cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.397jh4
title: Occurrence Download

description: A dataset containing 11965872 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is one of (United Kingdom of Great Britain and Northern Ireland, Finland, France, Sweden, Netherlands, Norway, Belgium, Germany, Switzerland, Austria, Spain, Italy, Portugal, Iceland, Denmark, Liechtenstein, Luxembourg, Poland, Czechia, Ireland, Estonia, Slovakia, Slovenia, Latvia, Lithuania, Hungary, Croatia, Greece, Serbia, North Macedonia, Albania, Montenegro, Kosovo, Bosnia and Herzegovina, Romania, Bulgaria, Belarus, Moldova, Republic of, Isle of Man, Andorra, Ukraine)",
 "Geometry POLYGON((-20 20,60 20,60 72,-20 72,-20 20))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (March, May, April, June, July, August, September)",
 "OccurrenceStatus is Present",
 "TaxonKey is Anseriformes",
 "Year 1950-2014"
 ]
}

 The dataset includes 11965872 records from 1439 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056972-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29064527
title: Raw Olink File

description: Lung adenocarcinoma (LUAD) is a biologically and clinically heterogeneous disease that poses a major challenge for prognosis and treatment. In this study, we performed proteomic profiling in a cohort of 88 LUAD patients to identify molecular subgroups and investigate their clinical relevance. Unsupervised clustering of the proteomic data allowed us to identify two distinct patient groups with different demographic, clinical, and molecular characteristics. Cluster 1 consisted predominantly of older patients and showed increased expression of immune and inflammatory pathways, including significant enrichment of TNF and Toll-like receptor signaling. This suggests a stronger innate immune response that may be associated with better disease control. In contrast, cluster 2 was characterized by younger demographics, a higher proportion of female patients, and a greater frequency of smoking. This cluster showed less activation of immune-related pathways and a significantly shorter time to disease recurrence, suggesting a more aggressive clinical course and poorer prognosis. The differential expression of immune pathways between clusters underscores the role of the tumor microenvironment in disease progression and response to treatment. Our results demonstrate the value of integrating proteomic and clinical data to identify biologically distinct LUAD subtypes. This molecular stratification can improve the understanding of tumor behavior and inform personalized treatment strategies. Thus, proteomic profiling is a promising tool to guide biomarker-directed treatment of lung adenocarcinoma.
DOI: 10.6084/m9.figshare.29064527.v1
title: Raw Olink File

description: Lung adenocarcinoma (LUAD) is a biologically and clinically heterogeneous disease that poses a major challenge for prognosis and treatment. In this study, we performed proteomic profiling in a cohort of 88 LUAD patients to identify molecular subgroups and investigate their clinical relevance. Unsupervised clustering of the proteomic data allowed us to identify two distinct patient groups with different demographic, clinical, and molecular characteristics. Cluster 1 consisted predominantly of older patients and showed increased expression of immune and inflammatory pathways, including significant enrichment of TNF and Toll-like receptor signaling. This suggests a stronger innate immune response that may be associated with better disease control. In contrast, cluster 2 was characterized by younger demographics, a higher proportion of female patients, and a greater frequency of smoking. This cluster showed less activation of immune-related pathways and a significantly shorter time to disease recurrence, suggesting a more aggressive clinical course and poorer prognosis. The differential expression of immune pathways between clusters underscores the role of the tumor microenvironment in disease progression and response to treatment. Our results demonstrate the value of integrating proteomic and clinical data to identify biologically distinct LUAD subtypes. This molecular stratification can improve the understanding of tumor behavior and inform personalized treatment strategies. Thus, proteomic profiling is a promising tool to guide biomarker-directed treatment of lung adenocarcinoma.
DOI: 10.15468/dl.e26hjx
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "StateProvince is 甘肃",
 "TaxonKey is Dioscorea opposita"
 ]
}

 The dataset includes 70 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025201-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gpjszp
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-30 81, 50 81, 50 12, -30 12, -30 81))",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Living Specimen, Occurrence evidence)",
 "TaxonKey is one of (Laophonte cornuta)"
 ]
}

 The dataset includes 21 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025202-250827131500795/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.2024.535
title: Fertilizer price forecasting using machine learning: a case study of rice fertilizer in Thailand

description: This study forecasts fertilizer prices using Decision Tree (DT), Artificial Neural Network (ANN), and Ensemble Models. Data collected from 2020 to 2023 was used to forecast and compare the accuracy between the predicted results and the actual values and each model. The forecasts from 2020 to 2023 serve as a basis for decision-making regarding the selection of appropriate data for future fertilizer price prediction. The experimental results reveal that the Ensemble Model improves forecasting accuracy. Furthermore, the findings indicate that ANN outperforms the DT model in terms of prediction accuracy. This is due to ANN's superior ability to capture complex and nonlinear patterns in data. While DT models rely on simple rule-based splits and often suffer from overfitting, ANN models can generalize better, particularly when working with continuous and time series data. The layered structure of ANN allows it to learn intricate relationships and update its parameters through gradient-based optimization, making it more suitable for dynamic and uncertain environments such as fertilizer price forecasting. However, certain abnormal or unexpected conditions may still lead to forecasting inaccuracies.
DOI: 10.14457/tu.the.2024.536
title: Optimizing vehicle routing for perishable food logistics ensuring freshness and efficiency

description: The delivery of perishable food products poses unique challenges due to the critical need to preserve freshness while optimizing logistics efficiency. Traditional Vehicle Routing Problem (VRP) models primarily focus on minimizing distance or cost but often neglect time-sensitive deterioration, which is vital in cold chain logistics. This study develops an advanced Capacitated Vehicle Routing Problem with Route Time Windows (CVRPT) model that integrates a freshness decay function to optimize routing decisions based on minimizing product freshness loss. The model incorporates constraints such as vehicle capacity, route time limits, travel and unloading times, and uses a linearized freshness decay function for tractability. A case study based on a supermarket distribution scenario evaluates two routing objectives: minimizing total distance and minimizing freshness loss. Results demonstrate that the freshness-focused model significantly reduces overall product deterioration. However, this improvement comes with longer travel distances and more complex routing. This trade-off emphasizes the importance of balancing product quality preservation with operational efficiency. The findings offer a practical framework for improving perishable food logistics and lay the groundwork for further research using metaheuristic algorithms for large-scale applications.
DOI: 10.14457/tu.the.2024.537
title: An application of computer vision-based material classification and fragility risk assessment

description: This independent study explores the application of computer vision-based material classification with fragility risk assessment to enhance process efficiency in logistics and supply chain management. The proposed system leverages advanced computer vision model, YOLOv8-seg to accurately segment between three material types: ceramic, plastic, and metal. In addition to segmentation, the system assesses fragility using product shape, enabling proactive handling strategies to minimize damage during transportation and storage. By model material classification and fragility evaluation, the system reduces manual effort, minimizes errors, accelerates product sorting, and optimizes packaging decisions.
DOI: 10.14457/tu.the.2024.538
title: Optimization of vehicle routing with time windows for cold chain logistics using OpenSolver

description: Cold Chain Logistics (CCL) is a critical aspect of supply chain management for industries that handle temperature-sensitive products, especially aquatic products. However, challenges related to transportation cost, cluster infrastructure, and temperature control requirements must be addressed. This study developed a customized CVRPTW model to improve distribution network efficiency and minimize the cost by using OpenSolver. The model was evaluated using real-world data from a Thai cold chain logistics company. The results demonstrated that the model significantly reduced total costs, the number of trucks required, and routing for each truck. These findings highlight the potential of VRP models to optimize cold chain logistics.
DOI: 10.14457/tu.the.2024.539
title: Inventory management using (s,S) policy with backlog and constant lead time

description: Effective inventory management is crucial for supermarkets, especially in managing food products where maintaining availability and minimizing costs must becarefully balanced. This thesis explores the application and optimization of the (s,S)inventory policy, focusing on intermittent demand, constant lead times, and backlogscenarios common in supermarket operations. The research develops a mathematical model tailored to any goods, aiming to minimize total costs associated with holding, ordering, and stockouts under periodic review conditions.By integrating key factors such as order-up-to levels and constant lead times,the model offers a structured approach to addressing backlogs. The results providepractical insights into how supermarkets can enhance inventory optimization,improving service levels while reducing costs in complex supply chains. This studycontributes to the understanding of inventory management in retail environments with variable demand and emphasizes the importance of balancing cost-efficiency and product availability.
DOI: 10.14457/tu.the.2024.540
title: Sustainable packaging design for consumer products develop and test packaging made from environmentally friendly materials

description: Sustainable packaging is essential for reducing environmental impact,particularly in consumer product protection. This study focuses on the development and evaluation of impact-resistant, biodegradable packaging materials derived from sagostarch and hydrogel-based biopolymers. Two composite formulations wereinvestigated: Sago-Starch/Sodium Alginate and Sago-Starch/Kappa Carrageenan, both designed to offer enhanced mechanical properties while maintaining environmentalsustainability. The combination of sago starch, a renewable and biodegradablepolysaccharide, with hydrogel-based components provides a material with shock-absorbing capabilities, structural flexibility, and biodegradability, making it a potential alternative to conventional synthetic packaging. The formulated materials were tested for impact resistance, mechanical strength, and protective performance to assess their suitability as cushioning materials for consumer goods. Preliminary findings indicate that these bio-composites demonstrate resilience to impact, elasticity, and protective qualities, suggesting their potential for applications in eco-friendly packaging. Further research will focus on material optimization, durability enhancement, and life-cycle assessment to evaluate their commercial viability for sustainable packaging solutions.
DOI: 10.14457/tu.the.2024.541
title: Multi-echelon supply chain network design with warehouse location and transportation mode optimization for cost and emission reduction: a case study in Northeastern Region of Thailand

description: This study develops a Multi-Echelon supply chain network model integrating warehouse location and transportation mode optimization to minimize both costs and Greenhouse Gas Emissions. Focusing on a real-world case in Northeastern Thailand's cement distribution network, the research applies a Multi-Objective Linear Programming (MOLP) framework, using the Augmented ε-Constraint Method and preemptive goal programming to balance cost-efficiency with environmental sustainability. Further, to account for uncertainties in operational costs and emissions, a Fuzzy Multi-Objective Linear Programming (FMOLP) model is constructed, utilizing triangular fuzzy numbers and the Max–Min Satisfaction Method. Numerical experiments demonstrate that strategic warehouse placement and transport mode selection significantly influence both operational costs and environmental outcomes. The fuzzy model extension enhances decision-making under uncertainty, providing robust, resilient strategies for supply chain planners. This research offers practical insights for industries seeking to optimize logistics networks under sustainability goals while facing real-world variability.
DOI: 10.14457/tu.the.2024.542
title: Inventory optimization for slow-moving products: a case study in the materials handling and industrial equipment industry in Thailand

description: In every industrial, warehousing, and distribution operation, materials handling equipment is considered as a critical component in assisting safe and efficient movement of goods, from raw materials, work-in-progress, to finished products of various shapes and sizes. Although crucial in nature, this equipment is generally considered as an afterthought, and funding for procurement of such equipment would be cut when a company prioritizes cost-saving measures. As companies are struggling to re-emerge from the aftermath of the pandemic, a massive influx of foreign competition caused a massive shock in terms of competitiveness for the existing local businesses. As such, local companies resorted to cost-cutting measures to remain relevant in the market. However, this caused a ripple effect to the existing inventory forecast methods, as demand for certain materials handling and industrial equipment has seen a plunge and began to excessively accumulate which in turn affecting inventory holding costs, possible damage from long-term storage, and obsolescence. Quick measures, such as immediate price cuts and generous payment terms to incentivize customers have seen minimal success, as demand for industrial goods is not as elastic as consumer goods. This study explores the dynamics of the materials handling and industrial equipment market, existing inventory data from the company in such market and the possibility of using optimization models to assist with the optimization of such inventory.
DOI: 10.14457/tu.the.2024.543
title: A simplified approach: base stock policy implementation in general toy supply chains

description: The toy retail industry faces significant challenges due to high demand uncertainty and short product life cycles. Traditional inventory management methods often lead to stockouts, resulting in lost sales, or overstocking, which increases holding costs and potential obsolescence. Small to mid-sized enterprises (SMEs) in this sector frequently lack access to advanced inventory management systems.Despite extensive academic research on based-stock policies, there’s a notable gap in practical, accessible tools, specifically Excel-based simulations, that can serve as decision-support for toy retailers. Many analytical models are complex, and advanced simulation studies often require specialized software and expertise, limiting their adoption by managers.This project aims to bridge this gap by developing an accessible, simulation-based tool in Excel and as a web application. This primary goal is to help toy retailers optimize their inventory policy by minimizing total inventory costs, including ordering, and shortage costs. The project also seeks to obtain optimal cycle service level (CSL) through effective reorder point decisions and provide a low-cost decision support tool tailored for small to mid-sized retailers.
DOI: 10.14457/tu.the.2024.545
title: Development of a hybrid multisource soil moisture estimation framework (HMSMEF) using machine learning and remote sensing data

description: Accurate, high-resolution soil moisture data is essential for optimizing agricultural water use and supporting environmental monitoring, particularly in rice cultivation areas with complex terrain and spatiotemporal variability. This study aimsto develop a Hybrid Multisource Soil Moisture Estimation Framework (HMSMEF) that integrates advanced machine learning techniques with diverse remote sensing and auxiliary datasets to downscale SMAP soil moisture data to 10 m resolution. The framework synthesizes optical, radar, land use, precipitation, and topographic data through permutation testing of nine synergy combinations and applies three regression models: Random Forest (RF), Gradient Boosting (GB), and Classification and Regression Trees (CART). The optimal configuration—Synergy 6, incorporating Sentinel-1 SAR, CHIRPS precipitation, and SRTM topography—achieved the best results using the CART model (R² = 0.927, RMSE = 0.005). Bias correction methods improved R² to 0.936, and a stacking ensemble of CART, RF, and GB further enhanced accuracy. Validation with two in-situ datasets showed strong performance: (1) 2018–2019 field data (R² = 0.91, RMSE = 5.35) for 10–40 cm depth, with a widely sensor distance of 73 m; and (2) 2021–2023 green manure plots (R² = 0.67 untreated, R² = 0.60 treated), with dense sensor spacing (6.8 m), reflecting local variability. HMSMEF demonstrates a scalable and robust solution for high-resolution soil moisture estimation, with significant implications for precision agriculture, sustainable water management, and environmental modeling.
DOI: 10.15468/dl.t2dj6f
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Basella alba",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025205-250827131500795/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.2024.546
title: Digital technologies and circular economy as drivers for sustainability in the Thai metals industry: a qualitative study

description: Sustainability enhancement has received increased awareness in the Thai manufacturing sector. This sector needs to address the negative impact of external environmental factors such as pandemic disruptions. Thai manufacturing companies must adapt and improve their operations, considering sustainability impacts and business disruptions, to sustain their businesses. Implementing digital technologies and Circular Economy (CE) practices has a strong potential to help manufacturing companies improve their sustainability performance and better respond to business disruptions. This dissertation comprises two qualitative analyses within the context of the Thai metals industry. The first analysis focuses on the perspective of project management as the facilitator for the implementation of Industry 4.0 technologies and CE practices, aiding Sustainable Supply Chain (SSC) development. The second analysis examines the improvement of Triple Bottom Line (TBL) sustainability at the firm level. The implementation impact on TBL sustainability, emphasizing the TBL synergies and trade-offs, is also studied. A semi-structured interview approach is utilized to collect the primary data for both analyses. The outcomes of the first analysis provide insights into the benefits of project management and how Industry 4.0 technologies, CE practices, and SSC help address business disruptions caused by the COVID-19 pandemic. The first analysis also indicates that the pandemic disruptions motivate manufacturing companies to implement and utilize digital technologies for impact mitigation. The findings of the second analysis highlight the improvements in organizational structure and KPIs, contributing to TBL performance enhancement. The second analysis also shows that economic and environmental performance can be measured and analyzed in a more concrete manner, while there is still a need for an improved understanding of evaluating social impact. On the practical aspects, this study provides inputs that help companies plan for the implementation of digital technologies and CE practices to effectively address their disruption challenges, engage in developing SSC, and promote sustainability performance. This study can help raise companies’ awareness of long-term benefits achieved by using CE practices and digital technologies.
DOI: 10.15468/dl.8yd8j9
title: Occurrence Download

description: A dataset containing 2665 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Glaucidium brodiei",
 "Year 2000-2025"
 ]
}

 The dataset includes 2665 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025203-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wshsrz
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Basella alba",
 "Country is Equatorial Guinea",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025206-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrn3b6
title: Occurrence Download

description: A dataset containing 12763 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphibia",
 "Geometry POLYGON ((50.7597 -16.0878, 50.8081 -15.9401, 46.1382 -15.9401, 46.1382 -24.9401, 47.6434 -24.9401, 49.8893 -18.4615, 50.0065 -17.575, 50.4326 -17.1196, 50.4413 -16.4368, 50.7597 -16.0878))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)"
 ]
}

 The dataset includes 12763 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025204-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.