cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8375666
title: Proteomic profiling of survivors of SARS-CoV-2-induced ARDS

description: Around 80% of patients who develop acute respiratory distress syndrome (ARDS) secondary to SARS-CoV-2 infection experience persistent lung dysfunction. The molecular factors that mediate pulmonary sequelae and recovery are unknown.<strong> </strong>In this context, we sought to comprehensively characterize the proteomic determinants of pulmonary diffusion impairment.<strong> </strong> This was a prospective cohort study including eighty-seven SARS-CoV-2–induced ARDS survivors. A complete pulmonary function evaluation and chest computed tomography (CT) were performed 3 months after hospital discharge. Proteomic profiling (364 proteins) was performed in plasma samples using proximity extension assay (PEA) technology. Partial least squares-discriminant analysis (PLS-DA) and random forest (RF) methods were used to assess predictor importance. Thirty percent of patients presented moderate to severe impairment of lung diffusing capacity (D<sub>LCO</sub>&lt;60% predicted). In the univariate analysis, fifteen proteins showed high concentrations in patients with D<sub>LCO</sub>&lt;60% [false discovery rate (FDR)&lt;0.05]. Pleiotrophin (PTN) displayed the highest differences: fold change=2.22 and FDR=0.001. Differentially detected proteins showed an inverse and independent dose–response relationship with D<sub>LCO</sub>. The multivariable approaches clustered proteins according to the severity of diffusion impairment. Clusters were composed of host mediators of cell proliferation and differentiation, tissue remodeling, angiogenesis, coagulation, inflammation, immune response and fibrosis signaling. In survivors of SARS-CoV-2–induced ARDS, lung diffusion impairment is associated with specific circulating factors implicated in multiple injury and repair mechanisms. The host protein signatures allow a better understanding of pulmonary sequelae and may constitute therapeutic targets and biomarkers. The long-term biological and clinical significance of these observations requires further investigation.
DOI: 10.3886/e163822v2-141782
title: Code for: Digital Addiction

description: Many have argued that digital technologies such as smartphones and social media are addictive. We develop an economic model of digital addiction and estimate it using a randomized experiment. Temporary incentives to reduce social media use have persistent effects, suggesting social media are habit forming. Allowing people to set limits on their future screen time substantially reduces use, suggesting self-control problems. Additional evidence suggests people are inattentive to habit formation and partially unaware of self-control problems. Looking at these facts through the lens of our model suggests that self-control problems cause 31 percent of social media use.
DOI: 10.6084/m9.figshare.24188748
title: Disinformation: Research project database

description: An overview of the current state of disinformation research in Spain
DOI: 10.6084/m9.figshare.24188748.v7
title: Disinformation: Research project database

description: An overview of the current state of disinformation research in Spain
DOI: 10.5281/zenodo.8375312
title: 20230822-IKEM: Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.

description: This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20230822-IKEM<br> <br> Experiment design: Agents interact by playing games with objects and adapt their ontologies to agree on decision taking. Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.<br> <br> Experiment setting: Agents learn decision trees (transformed into ontologies); choose the objects (and partners) to interact with; adapt by splitting their leaf nodes<br> <br> Hypotheses: 1) Agents will be able to fulfil their motivation in terms of increased exploration (exploratory motivations) and decreased exploration (non-exploratory motivation). 2) Agents with exploratory motivation will be more complete, but less accurate than the baseline and non-exploration. 3) Curious agents will be more accurate and complete, but converge slower in comparison with creativity. 4) In more complex settings (higher number of agents and properties), exploration leads to more completeness, accuracy, distance and faster convergence than the baseline. 5) Agents with non-exploratory motivation will be less accurate and complete than the baseline. 6) The indirect learning models have a higher accuracy and completeness than the direct models. 7) Agents with socially oriented intrinsic motivation will have less diverse knowledge, but agree more and converge faster than agents with individual intrinsic motivation. 8) Heterogeneously motivated agents will have a higher accuracy and completeness, but lower diversity and converge slower than homogeneously motivated agents.<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] https://sake.re/20230822-IKEM<br> [2] https://gitlab.inria.fr/moex/lazylav/<br>
DOI: 10.5281/zenodo.8375311
title: 20230822-IKEM: Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.

description: This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20230822-IKEM<br> <br> Experiment design: Agents interact by playing games with objects and adapt their ontologies to agree on decision taking. Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.<br> <br> Experiment setting: Agents learn decision trees (transformed into ontologies); choose the objects (and partners) to interact with; adapt by splitting their leaf nodes<br> <br> Hypotheses: 1) Agents will be able to fulfil their motivation in terms of increased exploration (exploratory motivations) and decreased exploration (non-exploratory motivation). 2) Agents with exploratory motivation will be more complete, but less accurate than the baseline and non-exploration. 3) Curious agents will be more accurate and complete, but converge slower in comparison with creativity. 4) In more complex settings (higher number of agents and properties), exploration leads to more completeness, accuracy, distance and faster convergence than the baseline. 5) Agents with non-exploratory motivation will be less accurate and complete than the baseline. 6) The indirect learning models have a higher accuracy and completeness than the direct models. 7) Agents with socially oriented intrinsic motivation will have less diverse knowledge, but agree more and converge faster than agents with individual intrinsic motivation. 8) Heterogeneously motivated agents will have a higher accuracy and completeness, but lower diversity and converge slower than homogeneously motivated agents.<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] https://sake.re/20230822-IKEM<br> [2] https://gitlab.inria.fr/moex/lazylav/<br>
DOI: 10.18712/nsd-nsd3005-v1
title: Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem-solving situations (ADAPT21)

description: All life is problem solving. We are most often faced with situations, in which the information that is needed to solve a problem is not immediately obvious and in which things can change quite rapidly. For instance, imagine the software on your smartphone went through an update and has therefore novel features, designs, and functionalities. You will find yourself in a new and perhaps unfamiliar situation that requires you to learn and adjust your previous thinking about how your smartphone works.

The ability to cope with such a situation that is subject to novelty and changes is referred to as “adaptability”. Given the rapid development of modern information and communication technology in our society, this ability has become as important as never before. But how exactly does adaptability work? Which steps need to be taken in order to become adaptive? How does adaptability relate to the actual success in solving a problem? And, most importantly, how can we know that we are adaptable?

This study takes an educational perspective on these questions, thereby focusing on how secondary school students’ creatively adjust their thinking, behavior, and drive to changes and novelty in problem solving situations. Specifically, in order to study how adaptability works, a computer-based test will be developed that captures the construct. This innovative test will be evaluated with respect to its quality and the extent to which it measures what it is supposed to measure.

To further understand the nature of adaptability as a construct that may assist students in academic and non-academic contexts, its relations to constructs such as intelligence, school achievement, self-concept, and the willingness to engage in problem solving will be investigated. 

The project will generate new knowledge about adaptability in complex problem solving situations as a key skill in 21st century education.
DOI: 10.14457/tu.the.2022.763
title: Shrimp length estimation for an automatic feeding-tray lifting system used in shrimp farming

description: The shrimp farmer monitors shrimp growth by measuring the shrimp’s size with some equipment or estimating its size with the naked eye. These traditional approaches are time-consuming, experience-required, and tedious. To facilitate shrimp farmers, this research aims to propose shrimp length estimation based on images captured by an automatic feeding-tray lifting system used in shrimp farming. The framework of the proposed method was designed into three main processes. The first process is shrimp detection. Two convolutional neural network models for instance segmentation were proposed to detect shrimps and generate segmentation masks. The first model is a mask region-based convolutional neural network with a ResNeXt model which achieved a precision of 97.10%, a recall of 94.36%, a F1 score of 95.71%, and an average precision of 95.97%. The second one is the newest version of the You Only Look Once model which achieved a precision of 91.87%, a recall of 97.74%, a F1 score of 94.72%, and an average precision of 95.60%. From the comparison of both models, the first model was chosen as a primary detection model for the proposed method because of its performance and ability to generate a high-quality segmentation mask. In the second process, this study proposed an approach for determining a reference scale which is used to convert a pixel distance in the image to an actual length. The diameter of a feeding tray installed on the automatic feeding-tray lifting system was used as a reference object. The last process is shrimp length estimation. Two unique methods were proposed for estimating shrimp length. The first method achieved a mean absolute error of 1.63 cm and a mean absolute percentage error of 11.59%. The second method achieved a mean absolute error of 0.65 cm and a mean absolute percentage error of 4.61%. Both methods have their own strength and weakness. The former works better in the case of straight-body shrimp, but the latter works well in the case of curved-body shrimp. To improve the performance, another method was proposed to combine both methods using a box ratio or eigenvalue ratio as a method-selection parameter. The combination method based on the box and eigenvalue ratio achieved a mean absolute error of 0.59 cm and 0.60 cm, respectively. It indicates that the combination method can reduce the error of the previously proposed estimation methods. This proposed method helps shrimp farmers save time and monitor their shrimp growth easily. It also generates useful digital data for shrimp farmers so that they can use it to analyze and manage their farms effectively. Moreover, some features were included in the proposed method in order to facilitate the shrimp farmer, for example, sending an estimation result to the shrimp farmer via a LINE application, notifying problems on the automatic feeding tray lifting system to the shrimp farmer, etc.
DOI: 10.14457/tu.the.2022.764
title: Automated forex trading system using stacked machine learning and technical analysis

description: Forex appeals to investors who enjoy the challenge of profiting from market fluctuations. However, it also carries the risk of potential losses. To mitigate these risks, many investors seek tools for predicting price movements. This thesis suggests utilizing Stacking Machine Learning Models with Deep Q-Network to generate automated Forex trading, assisting investors in decision-making and strategy planning. Our experiments involve comparing four major currency pairs using Trading Metrics to evaluate model performance. As experiment results, our model demonstrated profitability and achieved a minimum accuracy of 65% in both Short Trades Won (%) and Long Trades Won (%).
DOI: 10.14457/tu.the.2022.766
title: High-value fruit biometric identification via triplet-loss technique

description: This thesis proposes a novel method for biometric authentication of fruits based on their distinctive rind patterns, similar to fingerprint identification. Luxury fruits, highly valued in Japan, currently rely on serial numbers, QR codes, and RFID tags for authentication, which can be forged or replicated. By implementing biometric authentication using rind patterns, the trust and value of these fruits can be significantly enhanced, while also preventing fraud and counterfeiting in the agricultural industry. The study introduces a melon identification system that utilizes a convolutional neural network (CNN) with a triplet loss function, enabling accurate identification even with variations in lighting, shadows, and angle. The proposed method overcomes the limitations of previous approaches by capturing important features through CNN’s Automatic feature identification. This research contributes to the field of agricultural product authentication, providing a secure and reliable method that can be extended to other products, increasing customer trust and market value.
DOI: 10.14457/tu.the.2022.767
title: Pattern recognition of partial discharge faults using convolutional neural network (CNN)

description: Partial Discharge (PD) analysis is one the most widely used methods to monitor and determine the fault conditions of electrical equipment, especially in high-voltage environments such as power transformers and power generators. Conventional method of PD analysis that is widely used in multiple studies and commercial equipment usually rely on a feature extraction technique such as the Phase Resolved Partial Discharge (PRPD) pattern to assist PD experts to inspect the faults in the system. This study proposes a CNN based method to recognize the PRPD patterns for different types of PD. The differences of each type of PD, data pre-processing steps and visualization of PD waveforms in PRPD patterns are discussed in details. The obtained PRPD pattern images are then used to train a pattern recognition model and the results show that the proposed method can effectively classify different types of PD under consideration.
DOI: 10.14457/tu.the.2022.768
title: Estimation of crop water stress index for real-time durian orchard monitoring system

description: This thesis presents a comprehensive framework for estimating the crop water stress index (CWSI) in durian farming. By integrating the Kalman filter with a neural network, the framework improves the accuracy of canopy temperature estimation, identify the non-water-stressed baseline for durian tree, and reduces noise in CWSI calculations. Traditionally, stem psychrometers have been used to identify water stress in plants. However, they are not designed for long-term use and are not suitable for real-time monitoring. CWSI provides a suitable alternative, allowing for continuous monitoring and accurate estimation of water stress levels in durian trees. The main objective of this research is to estimate CWSI to enhance water management practices and optimize durian productivity. The framework’s evaluation was conducted on a durian farm in Chanthaburi, Thailand. where we collected data on the durian tree from December 2020 and November to December 2021. By utilizing IoT thermal cameras for data collection, the framework successfully estimated canopy temperature with a notable 27.82% improvement in correlation. One of the key contributions of this work is the proposed method to identify the non-water-stressed baseline for CWSI calculation, which plays a vital role in accurate estimation. Additionally, the framework reduces noise in CWSI by approximately 9.47%. Overall, the results demonstrate significant improvements in accuracy and correlation with stem water potential, with the framework achieving an impressive average correlation of -0.7790. This represents an enhancement of up to 40% in overall accuracy compared to not using the framework. However, it is essential to note that the evaluation is based on limited stem water potential data. The advantages of this framework extend beyond improved accuracy and correlation. It promotes water conservation and enables more ecient water management, which is crucial for sustainable durian production. By accurately estimating the CWSI, farmers can make informed decisions about irrigation scheduling, leading to better water management practices and increased durian productivity. This framework presents a valuable tool for the agricultural sector, aligning with the principles of precision farming and smart farming to contribute to the advancement of sustainable durian cultivation and support ecient water management practices.
DOI: 10.14457/tu.the.2022.769
title: Sustainable rice consumption index in Thailand

description: Land use planning in agriculture development is vital to sustainable production and consumption. In Thailand, rice is the most important staple crop for domestic consumption. Rice by-products, including rice husk and rice straw, are also used as livestock feeds. While the national rice production capacity is much greater than domestic consumption, many provinces still have an insufficient local or regional supply of rice. The imbalance in supply and demand comes at a cost of unnecessary transportation and inventory and the possibility of rice stock’s quality decline. Such issues have been overlooked, partly due to the lack of a sustainable land use index that can be used to evaluate and select the agricultural development plan for sustainable production and consumption. This study develops a provincial supply-to-demand ratio for rice and by-products. The up-to-date national rice demand and rice productivity data are used to create the supply-to-demand maps for rice and by-products. The supply-to-demand map can be used for develop rice production strategies. The findings of this study also contribute to the improved public awareness of carrying capacity and sustainable development.
DOI: 10.14457/tu.the.2022.771
title: Fault prediction model for motor and generative adversarial

description: The manufacturing process must continuously be improved. One of the most efficient strategies is maintenance scheduling by predictive maintenance for early fault detection and assisting with real-time decisions. The major concern of developing a predictive maintenance system is lack of the abnormal data and the cost of a high-specification sensor device for collecting data. This paper introduces the unsupervised learning model called Generative Adversarial Networks (GANs) for the generation of abnormal data in form of acceleration signals in order to provide a dataset for developing early fault prediction model and assisting a real-time decision on a low-frequency sensor device. The prediction model dataset is labeled on ISO10816-3 to classify the label of data by Velocity Vibration (mm/sec). The machine learning classifier model implements with hyperparameters optimization framework called OPTUNA to provide the best model performance. The proposed system aims to assist in real-time decision and maintenance schedules for the injection molding machine and offer the prediction model based on low-frequency sensor data from a drive motor
DOI: 10.14457/tu.the.2022.772
title: A computer vision-based counting system: a case study of rubber grommet

description: This research addresses the challenges of manually counting overlapping rubber grommets in mass production processes. By exploring the practical implications of computer vision technology, the study aims to develop an affordable and efficient alternative for counting rubber grommets. The objective is to develop an accurate object detection model for counting overlapping rubber grommets and evaluate different training methods to optimize performance. The methodology involves dataset collection, model customization, and adjustments of hyperparameters. The results highlight the significant impact of modifying the training dataset on performance, with adjustments to training cycle and batch size also influencing model performance. From the result, the model achieved the highest accuracy with an F1 score of 97.94%. Even though there are some errors in detection and counting, they primarily occur due to the intense overlap of rubber grommets. However, the model still effectively serves its objective by detecting overlapping and in-contact rubber grommets to a certain degree. The study identifies limitations and proposes future work to enhance the model, including expanding the training dataset, implementing cross-validation for data splitting, and incorporating object tracking algorithms. Successful detection and counting of overlapping rubber grommets can offer valuable insights for improving operational efficiency and productivity in rubber grommet production.