Miracle-dz/UniFAR-FTUs huggingface dataset:
Support for social policies: Focusing on effects of group belonging

This study aims to elucidate the structure of support for social policies (redistribution and free competition), focusing on the role of community interests (especially demographic decline). To this end, Japan was selected as a case study because it has the highest proportion of the elderly population in the world. The author analyzed data from the National Survey of Social Stratification and Social Mobility in 2015 and the Population Census for the same year, employing ordered logit models. The results revealed that people living in demographically declining communities were more likely to support redistribution and less likely to endorse free competition, compared to individuals from other communities. Furthermore, compared to the underprivileged, wealthy individuals were more likely to consider community interests irrespective of individual benefits. This finding demonstrates that community interests may have a significant influence on individuals’ policy preferences.
Native and Oxidized Starch for Adsorption of Nickel, Iron, and Manganese Ions from Water

The adsorption of heavy metal ions from surface water with ecologically safe and biodegradable biopolymers is increasingly becoming an appealing research challenge. Starch as a biopolymer is exceptionally attractive to solve this problem for its low cost and abundant availability in nature. To expel Ni2+, Fe2+/3+, and Mn2+ from water, we analyzed two native and two oxidized starches, namely potato and corn starch, as bio-adsorbers. The morphology and the surface property of the different starches were studied using SEM. To assess the effectiveness of adsorption onto the starches, we tested three realistic concentrations based on German drinking water ordinance values that were 10-fold, 100-fold, and 1000-fold the limits for Mn2+, Fe2+, and Ni2+, respectively. The concentration of the different ions was measured using the ICP-OES. Furthermore, from subsequent investigations of the adsorption isotherms, we evaluated the adsorption capacities and mechanisms. The adsorption isotherms were fitted using the Langmuir, Sips, and Dubinin–Radushkevich models, whereby Sips showed the highest correlation. Oxidized potato starch achieved viable adsorption capacities of 77 µmol Fe2+/g, 84 µmol Mn2+/g, and 118 µmol Ni2+/g. Investigating the influence of initial swelling in water on the adsorption performance, we found that especially the percentage removal with oxidized starches decreased significantly due to the formation of hydrogen bonds with water molecules at their binding sites with prior swelling.
On Extracting Digitized Spiral Dynamics’ Representations: A Study on Transfer Learning for Early Alzheimer’s Detection

This work proposes a decision-aid tool for detecting Alzheimer’s disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology for exploiting transfer learning to compensate for sparse data. We embed directly in spiral trajectory images, kinematic time functions. With transfer learning, we perform automatic feature extraction on such images. Experiments on 30 AD patients and 45 healthy controls (HC) show that the extracted features allow a significant improvement in sensitivity and accuracy, compared to raw images. We study at which level of the deep network features have the highest discriminant capabilities. Results show that intermediate-level features are the best for our specific task. Decision fusion of experts trained on such descriptors outperforms low-level fusion of hybrid images. When fusing decisions of classifiers trained on the best features, from pressure, altitude, and velocity images, we obtain 84% of sensitivity and 81.5% of accuracy, achieving an absolute improvement of 22% in sensitivity and 7% in accuracy. We demonstrate the potential of the spiral task for AD detection and give a complete methodology based on off-the-shelf features.
Failure Mode and Effect Analysis Using T-Spherical Fuzzy Maximizing Deviation and Combined Comparison Solution Methods

Failure mode and effect analysis (FMEA) is a potent risk analytical instrument extensively utilized for enhancing systems’ quality. Because the classical FMEA model has some deficiencies, numerous fuzzy set-based enhanced FMEA techniques have been developed to improve risk evaluation results’ reasonability. However, most of them require experts to follow certain associated constraints when expressing preferences; otherwise, their preferences will be invalid, which limits experts’ flexibility. In addition, the previous methods rarely consider the reliability of weight allocation results and the stabilization of risk ranking results. Many previous methods usually emphasize the local difference between assessments of failure modes in calculating objective weights and merely depend on one compromise solution in ranking failure modes, both of which may affect the precision of their results. To overcome these limitations, this study applies T-spherical fuzzy sets, the recent generalization of fuzzy sets without strict constraints, to flexibly characterize experts’ preferences. Subsequently, a divergence-based maximizing deviation method is presented to determine the weights of experts and risk factors. A new consensus feedback mechanism is also introduced to achieve consensus among experts. Furthermore, a T-spherical fuzzy combined compromise solution method is presented to rank failure modes stably. Finally, a case study, sensitivity analysis, and comparisons show that the proposed model is effective and practically suitable.
A Novel Methodology for Credit Spread Prediction: Depth-Gated Recurrent Neural Network with Self-Attention Mechanism

This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN’s prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.
Real-Time Driving Scene Understanding via Efficient 3-D LIDAR Processing

The 3-D light detection and ranging (3-D LIDAR) sensors are widely used in autonomous vehicles; however, their drawback is the significant computation processing requirement. Although parallel processing using high-end graphics processing units (GPUs) can solve the aforementioned problem, the cost of high-end GPUs prevents 3-D LIDAR from being used in commercial vehicles. In this study, a new fast and efficient perception method using 3-D LIDAR is developed. The aim is to develop a real-time perception method that can run on both a low-end GPU and a central processing unit (CPU). The proposed method consists of three parts: preprocessing, point cloud classification network (PCCN), and filtering. To process a point cloud using minimal computation and achieve good performance, handcrafted features and learning-based features are used in PCCN—the former capture the global information of the given point cloud, whereas the latter encode the local information of the points in the cloud. Finally, the proposed LIDAR perception method is applied to the SemanticKITTI dataset, and it is validated by comparing with other competing methods in terms of accuracy and computation time on the CPU.
Feeding Thai Native Sheep Molasses Either Alone or in Combination with Urea-Fermented Sugarcane Bagasse: The Effects on Nutrient Digestibility, Rumen Fermentation, and Hematological Parameters

Simple Summary A key strategy for attaining value-added and ecologically sustainable operations is the conversion of agro-industrial waste into animal feed. Sugarcane bagasse, a fibrous byproduct of the sugar-refining process, is widely available. Bagasse, however, has low nutritional value and a high quantity of indigestible fiber, which causes low digestibility and, as a result, poor animal performance. Ensiled bagasse’s fermentation properties improved after being treated with molasses and urea. Sheep’s ability to digest crude protein was increased by mixing fermented sugarcane bagasse with 10% molasses and 3% urea without having an adverse impact on the ruminal fermentation or the animal’s hematological parameters. Abstract The purpose of this study was to find out how adding molasses to fermented sugarcane bagasse (FSB) alone or in combination with urea affected sheep’s rumen fermentation, hematological parameters, and ability to digest nutrients. Four Thai native sheep with an initial body weight (BW) of 20.87 ± 1.95 kg and 11 ± 1.0 months old were assigned to a 4 × 4 Latin square design with 4 periods of 14-d adaptation and 7 d of sample collection. Each treatment received a different combination of experimental roughage as follows: FSB without additives (T1), FSB + 10% molasses (T2), FSB + 20% molasses (T3), and FSB + 10% molasses + 3% urea (T4). The concentrate diet was fed twice daily at 2% BW, while roughage sources were provided ad libitum for each treatment. The crude protein (CP) digestibility in the T2 and T3 groups was higher (p < 0.05) than in the FSB group without additions, with the T4 group having the highest (p < 0.05). Although there were no significant differences in blood glucose, packed cell volume, ruminal pH, ammonia–nitrogen (NH3-N), propionic acid, or acetic acid, the plasma urea nitrogen (PUN) at 0 h was highest in the T4 group (p < 0.05) compared with the other groups. However, the proportion of butyric acid tended to be higher in all FSB groups with additives. Thus, the current experiment concluded that the addition of molasses alone or in combination with urea had positive effects on pH and LAB population, and including both together in FSB improved the CP digestibility of sheep. In conclusion, FSB with 10% molasses and 3% urea might be used as an alternate roughage source for ruminants without affecting the animal’s ruminal fermentation or hematological parameters.
Misinformation and Facts about Breast Cancer Screening

Quality medical practice is based on science and evidence. For over a half-century, the efficacy of breast cancer screening has been challenged, particularly for women aged 40–49. As each false claim has been raised, it has been addressed and refuted based on science and evidence. Nevertheless, misinformation continues to be promoted, resulting in confusion for women and their physicians. Early detection has been proven to save lives for women aged 40–74 in randomized controlled trials of mammography screening. Observational studies, failure analyses, and incidence of death studies have provided evidence that there is a major benefit when screening is introduced to the general population. In large part due to screening, there has been an over 40% decline in deaths from breast cancer since 1990. Nevertheless, misinformation about screening continues to be promoted, adding to the confusion. Despite claims to the contrary, a careful reading of the guidelines issued by major groups such as the U.S. Preventive Services Task Force and the American College of Physicians shows that they all agree that most lives are saved by screening starting at the age of 40. There is no scientific support for using the age of 50 as a threshold for screening. All women should be provided with the facts and not false information about breast cancer screening so that they can make “informed decisions” for themselves about whether to participate.
Primary-like Human Hepatocytes Genetically Engineered to Obtain Proliferation Competence as a Capable Application for Energy Metabolism Experiments in In Vitro Oncologic Liver Models

Simple Summary Fatty liver disease is an increasing health concern in Westernized countries. A fatty liver can lead to hepatocellular carcinoma (HCC), a type of liver cancer arising from hepatocytes, the major cells of the liver. How HCC may develop from the fatty liver is not known, and good cellular systems to investigate this are lacking. Recently, hepatocytes that can multiply continuously have been generated and suggested for hepatocyte research. In this study, we compared these continuously multiplying human hepatocytes to normal human hepatocytes and liver cancer cells, both within the state of fatty liver or not. We identified that these multiplying hepatocytes displayed many similarities to the liver cancer cells in terms of energy metabolism and concluded that these hepatocytes could be a pre-cancer model for liver cancer research and would be a valuable tool for HCC research. Abstract Non-alcoholic fatty liver disease (NAFLD), characterized by lipid accumulation in the liver, is the most common cause of liver diseases in Western countries. NAFLD is a major risk factor for developing hepatocellular carcinoma (HCC); however, in vitro evaluation of hepatic cancerogenesis fails due to a lack of liver models displaying a proliferation of hepatocytes. Originally designed to overcome primary human hepatocyte (PHH) shortages, upcyte hepatocytes were engineered to obtain continuous proliferation and, therefore, could be a suitable tool for HCC research. We generated upcyte hepatocytes, termed HepaFH3 cells, and compared their metabolic characteristics to HepG2 hepatoma cells and PHHs isolated from resected livers. For displaying NAFLD-related HCCs, we induced steatosis in all liver models. Lipid accumulation, lipotoxicity and energy metabolism were characterized using biochemical assays and Western blot analysis. We showed that proliferating HepaFH3 cells resemble HepG2, both showing a higher glucose uptake rate, lactate levels and metabolic rate compared to PHHs. Confluent HepaFH3 cells displayed some similarities to PHHs, including higher levels of the transaminases AST and ALT compared to proliferating HepaFH3 cells. We recommend proliferating HepaFH3 cells as a pre-malignant cellular model for HCC research, while confluent HepaFH3 cells could serve as PHH surrogates for energy metabolism studies.
SaaMES: SaaS-Based MSA/MTA Model for Real-Time Control of IoT Edge Devices in Digital Manufacturing

As a software delivery model, Software as a Service (SaaS) has attracted considerable attention from software providers and users. Most traditional companies are shifting their businesses to an SaaS model. SaaS development is a very complicated process and its success depends on architectural design and development. A Manufacturing Execution System (MES) was used at the expense of licensing fees for features not used in the On-Premise environment, although the features used vary depending on the manufacturing environment. In an SaaS environment, MES is applied with a function-specific container approach through a Microservice Architecture (MSA) to select and employ only the necessary functions. Furthermore, as the number of customers of virtualized applications increases in SaaS-based services, complexity and operating costs increase; thus, Multi-tenancy Architecture (MTA) technology, which serves all customers through a single instance of the application is crucial. Thus, in this study, we investigate the MTA approach and propose a suitable MTA for the manufacturing execution system. Real-time response is crucial to achieving a cyber-physical system of digital manufacturing in SaaS-based MES. Furthermore, SaaS-based big data analytics and decision-making cannot meet the needs of numerous applications in real-time sensitive workplaces. In this study, we propose an SaaS-based MSA/MTA model for real-time control of Internet of Things (IoT) Edge in digital manufacturing (SaaMES), an architecture of SaaS-based MES with MSA and MTA to meet vulnerable workplaces and real-time responses in Cloud environments. The analysis is used by applying the Autoencoder and Generic Adversarial Networks analysis model to IoT Edge for the connection between the Cloud environment and work site to enable real-time response and decision-making through communication using OPC-UA and small-scale analysis.
Estimation of Fusarium Head Blight Severity Based on Transfer Learning

The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB.
Robust Control of Power Buffers in DC Microgrids

Power buffers are dc-dc converters with large capacitors to provide inertia during microgrid transients. This power buffer converter precedes the point-of-load (PoL) converter; the later converter is tasked with regulating the load voltage. By controlling the input impedance of the power buffer, the impact of abrupt load changes on the resistive dc network is cushioned. In practice, dc microgrids might need to support uncertain loads. Such loads, however, have not been treated for power buffers with proper robust controllers. Herein, load uncertainties are represented using polytopic models, and state feedback controllers are developed by solving the resulting linear matrix inequalities. Centralized, decentralized, and distributed control schemes are accommodated in the presence of uncertain loads. Proposed robust controllers are validated for a dc microgrid, populated with power buffers, in a controller/hardware-in-the-loop environment.
Correlation and coherence in quantum-dot cellular automata

by Géza Tóth In this thesis we investigate the role of correlation and coherence in two pos realizations of Quantum-dot Cellular Automata (QCA): realizations as a semicondu multi-quantum-dot structure and as a metal-island single electron tunneling circuit. two are different from the point of view of the underlying physics. The metal isla circuits are very strongly connected to the heat bath and they can be modeled classically, using classical quantities such as charging energy and capacitance. To the semiconductor realization, a quantum mechanical treatment is necessary. The qu mechanical state of the cells evolves coherently, at least for time scales smaller tha decoherence time. In the first part of the thesis the theory of metal island circuits is us design a cell structure permitting adiabatic clocking. It is also used to analyze conductance suppression of coupled double-dots and reproduce the correspo experimental results from the theory by modeling coherent electron motion inside QCA cell. In the second part the semiconductor QCA realization is studied. U Hartree-Fock approximation the basic phenomena in the one dimensional QCA (large and small amplitude polarization wave propagation and collision) is investiga art of nd the ctor with d that ther ority ults The approach is also used to define Quantum Cellular Neural Networks. In the last p the thesis intermediate approximations are constructed between the Hartree-Fock a exact model. An alternative of the density matrix description, the coherence ve formalism is reviewed and used to investigate possibility of quantum computing QCA. Using the coherence vector formalism as a basis an approximation is presente includes all two-point correlations while neglects the higher order correlations. Ano approach is shown for improving the self-consistent Hartree-Fock model for a maj gate by including correlation effects. The method fixes the qualitatively wrong res obtained if the length of the input legs are very different. v . vi . xiii . 1 . 6 . 8 . 13 . 16 . 17 . 29 . 32 . 35 . 40 . 42 . 44 . 46 . 51 . 55 . 58 . 58 . 61 . 64 . 64 . 67 . 70 . 74 . 76 . 77 . 79 . 82 . 84 . 85 . 86 . 88 . 88 TABLE OF CONTENTS LIST OF TABLES . LIST OF FIGURES . ACKNOLEDGEMENTS .
Non-negative Matrix Factorization を用いたベクトル空間情報検索モデルの次元削減手法

The Vector Space Model (VSM) is a conventional information retrieval model, which represents a document collection by a term-by-document matrix. Since term-by-document matrices are usually high-dimensional and sparse, they are susceptible to noise and are also difficult to capture the underlying semantic structure. Dimensionality reduction is a way to overcome these problems. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are popular techniques for dimensionality reduction based on matrix decomposition, however they contain both positive and negative values in the decomposed matrices. In the work described here, we use Non-negative Matrix Factorization (NMF) for dimensionality reduction of the vector space model. Since matrices decomposed by NMF only contain non-negative values, the original data are represented by only additive, not subtractive, combinations of the basis vectors. This characteristic of parts-based representation is appealing because it reflects the intuitive notion of combining parts to form a whole. Using MEDLINE collection, we experimentally showed that NMF offers great improvement over the vector space model.
Comparing Students’ Perceptions on Quality of School Life: Voices from Malay and Chinese Ethnic Students

While the quality of school life is expected to be cultural and context-specific, fewer studies have been conducted to investigate the conceptualisation of quality of school life in a multi-ethnic and multicultural context. This study aims to compare Malay and Chinese primary school students’ perceptions on the quality of school life in Malaysia. This study employed a quantitative cross-sectional survey research design. Survey data were collected from 594 Grade 5 students. Findings revealed that both Malay and Chinese students ranked the highest score on the opportunity dimension and the lowest scores on the negative affect dimension. The Malay students scored higher means in all dimensions of quality of schools than the Chinese students, except the findings revealed cultural differences in the perceptions of quality of school life.