Man, and Robinhood food delivery services during the COVID-19 outbreaks in Thailand, the purpose of this research was to study into the factors that influence FDAs usage. Through an online questionnaire survey, a total of 536 genuine responses were collected. Data were analyzed by using Minitab, a statistical software program. The findings showed the elements that have a substantial influence on customer’s intention to use of each food delivery application and FDAs can use these results as opportunities for business performance improvement.
DOI: 10.14457/tu.the.2021.589
title: Willingness to pay for products on online shopping platform such as Shopee in Thailand

description: A user's willingness to pay (WTP) on online platform such as Lazada and Shopee has received a great deal of attentions in recent years. The current study is about the factors that affect a user’s willingness to pay and introduce a model on which factor that has the most influence on willingness to pay. We use design of experiment with fractional factorial design as main methods of data analysis which will be analyzed using Minitab program. According to 6 factor that we provide in this model, we find that online shop with high review score and customer score had most influence on customers’ willingness to pay compared to other factors. Furthermore, regarding Shopee Mall, which is the second most influential factor, it is beneficial to be a part of Shopee Mall because based on our research sellers will have higher chances of selling their product than without it. The user's interactions with online connections plays a significant role on having positive effects on WTP. This research contributes several new findings to WTP and provides insights of how the user interact with the internet.
DOI: 10.14457/tu.the.2021.590
title: The effect of government policy on the battery electric vehicle demand

description: The electric vehicle trend is rising high around the world. Many people have selected battery electric vehicles (BEVs) as their first choice, but Thailand did not get that much attention. In 2020, BEVs took around 54.3% of the first car market increase from 2019 by 11.9% (Norway first country to see electric cars comprise 50% of net vehicle sales, 2022). So, this study examines any factors that impact BEVs and their impact on each factor. This study aims to develop a system dynamics model to forecast the effect of Thai government policy on BEVs and use this model to conduct the policy analysis to increase battery electric vehicle users in Thailand. The purpose of this study is to examine the effect of Thai government policy on electric vehicle demand in Thailand. To analyze the scenarios of which policies should be lunch together and suitable for Thai government situations. The main procedure for forecasting the trend of electric vehicles includes gathering data sources, validating key factors, and system dynamics modeling approach to model the trend of BEVs usage. Furthermore, the sensitivity analysis of three key factors was also conducted.
DOI: 10.14457/tu.the.2021.591
title: Capacity building for digitalization of manufacturing industries toward industry 4.0 in Thailand

description: Manufacturing is the most important sector, contributing the most GDP in Thailand. The organizations are trying to develop to industry 4.0 through technology adoption. Capability building is one of the key factors companies invest in to step up in industry 4.0. The study’s objective is to investigate the key factors for collaboration between the companies and academic sectors, including the participation of the government. The three outstanding large companies related to digitalization were selected to be the case study of this paper. Data collection is interpreted by literature review and secondary case study and then analyzed by the thematic analysis of focus point and key success model. The detail of the result of the analysis is broken down by the focus point of the case factor between sectors of each case study.
DOI: 10.6084/m9.figshare.21103021
title: Data and software for "Inequality and fairness with heterogeneous endowments"

description: Anonymized data and Python and R scripts for the article "Inequality and fairness with heterogeneous endowments" published in PLoS ONE.
DOI: 10.14457/tu.the.2021.592
title: Inventory optimization with (S, s) policy and backlog under stochastic discrete demand and lead time

description: This paper presents the simulation sheet and python code for optimizing the total cost with real data of demand and information factors of product. Python code has been used as a tool to automate the simulation sheet reduced unnecessary time if do it manually. The way that used to optimized are cyclic coordinate search in three ways, horizontal, vertical, and diagonal, because the result shows that the optimal cost will shows in the diagonal line. The findings of this study will help to determine inventory levels and annual costs in warehouse environments, allowing for more complicated and realistic investigations in warehouse systems.
DOI: 10.14457/tu.the.2021.593
title: Multi-objective optimization for green supply chain network design problem

description: The rising levels of industrialization in developed countries, combined with globalization trends, have created new challenges to supply chain management. In this paper, a green supply chain network design approach is proposed to deal with the trade-offs between environmental and financial issues in order to reduce the negative environmental impacts caused by increasing levels of industrialization. When constructing the supply chain, the new approach integrates a supply chain network design and a multi-objective optimization mathematical model to maximize total profit and minimize carbon dioxide emissions. There are five model created to solve this problem which are minimizing weighted percentage deviation from goal method (non-fuzzy and fuzzy model), maximizing minimum satisfaction method (non-fuzzy and fuzzy model) and minimizing maximum deviation from satisfaction target method. The numerical illustration is simulated from the real data such as the location of facility and customer, selling price, cost, and carbon emission from transportation and production. The proposed mathematical model can illustrate the trade-offs between TP and TCOE, assuming a different transportation mode network. The results of this study show the pros and cons of each method and results allowed to identify the capability of the model to deal with the trade-offs between the profit and environmental issues as well as to identify its main limitation when addressing real world problems.
DOI: 10.14457/tu.the.2021.594
title: The expansion of the EV charging infrastructure network in Thailand

description: This article forecast the expansion of Thailand's electric vehicle (EVAT) charging infrastructure network. This research aimed to examine the critical factors that affect the expansion of the charging infrastructure network and find the recommendation for the policies to use in the actual situation. The study found that the critical factors that affect the number of charging infrastructures are the number of EVs, major and minor company investments, and the operating economy. The main procedure for forecasting the expansion of charging infrastructures includes gathering data sources, validating key factors, and using the system dynamics modeling approach to simulate the forecast result of the charging infrastructure. Furthermore, the sensitivity analysis is conducted for model validation and to see how the different scenarios affect the forecast result.
DOI: 10.14457/tu.the.2021.595
title: Causes and improvement of non-conformances founded during internal process audit in PCB factory

description: Process auditing is important in manufacturing control and factory management. PCB factories are one of those businesses that need appropriate work orders and labor allocated. The PCB factories have about 40 departments. Most of the problems in PCB factories are those that arise during the production process. The research aims to reduce all problems that arise during the manufacturing process. All the research will begin with an examination of the problems arising within the internal audit process, and it will decide to come up with a solution to all the problems by collecting all the problems that occurred in 1 year and categorizing them into categories. The severity score of each problem will calculate by using FMEA analysis. After that, the problem with the highest score will calculate for cause and effect by using a fishbone diagram. Fuzzy relation diagrams will be constructed to see how difficult it is to prevent problems from occurring again and determine which causes and solutions are the most effective. Finally, we will use the solutions we have calculated from the fuzzy relation diagrams to consider which solution methods are ready to be solved and which one must wait for a board of directors' approval to be solved.
DOI: 10.14457/tu.the.2021.596
title: Market basket analysis of drug store in seasonal period

description: One of challenges for a retail store that invested in data collecting method is how to use that data with important information from databases to gain a competitive advantage. To set up a store both online and offline company needs to plan how to do a product arrangement or how to do marketing activity. In this research is a study of Market Basket Analysis is the one of data mining technique, MBA approach to determine which product are purchased together by customer in other words, if customer buys a product, what is the probability that they will buy another product in same basket. In this research use a data of drug store from 2018 to 2020 and grouping data together in seasonal period to see customer behavior in each season that how are they buy product in same basket together. Research use Rapid Miner Studio for analysis of item set. Theobjective of market basket analysis is to use information from analysis to generate more volume of sale of company by crate a promotion, cross sale, product recommendation,position to put a product together and etc. This research got 5 recommendation of item set which are H034&H003 (Turmeric capsules (general medicine) & Cissus quadrangularis L. Capsules), H034&H035 (Turmeric capsules (general medicine) & Makhampom Makhom (General Medicine) 120 ml.), H034&H009 (Turmeric capsules (general medicine) & Momordica charantia capsule), H034&H036 (Turmeric capsules (general medicine) & Capsule Fah Talai Throne (General)), H035&H003 (Makhampom Makhom (General Medicine) 120 ml & Cissus quadrangularis L. Capsules) all of item set are wining set that most occur in every season, researcher recommend company to do marketing activity on all of this set of product.