cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2025.66
title: Curriculum learning and reward shaping for multi-robot navigation via deep reinforcement learning

description: Autonomous multi-robot navigation in unknown environments remains a challenging task, as robots must make real-time decisions under dynamic and partially observable conditions without relying on global information or inter-robot communication. In recent years, the use of mobile robots has significantly expanded across diverse fields, transforming automation and efficiency in applications such as manufacturing, warehousing, logistics, healthcare, and service industries. This study presents a deep reinforcement learning–based (DRL-based) framework for multi-robot navigation and exploration in unknown environments, addressing the limitations of traditional and hybrid path planning approaches that rely on predefined maps and then resulting high computational demands. The proposed framework employs the twin delayed deep deterministic policy gradient with prioritized experience replay (TD3-PER) to enhance learning efficiency and adaptability without requiring prior environmental information. To mitigate suboptimal navigation behaviors such as oscillations, stagnation, and local optima, a structured reward function was designed through reward shaping. Furthermore, a curriculum learning strategy was introduced to improve scalability and robustness as the number of robots increases, followed by a policy-switching mechanism that enables adaptive decision-making without inter-robot communication. Experimental results in a virtual environment show that the proposed framework reduces training time and improves success rate in various types of environments and scenarios. In exploration tasks, the switching model demonstrated the best balance between speed, stability, and fault tolerance. Overall, the findings establish that the integration of reinforcement learning, reward shaping, curriculum learning, and policy switching provides a scalable and robust foundation for multi-robot systems operating in unknown and dynamic environments.
DOI: 10.15468/dl.t62zzt
title: Occurrence Download

description: A dataset containing 13797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation, Occurrence evidence, Material sample)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Wasmannia auropunctata",
 "Year 1000-2025"
 ]
}

 The dataset includes 13797 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006772-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bs38q2
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.86251 24.502697, 88.785341 24.49712, 88.710092 24.480528, 88.638633 24.453333, 88.572739 24.416212, 88.51404100000001 24.370086, 88.463987 24.3161, 88.423806 24.255593, 88.39447800000001 24.190063, 88.376711 24.121128, 88.370925 24.050491, 88.377242 23.979892, 88.395489 23.911067, 88.425197 23.845708, 88.465622 23.785417, 88.51576 23.731672, 88.57437400000001 23.685786, 88.640024 23.64888, 88.711102 23.621857, 88.785872 23.605375, 88.86251 23.599836, 88.939148 23.605375, 89.013918 23.621857, 89.084996 23.64888, 89.15064599999999 23.685786, 89.20926 23.731672, 89.259398 23.785417, 89.299823 23.845708, 89.329531 23.911067, 89.34777800000001 23.979892, 89.354095 24.050491, 89.348309 24.121128, 89.33054199999999 24.190063, 89.301214 24.255593, 89.261033 24.3161, 89.21097899999999 24.370086, 89.152281 24.416212, 89.086387 24.453333, 89.014928 24.480528, 88.939679 24.49712, 88.86251 24.502697))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006773-260120142942310/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.2025.67
title: Magnetic inducement during the preparation of doped alumina catalyst support for alcohol steam reforming

description: Magnetic inducement is explored for its ability to control and improve the properties of mixed oxide supports for alcohol steam reforming. Ceria- and gadolinia-doped alumina supports can improve catalytic activity and mitigate coke formation from their oxygen storage capacity. In this study, magnetic inducement is applied during the synthesis of mixed oxide systems involving paramagnetic and diamagnetic ions, which are ceria-doped, gadolinia-ceria-doped, and zirconia-doped alumina. The applied magnetic field has 2 arrangements, which are a) between same magnetic poles (N-N) and b) between opposite magnetic poles (N-S). The supports were characterized for their physical and chemical properties, then evaluated in terms of hydrogen production rate from ethanol or methanol steam reforming. It was found that the catalysts loaded on ceria-doped alumina and gadolinia-ceria-doped alumina supports prepared under N-N magnetic inducement showed the highest hydrogen production due to the superior Ce distribution. Rotating magnetic inducement that was applied to ceria-doped alumina supports resulted in support properties that are less prominent than their stationary counterparts. This is the opposing magnetic field induced from the electric field created in time-varying magnetic field, according to Lenz’s Law. Zirconia can also be used to control surface acidity of the doped alumina support. On the other hand, the effect of magnetic fields on gadolinia dopant in gadolinia-ceria-doped alumina could not be observed, so magnetic inducement may not be useful in controlling the properties of supports with more than one paramagnetic dopant. Magnetic inducement, however, could influence the property of zirconia-doped alumina supports, which consist only of diamagnetic species. Both N-N and N-S magnetic inducement led to an improvement in catalytic activity for ethanol steam reforming, even though they affected the supports’ surface acidity differently. In addition, effects of magnetic inducement during Cu-Zn catalyst loading on gamma-alumina were investigated for hydrogen production from methanol steam reforming. Rotating magnetic inducement resulted in improvement in hydrogen production rates compared to the non-magnetic and the stationary magnetic field counterparts. This was due to lower reduction temperatures of copper active metal, which could imply a change in dispersion of copper and zinc atoms on the support. The influence of magnetic inducement becomes less noticeable on non-porous, alpha-alumina, so it is expected that magnetic inducement may affect the solution viscosity and the formation of Cu-Zn in the alumina porous structure. Therefore, magnetic inducement can be used during both active metal loading and support synthesis to control the properties of a bimetallic catalyst and the mixed oxide supports, respectively.
DOI: 10.14457/tu.the.2025.68
title: Inventory model with demand forecasting based on long short-term memory

description: This study presents an inventory model that integrates machine learning-based demand forecasting to optimize inventory decisions under uncertain demand conditions. Weekly demand data from weeks 1 to 52 in 2021 were forecasted using Long Short-Term Memory (LSTM) and Bayesian Regression models in Python, as well as Exponential Smoothing (ETS) implemented in Excel. These forecasts were then applied to an inventory simulation using Excel Solver. Among the three forecasting methods, LSTM demonstrated the highest accuracy and was selected for further integration. The simulation compared three inventory control methods combined with LSTM, revealing that Method 3 consistently achieves a 100% Fill Rate and reduces total inventory costs by 13.72% compared to the baseline. While its total cost is slightly higher than Method 2, it remains significantly lower than Method 1, offering a reliable balance between cost efficiency and service level. The findings emphasize the benefits of combining advanced forecasting techniques with appropriate inventory policies to effectively manage demand variability, minimize stockouts, and improve overall supply chain performance.
DOI: 10.15468/dl.33ujp6
title: Occurrence Download

description: A dataset containing 994 records in GBIF matching the query:
SELECT
 '162' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -4.045127286108716
 AND decimalLatitude &lt;= 0.0
 AND decimalLongitude &gt;= 114.74440240591831
 AND decimalLongitude &lt;= 119.917667638792
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 994 records were derived from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006738-260120142942310/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.2025.69
title: Strategic success factors for digital transformation toward smart hospital in Thailand’s public health sector: a mixed method analysis

description: The transition to smart hospitals in Thailand's public sector represents a significant step towards digital transformation in healthcare. Addressing the challenges of knowledge management, leadership capabilities, and technology acceptance is crucial for success. By identifying the key factors that influence this transition, the study can provide valuable insights that contribute to improving healthcare delivery systems. This study not only supports the advancement of smart hospitals but also enriches the ongoing discourse on healthcare digitalization. The research on smart hospitals in Thailand is a comprehensive study that integrates both qualitative and quantitative methods to provide a holistic view of the digital transformation in healthcare. By engaging with healthcare professionals through surveys and in-depth interviews, the study captures a wide range of perspectives on the adoption of technology and management practices. The use of statistical and thematic analysis to interpret the data ensures a robust understanding of the underlying factors influencing the implementation of smart hospitals, which could significantly contribute to the optimization of healthcare services. The findings of this research are a testament to the pivotal role that knowledge management, leadership, and technology acceptance play in the evolution of smart hospitals. They not only contribute to a more innovative and adaptable healthcare environment but also directly impact operational efficiency and patient care. This underscores the broader implications of digital transformation within the healthcare sector, suggesting that the integration of technology is not just a trend but a fundamental component of modern healthcare delivery. The recent study underscores the synergy between knowledge management, leadership, and technology acceptance in transforming public hospitals into smart hospitals. It reveals that a strong foundation in knowledge management is crucial for the successful integration of new technologies and for fostering a culture of innovation among staff. Leadership is identified as the catalyst for change, essential for navigating the complexities of technology implementation. Moreover, the positive disposition of healthcare professionals towards technology is vital for the adoption of digital advancements, ensuring a smoother transition to smarter healthcare systems. The study underscores the transformative potential of digital technologies in Thailand's healthcare sector. It highlights the importance of leadership and knowledge management in adopting smart hospital technologies, which can lead to enhanced healthcare delivery and patient care. The insights provided by this research could be instrumental in guiding the development of policies and programs that support the digital competencies of healthcare professionals, ensuring that the benefits of technological advancements are fully realized in the public health domain.
DOI: 10.14457/tu.the.2025.70
title: Transportation mode estimation from large-scale human mobility data

description: As GPS-enabled devices become increasingly widespread, analyzing human mobility through recorded trajectories has become essential for developing intelligent transportation systems and promoting sustainable urban planning. However, these GPS datasets are often large-scale, high-frequency, and noisy, making it challenging to accurately classify transportation modes and detect transition points within trips that involve multiple transportation modes. To address this, we propose a deep learning–based framework that jointly estimates transportation modes and identifies mode transition points using a multi-output model built on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architecture. The framework incorporates an effective STAY/MOVE segmentation strategy to separate stationary and moving intervals, improving both mode recognition and transition detection. Rich kinematic features, such as speed, acceleration, direction changes, and turning behavior, are derived from raw GPS records (latitude, longitude, and timestamps) to capture both spatial and temporal mobility patterns. To handle the large dataset efficiently, a PySpark-based scalable inference pipeline is implemented for model inference, improving overall processing efficiency and reducing computation time. To evaluate generalizability, cross-province validation is conducted on a large-scale GPS dataset collected from GPS loggers and smartphones across multiple cities in China. The best-performing configuration achieves an F1-score of 96.8% and a recall of 96.8% for transportation mode classification, as well as an F1-score of 97.5% and a recall of 94.7% for transition point detection, successfully distinguishing walk, bike, car, bus, and train modes. These results demonstrate the robustness, scalability, and real-world applicability of the proposed approach across diverse urban contexts, highlighting its potential for large-scale mobility analytics and intelligent transportation applications.
DOI: 10.14457/tu.the.2025.71
title: Optimization model for container maintenance surcharge in the shipping industry

description: Shipping line industry is one that is currently growing in most recent periods since COVID-19 pandemic and one of their most valuable assets is containers. Most shipping lines currently are having issues with cost management and one of the costs is container maintenance fees. Proper container maintenance is crucial because it ensures the safety and integrity of goods during transit. Well-maintained containers prevent damage to the cargo, reduce the risk of accidents, and extend the lifespan of the containers themselves. This, in turn, helps shipping lines minimize unexpected expenses and maintain a reliable service for their customers. The container maintenance surcharge is a fee imposed by shipping lines to cover the costs of maintaining their container fleets. Premium and coverage analysis is crucial in identifying the most cost-effective methods to manage these maintenance expenses. Throughout the year, shipping lines face challenges in handling maintenance fees, influenced by cargo loads and market conditions. These factors must be aligned with customer behavior, particularly for key accounts that contribute significantly to trade lane volumes. By conducting premium and coverage analysis, shipping lines can pinpoint the most economical solutions for maintenance fees. This enables them to customize their approaches to meet the specific needs of their customers, ensuring optimal service delivery.
DOI: 10.14457/tu.the.2025.72
title: Real-time wildfire-prone area monitoring and early warning system based on two-stage YOLO-based smoke detection model

description: Wildfires present a severe risk to environmental sustainability, public health, and economic stability, especially in wildfire-prone areas such as northern Thailand. This thesis proposes a real-time wildfire-prone area monitoring and early warning system designed to address these challenges. First, a web-based application was developed to integrate IoT cameras with a YOLOv5-based smoke detection model for continuous monitoring and real-time notifications. Second, a two-stage smoke detection framework is introduced, leveraging Gaussian filtering and a dual-stage YOLOv5 pipeline to reduce false predictions and enhance detection reliability. The system was trained using the FireSpot dataset, comprising annotated images of early-stage wildfires, and demonstrated significantly improved performance over baseline models. Deployed in three municipalities in Chiang Mai, Thailand, the system has proven effective in delivering timely alerts and supporting wildfire management efforts. By integrating IoT technologies with machine learning techniques, this work provides a scalable and practical solution for early-stage wildfire detection, contributing to mitigation of environmental and public health impacts.
DOI: 10.14457/tu.the.2025.73
title: Multi-depot vehicle routing problem: a case study of regional distribution network in Thailand

description: Efficient distribution management is a critical challenge for retail supply chains operating in large urban regions. In Thailand, a case study retail chain within the Bangkok Metropolitan Region (BMR) rely on daily deliveries from multiple depots, yet current delivery planning often leads to redundant travel distances, high fuel costs, and scheduling inefficiencies. This study investigates the Multi-Depot Vehicle Routing Problem (MDVRP) in the context of a case study retail chain distribution network, aiming to minimize total transportation cost and travel distance while improving route scheduling.The research employs Open Solver in Microsoft Excel as the primary optimization tool to formulate and solve the MDVRP model. The approach considers multiple depots, heterogeneous store demands, and regional traffic conditions in Bangkok, with an emphasis on vehicle assignment, route sequencing, and scheduling efficiency. The results highlight optimized delivery routes that significantly reduce overall distribution costs compared to conventional planning methods.This study contributes to the field of logistics and supply chain systems engineering by demonstrating a practical application of VRP optimization techniques in a real-world retail distribution context. Moreover, it provides managerial insights for multi-depot logistics networks in urban settings, offering decision-makers a framework to improve operational efficiency, reduce distance, and enhance service reliability in last-mile delivery.
DOI: 10.14457/tu.the.2025.74
title: Enhancement of CsPbBr3 nanocrystal photodetector by plasmonic effect and their angle dependence study

description: In-organic perovskite nanocrystals (CsPbBr_3) show great potential for optical sensing applications that have excellent optic properties such as high carrier mobility, long carrier diffusion length, excellent visible light absorption. The absorption spectrum well overlaps with localized surface plasma resonance (LSPR) of noble Au nanocrystals (Au NCs). Currently, the state-of-art photocurrent of CsPbBr_3 is within the nanoampere range, limiting further practical application. Therefore, we would like to enhance the photocurrent of CsPbBr_3 nanocrystals and improve the device's stability. With the nature of energy level alignment between Au nanoparticle and CsPbBr_3 nanocrystals , significant photocurrent enhancement was demonstrated. In this study, CsPbBr_3 perovskite nanocrystals have been synthesized and used to fabricate the photodetector onto the gold finger electrode. Under laser excitement with wavelength at 520 nm, it has been observed that CsPbBr_3 nanocrystals alone could obtain the photocurrent to 100 nA. By introducing with the Au Nanoparticles, the net photocurrent has been increased nearly 1µA. Therefore, it could be said that Au nanoparticles can assist the photocurrent generation of CsPbBr_3 nanocrystals more sufficiently with better stability. This allows the further device system integration of such CsPbBr_3 nanocrystals with Au nanoparticles.
DOI: 10.5281/zenodo.18346513
title: Fluctuating environments cause context-dependent mate preferences in early population divergence

description: Attached you will find the R scripts and the accompanying data frames used to generate the results for this manuscript. Please refer to the attached README for instructions on how to use the scripts and data.

Fluctuating environments cause context-dependent mate preferences in early population divergence

Abstract

Understanding the factors shaping premating isolation in the early stages of population divergence is a major challenge in ecology and evolution. Context-dependent mate preferences that change with environmental conditions could either promote or counteract the development of premating isolation. Here, we investigated how environmental conditions and male origin affected female mate preferences in the colour polymorphic Common Bluetail Damselfly (Ischnura elegans). Closely located populations of this species differ in female colour morph frequencies, densities and species community composition, but there is extensive dispersal and gene flow between populations. We quantified female mate preferences for resident and immigrant males across years that differed in environmental harshness, revealed by interannual variation in female fecundity. Females showed highly context-dependent mate preferences: in a benign year, females preferred immigrant males, whereas in an extremely harsh year this pattern was reversed, and females instead preferred resident males. Furthermore, in the harsh year, field mating rates were low, and females were more prone to copulate in our experimental mate choice trials, indicating mate limitation and relaxed sexual conflict under harsh conditions. Female preferences for immigrant males in the benign year were unlikely due to inbreeding avoidance, as we found only weak population genomic structure and low fixation indices (FIS). Instead, female preferences for immigrant males when field mating rates are high is consistent with “chase away sexual selection”, whereby females develop resistance against mating harassment from local males. Unexpectedly, we found no differences in mate preferences between female colour morphs. Our study reveals that female mate preferences are highly context-dependent and shaped by environmental fluctuations, with implications for the development and stability of premating isolation.