cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.cpypq6
title: Occurrence Download

description: A dataset containing 379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "PublishingOrg is ccd1ddc0-6c21-11de-8224-b8a03c50a862",
 "TaxonKey is Anas platyrhynchos Linnaeus, 1758"
 ]
}

 The dataset includes 379 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010964-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rr2hac
title: Occurrence Download

description: A dataset containing 1669 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microlophus theresioides (Donoso-Barros, 1966)"
 ]
}

 The dataset includes 1669 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010965-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ruxeks
title: Occurrence Download

description: A dataset containing 789761 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.81424 -0.0924,-78.73659 -0.0924,-78.73659 -0.02382,-78.81424 -0.02382,-78.81424 -0.0924))",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "Year 2000-2025"
 ]
}

 The dataset includes 789761 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010961-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dh5gqy
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Machine Observation, Material citation, Material sample)",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is one of (Presbytis fredericae (Sody, 1930), Presbytis comata fredericae (Sody, 1930))",
 "Year 1000-2025"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010968-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6e74qw
title: Occurrence Download

description: A dataset containing 594 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lardizabala Ruiz & Pav."
 ]
}

 The dataset includes 594 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010971-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2024.96
title: Digitizing manual assembly and operator training in industry 4.0: the role of inside-out tracking in augmented reality systems

description: The evolution of augmented reality (AR) technology is reshaping industrial engineering, particularly in manual assembly training. This dissertation explores the utilization of AR, focusing on inside-out tracking, as an innovative approach to digitizing and assessing manual assembly tasks. Inside-out tracking leverages sensors embedded within AR devices, such as cameras, accelerometers, and gyroscopes, to capture spatial information, which is then used to digitize the operator's actions and interactions. This approach contrasts with traditional outside-in tracking methods that require extensive external infrastructure, offering a more adaptable and scalable solution for training environments. The research develops a comprehensive framework for integrating AR technology into the digitization process within industrial settings. This framework captures, analyzes, and provides feedback on cognitive and motor skills critical to manual assembly. A comparative analysis of inside-out and outside-in tracking techniques is conducted to evaluate their effectiveness across various industrial scenarios, offering valuable insights into the optimal application of AR technology in different contexts. Central to the framework are the components of skill digitization, skill comparison, and feedback provision, all refined and enhanced through the integration of AR technology. The study employs advanced methodologies, including computer vision and machine learning, to ensure the accuracy and actionability of the digitized data. This allows for real-time, precise feedback that can significantly improve training outcomes. In addressing the practical challenges of implementing AR in industrial training, such as tracking accuracy, environmental adaptability, and interface design, the dissertation provides guidelines to facilitate the effective deployment of AR technology. These contributions align with the broader objectives of Industry 4.0, promoting the creation of more immersive, responsive, and scalable training systems. By enhancing the digitization process through AR technology, this research sets new standards in industrial training. It addresses existing limitations in manual assembly training and lays the groundwork for future innovations, contributing to the ongoing digital transformation of industrial engineering.
DOI: 10.14457/tu.the.2024.97
title: Study electrochromic of PEDOT:PSS with gold nanoparticles enhancement

description: This study explores the transformative potential of smart technologies, focusing on electrochromic mechanisms in smart windows. We investigate poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) enhanced with gold nanoparticles (AuNPs) to improve electrochromic performance. By integrating AuNPs into PEDOT:PSS films and treating them with lithium perchlorate (LiClO₄), we achieve significant optical transmission changes upon voltage application. Our method involves mixing and spin coating on ITO substrates, enabling real-time measurement of electrochromic properties. This research underscores the promise of advanced electrochromic materials for next-generation optoelectronic devices.
DOI: 10.14457/tu.the.2024.98
title: Enhanced polymeric guided-mode resonance sensor for volatile organic compound gas detector

description: The optical characteristics of polymers are improved by adding tungsten and titanium oxides, which are projected to raise the refraction value, offering greater resonance and sensing capacity to VOC vapor.The sensor was fabricated in a single step using a combination of spin coating and drop casting. The sensor properties were then characterized using FE-SEM, Ellipsometry, and an Interferometer. The sensing was tested using 2-propanol, Ethanol, Ammonia, Formaldehyde, and Water.The EDS energy mapping confirmed the presence of WO3 and TiO2 in the sample. The ellipsometry showed that the refractive index was enhanced from 1.50 to 1.53 and 1.54 by WO3 and TiO2 embedding, respectively. The resonance location at 566 nm for neat polymer and 576 nm for polymer composite confirms that the resonance was guided accordingly as simulated by RCWA. Using a polymer composite as a waveguide increases the vapor detection of formaldehyde and ammonia by 35%, reaching 0.98 nm and 1.18 nm of resonance shift, respectively.
DOI: 10.14457/tu.the.2024.99
title: Some classes of singular nonlinear elliptic equations

description: This thesis investigates the Lane-Emden type problem\[ \mathcal{L}u = \sum\limits_{i=1}^{m}\sigma_{i} u^{q_{i}}+\sigma_0, \quad u\geq0 \qquad \text{in} \quad \Omega, \quad\lim \limits_{x \rightarrow y} u(x) = f(y), \qquad y \in \partial^{\infty} \Omega,\]where $ \mathcal{L}u = - \text{div}(\mathcal{A} \nabla u)$ is a uniformly elliptic operator with bounded coefficients, $0 &lt; q_{i} &lt; 1$ with $i= 1,2,\dots,m$, each $\sigma_{i}$ is a nonnegative locally finite Borel measure on a regular domain $\Omega \subset \mathbb{R}^n$ possessing a positive Green function $G$ associated with the operator $\mathcal{L}$ and the boundary function $f \in \mathcal{C}^{+}({\partial^{\infty} \Omega})$. We establish the necessary and sufficient conditions for the existence of positive bounded solutions to the Lane-Emden type problem in the domain $\Omega$ involving several subnatural growth terms with the elliptic operator $\mathcal{L}$ and the fractional Laplacian $(- \Delta)^{\alpha}$ where $0 &lt; \alpha &lt; \frac{n}{2}$ on $\R^n$.As an application, we also find the same results for the continuous solutions to the sublinear elliptic equation\[ - \Delta u = \sum\limits_{i=1}^{m}\sigma_{i} u^{q_{i}}+\sigma_0, \quad u\geq0 \qquad \text{in} \quad \Omega, \quad \lim \limits_{x \rightarrow y} u(x) = f(y) \qquad y \in \partial^{\infty} \Omega.\]
DOI: 10.14457/tu.the.2024.100
title: The renaissance of “water-in-salt” electrolytes for energy storage devices

description: In recent years, there has been a growing focus on the demand for alternate electrolytes. There is a requirement to tackle the drawbacks of costly alternatives like lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in energy storage applications. This study explores a range of lithium salts (LiX) as potential substitutes, focusing on their suitability for "water-in-salt" electrolytes. By investigating electrolytes with concentrations varying from low to superconcentrated levels, the physical and electrochemical properties of LiX were thoroughly examined. Parameters such as viscosity, pH, conductivity, density, and temperature variations during mixing were meticulously evaluated to understand the behavior of LiX electrolytes.Electrochemical characterization using carbon-based materials in a three-electrode configuration and subsequent integration into coin cell supercapacitors provided insights into the performance of different LiX electrolytes. Notably, while highly concentrated LiX electrolytes, particularly LiCl, demonstrated the potential to expand the operating voltage window, challenges related to corrosion were identified, necessitating further mitigation strategies.Studies have also shown redox properties in LiBr and LiI electrolytes, with increased concentration observed to accelerate the redox process while maintaining voltage stability. LiNO3 becomes It is a potent alternative electrolyte. This is due to its potential being comparable to LiTFSI and its cost-effectiveness.Cost analysis shows that LiNO3 is a feasible option in terms of energy density. It has the potential for further optimization through the use of LiBr and LiI as redox enhancing electrolytes. This comprehensive review not only provides valuable insights into the physical and electrochemical characteristics of LiX electrolytes, but also highlights the potential of these electrolytes as alternatives. In the electrolyte system "Water in salt". Overall, this research contributes to improving our understanding of LiX electrolytes and leads to their practical use in next-generation energy storage devices. It offers a promising path towards cost-effective and efficient energy storage solutions.
DOI: 10.14457/tu.the.2024.101
title: Optimizing total cost for electric truck fleet considering weight impact on energy consumption

description: The increasing adoption of electric trucks (E-trucks) in logistics highlights their potential for reducing total transportation costs and advancing sustainability. This study investigates the impact of payload on energy consumption, revealing that an 850 kg increase in payload results in a 14% rise in energy use. A cost-optimization model assigns payloads to trucks while considering constraints such as demand, working hours, payload and battery capacities, and truck availability. The analysis covers 20 customer nodes within single-charge distances (20–80 km) and payloads ranging from 13 to 32 tons. The optimal solution yields a total transportation cost of 50,992.73 THB, comprising fixed costs of 11,627.57 THB and variable costs of 19,682.58 THB. Fuel costs dominate variable expenses (76.02%), followed by maintenance (11.89%) and toll costs. Deliveries require nine 19-ton trucks and one 50-ton truck, completing 32 trips across all nodes. Energy consumption patterns show that neglecting the payload effect can underestimate consumption by up to 272.65 kWh. The study finds that while 50-ton trucks incur higher energy and maintenance costs, they are indispensable for nodes requiring longer distances or higher payload capacities. Comparatively, E-trucks reduce variable costs by 51.39% (2) over diesel trucks, driven by 49.74% lower fuel costs and 40.10% lower maintenance expenses, achieving a payback period of approximately four years with 56.56% of IRR. These findings underscore the importance of optimizing payload assignments and integrating factors such as energy consumption, battery capacity, and charging infrastructure into transportation planning. By leveraging E-trucks, businesses can lower costs, enhance operational efficiency, and contribute to sustainability goals. 16.11% reduce carbon emission in transportation.
DOI: 10.14457/tu.the.2024.102
title: Automatic root cause analysis of network failure on IP/MPLS network using machine learning and case-based reasoning

description: The IP Multiprotocol Label Switching (IP/MPLS) network is a complex system comprising switches, routers, DWDM (Dense Wavelength Division Multiplexing) devices, Network Management System (NMS) servers, and various other components. Managing such large-scale networks requires multiple tools and advanced network management techniques. Due to the intricate architecture and interconnectivity of IP/MPLS, identifying and resolving network issues, particularly chain failures, is a challenging task. In chain failures, a single issue can cascade, affecting multiple interconnected devices. To address these issues, network operators rely on NMS, event or alarm signals from network devices, and frequently perform manual operational commands for further diagnostics. Given this complexity, a centralized approach is crucial for efficient network management. This article proposes a Multi-Purpose System that leverages Machine Learning and Case-Based Reasoning to enhance network operations and troubleshoot IP/MPLS networks. The system comprises several components: a Message Broker for real-time streaming of different message types (e.g., SNMP Traps, Syslog), a Log Template Generation service for standardizing logs, an Event Identification Service for classifying network events, a Node Chain Lookup Service for identifying impacted devices, and a Node Test Service for running diagnostic commands. Additionally, the system includes a Case-Based Fault Identification Service, which acts as a knowledge repository of historical fault cases and expert knowledge, a Notification Service for sending alerts through modern communication channels, and a Dashboard to provide network operators with root cause analysis and troubleshooting guidance. The proposed system aims to improve network availability and operational efficiency utilizing scenarios from the Provincial Electricity Authority of Thailand (PEA). We assessed its performance using event messages from various NMS. The results illustrate the system's efficacy regarding accuracy and performance, providing a reliable option for automating network troubleshooting and management.
DOI: 10.14457/tu.the.2024.103
title: Determining an optimal order-up to level on (T, S) policy by search methods with multi-item consumer product inventory

description: This research focuses on optimizing retail inventory for multi-consumer products using the periodic review/order-up-to policy, or (T, S) policy. It incorporates stochastic lead times, with demand estimated based on historical data. Since the inventory handles multiple items, storage space is limited, and shared space allocation is not used. The study aims to determine the optimal order-up-to level (S) for each product to minimize total annual cost (TAC).To find the optimal solution, the grid search method and the golden-section search method are automated through spreadsheet simulations using Macros in Microsoft Excel. Results indicate the optimal order-up-to level (S) and the corresponding nominal total annual cost (TAC) from both methods. The comparison reveals that the grid search method slightly outperforms the golden-section search due to the characteristics of the search technique.
DOI: 10.14457/tu.the.2024.104
title: An application of hybrid models combining regression tree and artificial neural network for demand forecasting: a case study of ceramic products

description: This case study aims to provide highly accurate demand forecasting by focusing on the most influential products. The models used include both Traditional Methods such as Weighted Moving Average (WMA), Double Exponential Smoothing (DES), Double Moving Average (DMA), and Simple Linear Regression (SLR) and Machine Learning methods, including Regression Tree, Artificial Neural Network (ANN), and a Hybrid Models combining Regression Tree and ANN with a Weighted Average Based on Error. The performance of each method will be evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to determine the best approach. This study will benefit ceramic manufacturing by developing and evaluating accurate demand forecasting models.
DOI: 10.14457/tu.the.2024.105
title: Hybrid support vector regression with clustering approach for Thailand short-term electricity load forecasting

description: Accurate forecasting of electricity load demand is crucial for effective management in the energy sector. However, due to the high level of uncertainty involved, achieving precise electricity load demand forecasts presents a significant challenge. Several factors, such as historical electricity demand, seasonal effects, day of the week, and holidays, influence the electricity. A combination clustering and an optimized support vector regression algorithm is proposed to improve short-term electricity load forecasting.The electricity load demand data is collected from the Electricity Generating Authority of Thailand (EGAT) and divided into two sets: a training set consisting of electricity load data from April 2019 to March 2020, and a testing set consisting of data from April 2020 to March 2021. The training dataset is used to apply K-Means clustering, which groups the electricity load demand based on their load patterns. For the testing set, a K-Nearest Neighbors classifier is employed to predict the cluster group label based on various features, including the day of the week, the month of the year, the holiday, and the bridging holiday.After clustering the training and testing datasets, the electricity load demand is predicted using Support Vector Regression with Bayesian optimization (SVR-BO). The proposed model is then compared with deep learning , manual classification based on days, and the load demand is forecasted using SVR-BO. The Mean Absolute Percentage Error (MAPE) metric measures forecasting accuracy.The proposed model in this study achieves remarkable performance in forecasting holiday load demand, except for the months of January and December. Moreover, the model outperforms manual classification in predicting weekday and weekend load demand. The main contributions of this research are integrating clustering and forecasting methods, applying K-Means clustering only on the training data, and using the K-Nearest Neighbors classifier to assign the correct cluster label based on relevant external factors for the testing data.
DOI: 10.14457/tu.the.2024.106
title: Economic and environmental values of electric vehicle batteries in Thailand utilizing system dynamics modeling and life cycle assessment approaches

description: The rapid global expansion of battery electric vehicles (BEVs) has led to an increase in retired EV batteries (REVBs), posing significant environmental challenges worldwide. Thailand, influenced by government incentives and technological advancements in EV batteries (EVBs), has seen a rise in BEV adoption, resulting in higher number of REVBs. Effective end-of-life (EOL) management strategies, including remanufacturing, repurposing, and recycling, are essential to mitigate environmental impacts and promote a circular economy. This research examines the environmental impacts and feasibility of EOL management of REVBs utilizing life cycle assessment (LCA) and system dynamics (SD) modeling approaches. The LCA method is utilized to assess the key environmental impacts associated with EOL management of REVB, including CO2eq emissions, human toxicity, terrestrial acidification, particulate matter formation, photochemical oxidant formation, water depletion, metal depletion, ozone depletion, and fossil fuel depletion. Normalized impact scores for different EOL management strategies are calculated in the LCA analysis and combined to achieve the final impact scores. The LCA results revealed that recycling process is the best EOL management process based on the environmental perspective. It gives the best final impact score of -279, presenting a significant reduction in the environmental impacts. The research also develops causal loop diagrams and an SD model to analyze the dynamic interactions among factors influencing EOL management. The benefits and costs, including environmental costs, are input in the SD model to select suitable EOL management strategies for implementation using the net present value (NPV) and internal rate of return (IRR). The simulation results revealed that the remanufacturing process is the most suitable EOL management process based on economic and environmental perspectives. It yields the highest IRR value of 45% in the next 30 years. Repurposing-only and remanufacturing plus repurposing strategies are also recommended for implementation as they yield the IRR values of 35 and 26% at the end of year 30. The sensitivity analysis was performed to validate the developed SD model and examine long-term strategies to enhance EOL management of REVBs in Thailand. The results suggest that the privilege parking, lithium reserve discovery rate, buyback cost, and human toxicity cost are major contributors to the feasibility of the EOL management project. The integration of these strategies promises an effective REVB management, reduces environmental footprints, and supports the sustainable growth of the Thai automotive sector in the long term.