Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming growth factor-β1 genotype and susceptibility to chronic obstructive pulmonary disease

Background: Only a few long term smokers develop symptomatic chronic obstructive pulmonary disease (COPD) and this may be due, at least in part, to genetic susceptibility to the disease. Transforming growth factor β1 (TGF-β1) has a number of actions that make it a candidate for a role in the pathogenesis of COPD. We have investigated a single nucleotide polymorphism at exon 1 nucleotide position 29 (T→C) of the TGF-β1 gene that produces a substitution at codon 10 (Leu→Pro). Methods: The frequency of this polymorphism was determined in 165 subjects with COPD, 140 healthy blood donors, and 76 smokers with normal lung function (resistant smokers) using the polymerase chain reaction and restriction enzyme fragment length polymorphism. Results: The distribution of genotypes was Leu-Leu (41.8%), Leu-Pro (50.3%), and Pro-Pro (7.9%) for subjects with COPD, which was significantly different from the control subjects (blood donors: Leu-Leu (29.3%), Leu-Pro (52.1%) and Pro-Pro (18.6%), p = 0.006; resistant smokers: Leu-Leu (28.9%), Leu-Pro (51.3%) and Pro-Pro (19.7%), p = 0.02). The Pro10 allele was less common in subjects with COPD (33%) than in blood donors (45%; OR = 0.62, 95% CI 0.45 to 0.86, p = 0.005) and resistant smokers (45%; OR = 0.59, 95% CI 0.40 to 0.88, p = 0.01). Conclusions: The proline allele at codon 10 of the TGF-β1 gene occurs more commonly in control subjects than in individuals with COPD. This allele is associated with increased production of TGF-β1 which raises the possibility that TGF-β1 has a protective role in COPD.
“I Want to Publicize My Stutter”: Community-led Collection and Curation of Chinese Stuttered Speech Data

This paper documents the process undertaken by StammerTalk , an online community of Chinese-speaking people who stutter, to autonomously collect and curate stuttered speech data. While marginalized communities were often treated as the subjects of personal data collection, StammerTalk pioneered the community data stewardship model to improve their experiences with speech AI technologies. Through interviews and surveys with community members who collected and contributed their speech data, we unpack the unique incentives and challenges marginalized communities face when engaging in grassroots data collection and governance. We find such community-driven data collection not only produced data needed to represent the community in data-intensive applications, but also empowered the community and its members, by enabling authentic expression of their identity through data and strengthening the connections within the community. While current socio-technical systems were not designed to support these initiatives, we discuss strategies and factors for communities to succeed in similar data endeavors.
THREE RED HERRINGS AROUND VAUGHT’S

We give a model theoretic proof that if there is a counterexample to Vaught’s conjecture there is a counterexample such that every model of cardinality ℵ 1 is maximal (strengthening a result of Hjorth’s). In the process we analyze three examples of a sentence characterizing ℵ 1 . We also give a new proof of Harrington’s theorem that any counterexample to Vaught’s conjecture has models in ℵ 1 of arbitrarily high Scott rank below ℵ 2 .
Similarities and Differences in Risk and Protective Factors in Teacher Induction for Prospective Elementary and Physical and Health Education Teachers

The growing body of literature on teacher induction barriers has revealed the need to address issues that may lead to negative consequences. Recent research points to an increased interest in teacher resilience, a concept that promotes positive adaptation to counteract these adverse outcomes. However, teacher induction and resilience may differ depending on teaching context. For example, do specialists such as physical and health education teachers experience the same induction situation as generalist teachers? The authors aimed to compare the anticipated risk and protective factors related to the induction of these prospective teachers. The data from two studies were analyzed using thematic coding. The results indicate similarities and differences between the two groups that suggest ways to better support teacher induction.
The Quaternary geomorphological evolution of the River Murray mouth and lakes region, southern Australia

This thesis examines the Quaternary geomorphological evolution of the River Murray Mouth region of southern Australia in the context of increasing aridity and fluctuations in eustatic sea level and climate. The region is located at the northern margin of the Coorong Coastal Plain, which preserves a long terrestrial record of Quaternary sea-level highstands within the Bridgewater Formation, a sedimentary succession of coastal carbonate aeolianites with associated beach and back-barrier lagoon facies. The stratigraphic record within the study area has been obscured by long term regional subsidence due to its location adjacent to the uplifting Mount Lofty Ranges, erosion by the River Murray and the dunefields of siliceous Molineaux Sands deposited under continental conditions during periods of glacially low sea levels. Unravelling the depositional record within this region assists in understanding localised variations in the sea-level record, the position of the River Murray during eustatic sealevel fluctuations and the dichotomy of sedimentary successions between the terrestrial, arid climates of glacial periods and marginal marine, wetter climates of interglacial periods. The development of the Pleistocene coastline has been extrapolated from the modern beach-barrier-lagoon analogue, following the principle of uniformitarianism. Pleistocene sedimentary successions were recognised by facies architecture and lithology with depositional and diagenetic environments determined through petrographic analyses. The correlation of the sedimentary successions was based on their morphostratigraphic relationships and geochronological analyses using amino acid racemisation, thermoluminescence, optically stimulated luminescence and radiocarbon methods. The results of these analytical techniques were correlated with the Coorong Coastal Plain sea-level highstand record and the oxygen isotope record. The calcium carbonate content of the Bridgewater Formation aeolianite in the study area is regionally variable and lower than the Coorong Coastal Plain equivalents with consequently higher quartz content. This is attributed to terrestrial sediment input by the River Murray, the quartz-rich character of the offshore shelf environment,
The Oxidation of Hydrogen Peroxide on Nanostructured Rhodium The Oxidation of Hydrogen Peroxide on Nanostructured Rhodium Microelectrodes Microelectrodes

: Mesoporous Rh films were deposited onto platinum microelectrodes from the H 1 lyotropic liquid crystalline phase of C 12 EO 8 (octaethyleneglycol monododecyl ether). The electrodes show well defined voltammetry for the oxidation and the reduction of hydrogen peroxide at low concentrations (<10 mmol 窑 L -1 ) with excellent stability for operation at neutral pH. Based on the hysteresis in the current and the potential dependence the oxidation of hydrogen peroxide occurs through a CEE mechanism involving Rh(OH) 3 on the mesoporous Rh electrode surface. At higher hydrogen peroxide concentrations the current reaches a plateau that is due to either saturation of the binding sites for hydrogen peroxide or limitation of the reaction due to acidification of the solution within the pores. For the thin films (below 200 nm) the hydrogen peroxide calibration curves we fitted to a one dimensional model for diffusion and reaction within the pores.
Semi-Supervised Dimensionality Reduction Using Pairwise Equivalence Constraints

To deal with the problem of insufficient labeled data, usually side information -- given in the form of pairwise equivalence constraints between points -- is used to discover groups within data. However, existing methods using side information typically fail in cases with high-dimensional spaces. In this paper, we address the problem of learning from side information for high-dimensional data. To this end, we propose a semi-supervised dimensionality reduction scheme that incorporates pairwise equivalence constraints for finding a better embedding space, which improves the performance of subsequent clustering and classification phases. Our method builds on the assumption that points in a sufficiently small neighborhood tend to have the same label. Equivalence constraints are employed to modify the neighborhoods and to increase the separability of different classes. Experimental results on high-dimensional image data sets show that integrating side information into the dimensionality reduction improves the clustering and classification performance.
Identification of Banana Ripeness using Convolutional Neural Network Approaches

Digital images have been widely used for computer vision systems of ripe bananas. Consumers or industrial companies need computer vision-based applications to determine the ripeness of bananas. The computer vision system is challenging because it processes images and must find a good method. In Saranya’s 2022 research, the Convolution Neural Network method is accurate. We propose to improve the CNN architecture to identify banana ripeness. We use the dataset from the Saranya 2022 research, and we augment the dataset to make it large. Proposed CNN architecture: We have 97.95% accuracy with the adam optimizer. Our proposed CNN architecture results are better than Saranya’s 2022 research.
Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level.
Analysis of Factors Influencing the Intention of Using Digital Banking Through Social Media

With the digitization of the banking sector and the use of social media in daily activities, the world of technology has developed significantly daily. The availability of social media as a source of communication and information gathering can be used to measure user intentions in using digital banks. This research is intended to analyze the factors or variables that influence the intentions of digital bank users through information conveyed through social media. This study adopts the Extended UTAUT research model, which includes Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Information Quality, Behavior Intention, and User Behavior variables. The research data was collected by distributing questionnaires through Google Forms and using Likert Scale with non-probability type snowball sampling to respondents. The data obtained is then processed with the SmartPLS software. Based on the test results shows that the variables Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) do not have a significant effect. In contrast, the Social Influence (SI) and Information Quality (IQ) variables significantly influence the Intention to use Digital Banks through information on Social Media.
Neuromorphic Neurons and Networks for Artificial Intelligence Built Using Temporal Space Calculations

We deigned a spiking neural network that computes network weights in the temporal dimension. Such a network can be used for artificial intelligence and deep learning. We demonstrate circuits implementing blocks for building such a network and then a training model. This enables creation of efficient Hessenstein-Reichardt detectors observed in motion detection networks in the nature. The proposed network allows reconfiguration across network layers algorithmically.
A High-speed and Low-power FPGA Implementation of Spiking Convolutional Neural Network Using Logarithmic Quantization

Spiking neural networks (SNN) have good computational energy efficiency and biological plausibility, and are promising for applications in low-power computing and brain-like computing. To achieve hardware acceleration of convolutional SNN (i.e., SCNN), we propose a configurable and extensible hardware architecture based on clock-driven design. In this architecture we use a strategy of output-channel-wise parallelism and hierarchical pipeline structure to speed up the computation. Aiming at retaining the performance when reducing the quantization bits, the adaptive channel-wise logarithmic quantization (ACLQ) method for SCNNs is proposed to ensure performance while significantly reducing the overhead on hardware resources such as on-chip memory. We configured and evaluated two sizes of LeNet networks model based on the proposed architecture in FPGA. Experiments show that our architecture achieved recognition accuracy of up to 99.26% on the MNIST dataset, while achieving a recognition speed of up to 1605 frames per second (FPS) at a 100MHz clock speed and consuming only 0.65mJ of energy to process each image. Using the proposed logarithmic quantization method, the weights’ bit width can be reduced to 3 bits with almost none of accuracy loss, which significantly reduces the on-chip resources and power consumption for weights storage.
Odonatos asociados a cursos estacionales de Sierra Morena (sur de España)

espanolSe estudia la fauna de odonatos existente en dos cursos estacionales de Sierra Morena. De las 21 spp observadas, dos terceras partes fueron anisopteros. Es destacable la presencia en este tipo de cursos de Onychogomphus forcipatus, Oxygastra curtisii, Libellula depressa y Orthetrum brunneum. Fue registrada abundante actividad reproductora exclusivamente otonal de Chalcolestes viridis, Aeshna mixta y Sympetrum striolatum EnglishThe dragonfly and damselfly fauna existing in two seasonal streams of the Sierra Morena Mountains is shown and discussed. Two thirds of the 21 spp recorded were anisopteran. Presence in such watercourses of Onychogomphus forcipatus, Oxygastra curtisii, Libellula depressa y Orthetrum brunneum is noteworthy. Abundant exclusively autumnal reproductive activity of Chalcolestes viridis, Aeshna mixta y Sympetrum striolatum was recorded
between Alzheimer patients and normal control Use of computerized CT analysis to discriminate

A newly developed computerized technique was used to analyze the CT scans of 49 patients with dementia of the Alzheimer type and 31 normal control subjects. Nine brain regions distributed across five CT slices were evaluated for each individual. For the purpose of analysis, the patients and controls were divided into an exploratory set and a test set. Several discriminant functions were conducted on the exploratory set and applied to the test set. The combination of variables that focused on regions in the temporal lobe was most accurate in differentiating Alzheimer patients from controls (94%). This degree of accuracy was achieved only when subjects younger than 65 years old were analyzed separately from those 65 years old and older. The newly developed computer software program was able to discriminate between independently selected groups of Alzheimer patients and control subjects. The program was most effective when the analysis emphasized regions in the temporal lobe and when subjects younger than 65 years old were analyzed separately from those 65 years old and older.
Understanding the Factors that Influence Users Continued Intention to Use Social Media

Our daily lives now almost must involve with Social Media Applications, and because of how convenient it is to use them and also with them is how we connected between each other’s Social Media apps are becoming more and more common. Accessibility and convenience do, however, come with concerns about the security of continuance usage of using social media applications. The definition of “Security” refers to perceived safety, which is the level of trust that individuals have in each social media. Investigating the Drivers of Users’ Persistent Intention to Use social media is the focus of this paper. A survey was conducted with 560 respondents who used e-Social Media apps at least once. The study’s goal was to ascertain how well respondents comprehended the factors that affect the continuity’s security. Utilizing a Social Media App. The study’s findings indicated that most participants are aware of the security of using Social Media Applications. Moreover, users of Social Media applications should pay more attention to Security and Behavior Intention in order to increase user continuance Usage towards the Social Media applications and the application’s ability to conduct safe security towards the application. The results of this study point to the necessity for Social Media application developers to play a more active role in informing their users of the value of security so that will make the user want to keep continue using Social media Applications.
Analysis Intention to Use of Smart Tourism Application with Model Extended UTAUT 2 Approach

The development of information technology currently provides great opportunities for tourism and tourist businesses. All developments provide useful application effectiveness for tourists. Therefore, it is important and useful to explore the factors that influence the intention to use tourism applications among tourists. This study aims to understand the basic factors that influence user intentions to adopt and use smart tourism applications among college students. This study uses 61 indicators with nine constructs to form the proposed research model on the basis of the Extended UTAUT2 model. This study used an online survey using Google Forms with the results of 278 valid and appropriate respondents. Analysis was performed with 278 data using SMART PLS 4.0. The analysis of testing the fit model shows that the results are good, namely R2 of 0.743. Also, found that of the eight hypotheses designed six hypotheses were not supported, namely Performance Expectancy, Hedonic Motivation, Effort Expectancy, Social Influence, Facilitating Conditions, and habit, which had no effect on tourist Behavior Intention. Two hypotheses were supported, namely, that deal proneness and pricing value have a crucial impact in raising tourist intention to utilize the application. The application provides easy transaction services with various payment methods, fast payments, high security for transaction convenience, provides promotional offers, and rewards. So, the tourist industry in developing tourism applications can fulfill users' wishes.
Knowledge and Perceived Security as Driven The Continuance Use of Mobile Fintech Payments

The issue of security in the use of payment-based technology is considered essential to continue to be studied because of the impact of advances in technology and information. One of the payment methods that is developing in Indonesia is m-fintech. Therefore, this study analyzes the aspects driving the continuance intention of m-fintech payments, namely knowledge, perceived security, and perceived usefulness. This study uses a questionnaire as the primary data to test the hypothesis. Questionnaires were distributed online to 357 consumers using m-fintech payments in Jabodetabek. Therefore, the sampling technique used is purposive. Partial Least Square-Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. 8.9 software is used to answer the hypothesis. The results of this study prove that knowledge, perceived security, and perceived usefulness are good predictors in explaining the continuance intention of m-fintech payments. In detail, knowledge influenced perceived security, and perceived usefulness influenced the continuance intention of m-fintech payments. Finally, perceived security and usefulness affect the continuance intention of m-fintech payments. These findings contribute to m-fintech payment companies continuing to improve the security of m-fintech payments in line with improvements in technology and information.
Variable on SME Fashion Industry With NFT Blockchain as the Design Protection and Counterfeiting Measurement Using Narrative Analysis

NFT development, backed by blockchain technology, is still in motion and ongoing though the hype has cooled off along with the advance of the technology. With the advancement of technology, fashion is also one thing that gets affected. Fashion, one of the human necessities, has given many business opportunities, with the SME fashion industry having a big part in it. With their limited budget, sadly, they didn't have many options to handle some of the biggest challenges in the fashion industry, which poached design and counterfeiting. Fortunately, NFT has given some solutions that could help cope with those problems. The NFT with blockchain technology could be used to protect how it works by making a unique signature to the digitized asset it is associated with. Real physical goods could be linked to the digitized version with NFT technology for recognition and protective purpose. It will help prove the digital goods' ownership and the linked physical ones. This paper sought to find out what variables affect the fashion SME industry with the NFT as the design protection and counterfeit measurement by using the qualitative narrative analysis from the data gathered using the conducted storytelling interview. The variable will then be compared with the previous study's findings for further analysis and results.
Correspondence-Free Point Cloud Registration Via Feature Interaction and Dual Branch [Application Notes]

Point cloud registration, which effectively coincides the source and target point clouds, is generally implemented by geometric metrics or feature metrics. In terms of resistance to noise and outliers, feature-metric registration has less error than the traditional point-to-point corresponding geometric metric, and point cloud reconstruction can generate and reveal more potential information during the recovery process, which can further optimize the registration process. In this paper, CFNet, a correspondence-free point cloud registration framework based on feature metrics and reconstruction metrics, is proposed to learn adaptive representations, with an emphasis on optimizing the network. Considering the correlations among the paired point clouds in the registration, a feature interaction module that can perceive and strengthen the information association between point clouds in multiple stages is proposed. To clarify the fact that rotation and translation are essentially uncorrelated, they are considered different solution spaces, and the interactive features are divided into two parts to produce a dual branch regression. In addition, CFNet with its comprehensive objectives estimates the transformation matrix between two input point clouds by minimizing multiple loss metrics. The extensive experiments conducted on both synthetic and real-world datasets show that our method outperforms the existing registration methods.