cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2019.1446
title: Flood resilient supply chain network design

description: Modern supply chain is vastly expanded due to globalization and businesses evolving beyond countries and borders. Due to this nature of strong expansion in global supply chain, a failure in one supply chain component could cost millions of dollars. On the other hand, the global supply chain is more vulnerable to natural disasters more than ever with the infrastructural developments being carried out in the last few decades. A case of a small node failure could affect a large supply chain failure if the supplies are not managed properly during the time of failure. These few trends are the main reasons why many modern supply chain designers are focusing more and more on making the supply chain more resilient. More diverse supply chain networks are being designed around the world to ensure that the supply chain functioning is uninterrupted despite various nodes facing disruptions. Resilience in supply chain consists of two main strategies. Firstly, each and every organization in a global supply chain needs to minimize the risk of being disrupted by natural disasters and secondly, if an organization or a supply chain had to face the sequences of a disruption, it should have the ability to recover and get back to the normal working state as soon as possible with the smallest possible expense. “Resilience Triangle” is a concept that is used by many researchers as a way of understanding supply chain performance when faced with a disruption. Resilience triangle categorizes the performance of a supply chain into few phases and the supply chain analysts can consider each phase and the supply chain parameters related to each phase in order to uplift the performance. Basically this understanding helps the organization to ensure that they face the least damage if the organization is hit by a disruption and to recover in the quickest and best possible manner after facing the disruption. To analyze the potential risks to supply chain nodes, probability of failure can be utilized. The failure probability of each node can be connected to its geographical parameters when it comes to facing natural disasters. Another key supply chain decision in disruption management is the allocation of resilience budgets. This helps the system to accelerate the immediate response after facing a disruption. To analyze these parameters, building mathematical models is important. With the information gained from a mathematical analysis, the supply chain managers can directly make important supply chain decisions. Qualitative analyses too can only be evaluated if quantitative analyses are performed. Results from the quantitative analysis can be used to perform various further analyses such as comparing how the supply chain decisions change when resilience taken into consideration and not taken into consideration, how those parameters behave when the period of the disruption changes and the amount of budget.
DOI: 10.14457/tu.the.2019.1447
title: Meta-prediction models for bullwhip effect prediction of a supply chain using regression analysis

description: Supply chain systems have one common problem, the “bullwhip effect and stock amplification”. It generates large costs and losses for companies in the chain. In this study, the main factors that can cause the bullwhip effect and stock amplification are investigated using a simulation-based optimization approach and regression analysis. Both single and two-echelon supply chains, consisting of a manufacturer and a retailer with uncertain customer demand and delivery lead time operating with the periodic-review reorder cycle policy, are studied. The parameters of the smoothing inventory replenishment and forecasting methods, leading to an optimal ordering quantity for each order in each review period, are required. These parameters are optimized in terms of minimizing the Total Stage Variance Ratios (TSVRs) of all echelons by using the OptQuest optimization tool. The results show that even though all factors of interest have an impact on the bullwhip effect, using smoothing proportional controllers can reduce TSVRs. TSVRs are the sum of the Order Variance Ratio (OVR) and Net Stock Amplification (NSA). A chain operating under the centralized controlling policy has lower TSVRs, as compared to a chain operating under the decentralized controlling policy. These meta-prediction models can effectively help predict the amount of the bullwhip effect of a chain under various situations with an average MAPE of less than 11%. The results can assist decision makers in the management of a supply chain, to realize, benchmark with the optimal results, and reduce the TSVRs under an uncertain environment.
DOI: 10.14457/tu.the.2019.1448
title: A Markovian approach for optimizing A (T, S) inventory system under stochastic discrete demand and lead time with lost sales

description: This study focuses on a stochastic inventory control problem of the order-up-to-level periodic review system. The inventory status is tracked every regularly fixed period and replaces an order to raise the inventory level up to a predefined point. Unsatisfied customers’ demand is considered as totally lost. Warehouse’s capacity is also taken into account, while excessive units are stored in a rented warehouse. The replenishment lead time is considered as discrete and stochastic. To study this stochastic inventory problem, where demand is modeled using an empirical distribution, a Markovian approach is proposed to handle uncertainties. By using this Markov Chain, accurate approximations of system measures of performance, i.e., the expected inventory on-hand, expected shortage, and expected over-storage amounts can be obtained. The optimal solution for the Markov model is obtained by using a customized Golden Section Search procedure. The algorithm is applied to a real capsule-type medicine item to determine the optimal review period and target level. Then, the effectiveness of the Markov approach is demonstrated through the comparison between our proposed model and the simulation. As a result, the Markov model can describe the behavior of the order-up-to-level periodic review system.
DOI: 10.14457/tu.the.2019.1449
title: Framework to determine factors affecting the willingness to pay for electric vehicle conversion (EVC)

description: The transport sector is one of the main contributors to carbon dioxide emission in Thailand. This is because of the continuous increase in the utilization of gasoline. Government industrial agencies have acknowledged the importance of reducing carbon dioxide emissions. Consequently, the automotive industry has been developing an advanced technology that can release less carbon dioxide; one such technology is the development of an electric vehicle (EV) that uses electric energy instead of gasoline. However, electric vehicles have not received much interest from consumers in Thailand, as they are usually more expensive than regular gasoline cars when compared to cars of the same size. Recently, the Electricity Generating Authority of Thailand (EGAT) and the National Science and Technology Development Agency (NSTDA) have jointly collaborated to develop electric vehicle conversion (EVC). It is a car modification that converts an existing non-EV vehicle to be an EV. The conversion cost is about 200,000 Thai Baht (excluding battery). The existing functions in an old car such as the air-conditioner, radio, and electric systems are maintained in the conversion process. However, EVC is still in the development phase, and a plan to commercialize EVC has not been developed. The objective of this research is focused on determining significant factors affecting the willingness to pay (WTP) for EVC. A questionnaire survey was developed and distributed randomly to 406 people at Bangkok International Motor Show 2019 event in Thailand. The descriptive results show that approximately 69% of respondents are willing to convert a regular gasoline vehicle to an EV, the average price that they are willingness to pay for conversion is about 160,931 Baht. The results of binary logistic regression analysis and multiple regression indicated that, the factors that the factors that affect a customer's willingness to pay are monthly income, education, household size, number of owned cars, maintenance cost, age of the owned car, driving distance per day, environmental awareness, and environmental behavior. Some recommendations are discussed.
DOI: 10.14457/tu.the.2019.1450
title: Comparison of various pretreatment technologies on enhancing sugar yields from sugarcane trash

description: As a feedstock in a biorefinery, sugarcane trash (leaves and tops) can be converted to monosaccharides by enzymatic hydrolysis and fermentation. Pretreatment is required to increase the efficiency of enzymatic hydrolysis. Five well-known pretreatments were investigated with varying catalyst dosage: dilute acid (ACD), alkaline sodium hydroxide (ALK-NaOH), alkaline hydrogen peroxide (ALK-H2O2), Liquid hot water (LHW), and organosolv (ORG). Due to limited data on the pretreatment of sugarcane trash reported so far, specificity of different pretreatment methods on local sugarcane trash have been explored to evaluate energy efficiency, waste generation and economic cost. The effects of different pretreatment on efficiency and specificity of the pretreatment process were studied with varying process parameters (temperature, time, and catalyst dosage). The best glucose recovery yields were 70.13%, 86.89, 87.57%, 90.90%, and 92.33% achieved with ACD, ORG, LHW, ALK-NaOH, and ALK-H2O2, respectively. This was based on the available glucose in the raw material after enzymatic hydrolysis using 20 filter paper units (20 FPU)/g of Cellic®CTec2. The specific energy consumption was 0.148, 0.262, 0.318, 0.409, and 0.558 kg of sugar recovery/kWh, in which the maximum efficiency was achieved with LHW process, followed by ALK-H2O2, ALK-NaOH, ORG, and ACD. The waste generation was 0.037, 0.197, 0.375, 1.191, and 1.527 based on kg of waste generation/kg of sugar recovery. The waste generation was highest for ALK-NaOH process, followed by ALK-H2O2, ACD, ORG, and LHW. The environmental economic impact was 10.11, 19.37, 25.61, 26.74, and 27.25 baht of glucose/baht of wastewater treatment cost. The environmental economic impact was the highest with ALK-H2O2, followed by ALK-NaOH, ORG, LHW, and ACD. The alkaline hydrogen peroxide (4%w/w H2O2) at pH 11.5 with 6 h reaction time showed the best environmental economic impact (27.25 baht of glucose/baht of waste treatment cost) and sugar recovery yield (73.32%). Moreover, this pretreatment shows acceptable energy efficiency (0.223 kg/kWh) and acceptable amount of waste generation (1.191 kg waste/kg sugar recovery).
DOI: 10.14457/tu.the.2019.1451
title: Designing adaptive controller for boat’s anchorless application

description: The anchorless application is a part of the dynamic positioning control for maintaining the position and heading of the surface vehicle at the desired position without a traditional anchor method. The anchorless application provides automatically maintaining position and heading based on environmental concerns, such as wind, current or water flow, and wave. Generally, the anchorless application is integrated into the dynamic positioning system, which is operated by using a Proportional-Integral-Derivative (PID) controller. The PID controller still provided insufficient, maintaining the position and heading of surface vehicles. Then, this study tries to implement the adaptive control technique for improving the performance of the dynamic positioning controller. A feedback error learning technique is an adaptive control technique based on the biology of the human brain. The FEL technique applies to utilize with the nonlinear complex system, which is found the real parameter of the system model precisely. The goal of this study is to design a controller to improve the performance of a maintained position and heading of the surface vehicle based on the FEL technique. The proposed controller was designed for the nonlinear CybershipII model with MATLAB/Simulink program. The scenario of simulation is assumed under the calm water sea state. The proposed controller scheme consists of both controller as a feedback PID controller and a feedforward controller based on FEL. And also, the observer is integrated into the proposed controller scheme. The proposed controller scheme can provide a faster rising time and the settling time of the system in both directions of motion (surge and sway directions) based on the Cybership II model. Besides, the proposed controller can reduce the position error based on the assumed scenario due to the calm water sea state. Moreover, the observer can eliminate the disturbances, which are generated from the white noise toolbox under the clam water sea state.
DOI: 10.14457/tu.the.2019.1452
title: Effects of magnetic inducement on Cu-Zn/CeO2-doped Al2O3 catalyst for methanol steam reforming

description: The active Cu-Zn catalyst can be developed for H2 production from methanol steam reforming (MSR) at low temperatures. Here the Cu-Zn catalyst is developed through support modification and enhancing dispersion of active metals on the catalyst support. The conventional γ-Al2O3 support is modified by partial doping of Ce into Al2O3 framework. Then magnetic inducement is applied during the sol-gel preparation of CeO2-doped Al2O3 support. A good Cu-Zn catalyst for MSR requires Cu and Zn cluster to be deposited together to provide Cu+ which is the perfect active site for MSR. The magnetic inducement is introduced during sol-gel preparation of catalyst by applying different magnetic pole arrangements (N-N, N-S, and without magnetic inducement) to investigate their ability to disperse and control Cu and Zn deposition. The catalytic activity for H2 productions are evaluated at the temperatures of 200 °C– 300 °C in continuous mode. The catalysts and supports are analyzed through SEM-EDS, XRD, XPS, and BET surface area to determine the Cu-Zn dispersion over supports and Ce dispersion on the CeO2-doped Al2O3 supports. The results show that the combination of partial doping of Ce into Al2O3 framework and magnetic inducement during support preparation process improves the hydrogen production rate. And also the combined application of magnetic pole arrangement during the sol-gel preparation of CeO2-doped Al2O3 support and Cu-Zn metal loading on the support, and enhances the catalytic activity of prepared catalysts compared to the catalysts prepared under separate application of magnetic inducements. Further, the concentration profiles of Cu2+ and Zn2+ ions on α-Al2O3 support show that irrespective of the magnetic pole arrangement, magnetic inducement is capable of providing an external driving force for the ions to be dispersed homogeneously on any support.
DOI: 10.14457/tu.the.2019.1453
title: A light-weight deep convolutional neural network for speech emotion recognition using mel-spectrograms

description: At present, speech emotion recognition is a challenging field of studies about emotions of speakers. Speech emotion recognition also enhances interaction between humans and machines. In many situations (e.g. embedded systems), we have to detect emotion in speech within limitation of both computing and memory resources. Even several previous works reported that a reasonable recognition rate can be achieved using transfer learning techniques with popular models, such as AlexNet, they suffered with a large model size and cannot be executed on an embedded system. To address this problem, we propose a lightweight deep convolutional neural network architecture, which utilizes only partial components of the AlexNet with log Mel-Spectrograms as input. Our result shows that the proposed lightweight model can achieve a comparable recognition rate with the state of the art, but the model size is reduced from 233 MB, the original AlexNet, to only 3.5 MB.
DOI: 10.14457/tu.the.2019.1454
title: Hybrid simulation-based optimization for production planning of a dedicated remanufacturing system

description: This study presents a comparative simulation study of a dedicated remanufacturing system using a hybrid simulation-based optimization approach. This remanufacturing system performs various processes to transform used products into as-new products. In this study, the original production line of this dedicated remanufacturing system is shared with multiple products and has a limited space to accommodate arriving used products. Therefore, an appropriate inventory capacity should be set and a proper switching rule should be introduced to set up the production line. Otherwise, excessive line switching time and cost would be incurred. One objective of this study is to introduce to sequentially improve and suggest a method to optimize the production planning of this dedicated remanufacturing system under uncertain conditions, i.e., uncontrollable product arrival and stochastic operational time. Then, a hybrid simulation-based optimization method is applied as an improvement on the simulation-based optimization model with OptQuest. The hybrid method with two hybrid optimization forms using a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as complementary to each other. A case study is used to demonstrate, identify possible solutions, and compare the performances among the algorithms to show the advantages of the proposed algorithms. This approach can assist in decision making for the planning and management of dedicated remanufacturing systems that are required to operate with various decision variables under uncertainties.
DOI: 10.15468/kt6uca
title: A New Stonefly From Lebanon, Leuctra Cedrus Sp. N. (Plecoptera: Leuctridae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Vinçon, Gilles, Dia, Aref, Sivec, Ignac (2014): A New Stonefly From Lebanon, Leuctra Cedrus Sp. N. (Plecoptera: Leuctridae). Illiesia 10 (1): 1-5, DOI: http://doi.org/10.5281/zenodo.4761087
DOI: 10.5281/zenodo.4765126
title: The Views of Public Service Managers on the Implementation of National Health Insurance in Primary Care

description: <strong>Abstract</strong> <strong>Background</strong> The South African government is implementing National Health Insurance as a monopsony health care financing mechanism to drive the country towards Universal Health Coverage. Strategic purchasing, with separation of funder, purchaser and provider, underpins this initiative. The NHI plans contracting units for primary healthcare services to function as independent sub-district purchasers and District Health Management Offices to support and monitor these contracting units. This decentralised governance model to the operational unit of primary healthcare, the heartbeat of any universal healthcare system, is critical to programme success. The views of district-level managers, who are at the centre of the planned phased rollout will shed light on current policy implementation. <strong>Objectives</strong> This is a qualitative study to explore district and sub-district managerial views on National Health Insurance and its implementation.
DOI: 10.5281/zenodo.3537673
title: CLDF dataset derived from Yang's "Lalo Regional Varieties" from 2011

description: Cite the source dataset as Yang, Cathryn (2011): Lalo regional varieties: Phylogeny, dialectometry and sociolinguistics. Bundoora: La Trobe University.
DOI: 10.14457/tu.the.2020.66
title: Relation extraction from medical texts using semi­supervised learning

description: Information extraction and knowledge discovery from large­scale clinical texts are beneficial and needy processes. In dealing with text mining, text preprocessing is the additional task to transform unstructured textual data into a structured one. Fortunately, the traditional data mining process can further utilize this machine­readable format. Two significant difficulties of relation extraction are the lack of domain experts for labeling examples, exceptionally, in a large volume of unlabeled data, mainly clinical texts. This issue is well-known when it deals with the supervised learning method. Another challenge is the intractable processing of unstructured clinical texts.This dissertation, firstly, studies how to handle textual data in the large­scale texts. Generally speaking, the thesis aims to propose an automatic leveraging the existing knowledge to overcome shortcoming the lack of domain expert for data labeling. Secondly, the thesis proposes an efficient method for relation classification which aims to reveal semantic relation label of the given entity pairs from clinical texts. To this end, the dissertation presents a semi­supervised learning framework as a solution for semantic relation extraction from texts, herein, clinical textual data. Furthermore, the dissertation introduces efficient parameters estimation for a generative model that argues the traditional text assumption. This contribution can dramatically improve a model performance. Lastly, the thesis contributes to examine on multiple approaches of unlabeled data augmentation in order to be able to control large­scale of unlabeled data with the effectiveness.
DOI: 10.14457/tu.the.2019.1455
title: Disruption management in fashion supply chain

description: Countless supply chains in the Asian region become victims of frequent disasters due to unpredictable weather. This paper offers an optimization model that considers scenarios. Also the model contains a fortification budget that is limited. This fortification budget is used to fortify warehouses. Along with this the supply chain balances good transportation and inventory management throughout the supply chain.The experimental outcomes suggest that when we increase fortification budget can lessen the total supply chain costs in different degrees. Furthermore when analyzing multiple scenarios when making decisions related to the supply chain, makes the supply chain network cost effective. Also experimental results show that varying fashion season combinations does not make significant changes to overall supply chain costs and designs. Furthermore fortification of the model can be incorporated according to the necessary outcome of the model.
DOI: 10.5281/zenodo.3537636
title: CLDF dataset derived from Marrison's "Naga Languages of North-East India" from 1967

description: Cite the source dataset as Marrison, Geoffrey Edward (1967) : The classification of the Naga languages of north-east India. Berkeley : University of California
DOI: 10.5517/ccdc.csd.cc276d62
title: CCDC 2061723: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537
DOI: 10.5517/ccdc.csd.cc276d73
title: CCDC 2061724: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537
DOI: 10.5517/ccdc.csd.cc276d84
title: CCDC 2061725: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537
DOI: 10.5517/ccdc.csd.cc276d95
title: CCDC 2061726: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537
DOI: 10.5517/ccdc.csd.cc276db6
title: CCDC 2061727: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537
DOI: 10.5517/ccdc.csd.cc276dc7
title: CCDC 2061728: Experimental Crystal Structure Determination

description: Related Article: Jun Zhang, Wataru Kosaka, Hiroyasu Sato, Hitoshi Miyasaka|2021|J.Am.Chem.Soc.|143|7021|doi:10.1021/jacs.1c01537