Miracle-dz/UniFAR-FTUs huggingface dataset:
Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions

Comparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: “Is Lyft cheaper than a regular taxi? ” or “What makes a mortgage different from a regular loan? ”. In this paper, we propose two new open-domain real-world datasets for identifying and labeling comparative questions. While the first dataset contains instances of English questions labeled as comparative vs. non-comparative, the second dataset provides additional labels including the objects and the aspects of comparison. We conduct several experiments that evaluate the soundness of our datasets. The evaluation of our datasets using various classifiers show promising results that reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. When approaching the unsupervised sequence labeling task, some headroom remains.
Social support and positivity: Analyzing user-generated comments on the Instagram pages of two Brazilian cancer hospitals

Online social media platforms are an important tool for individuals who wish to exchange ideas, stories, feelings, information. Hospitals and public health organizations can use social media platforms, including Instagram, to engage, interact or communicate with patients, family members and anyone interested in health information or new discoveries. The purpose of this study is to understand the main characteristics of user-generated digital narratives on the Instagram pages of two Brazilian cancer hospitals. The study fills a gap in the literature on cancer communication by studying a language other than English in a Latin American context, and examining user-generated comments over a period of more than 6 years. The findings of the authors’ content analysi s show a preponderance of positive and social support messages, even though cancer is a disease laden with negative stigma. Religiosity, which can strengthen supportive social ties and create a stronger sense of empowerment and control, was also a hallmark of these narratives. In the content analysis of Brazilian cancer hospitals’ Instagram accounts, the predominant characteristic of user comments was that of positivity, even though cancer is a disease laden with negative stigma.
APPLICATION OF MACHINE LEARNING FOR ESTIMATING KENYAN MOTOR VEHICLE INSURANCE PREMIUM

Motor vehicle damage insurance is the most common type of insurance in the world and one that generates the largest amount of loss for most insurance companies. In Kenya especially, the challenge faced by insurers is to balance the growth of the motor vehicle insurance business by increasing the customer base while also maintaining the profitability of this sector. It is crucial to identify the main causes of motor vehicle damage, its impact on revenue for insurers and factors that contribute to high motor claims to enable more accurate estimates of risk versus premium paid. In recent years the interest has increased in the use of information technology (IT) and statistical machine learning methods, supported by increasing computing capabilities, data availability and the trend towards automation. Statistical regression models have numerous applications in this regard. This paper explores applicability of new machine learning techniques such as tree-boosted models to optimize the proposed premium of prospective policy holders. It proposes two machine learning models for pricing motor vehicle damage insurance (decision trees and regression). The aim is to identify sources of risks in motor vehicles and the variables for motor vehicle premium determination. Data from insurance companies has been used, which is made up of the premium rates and compensations, and other variables such as age, driver's experience, etc. Results will be used to advise the insurance companies on how to charge premiums dynamically.
Analysis of the Effect of External Environment and Marketing Strategy on the Marketing Performance of MSME Fashion

AbstractThe purpose of this study was to determine the effect of the external environment and marketing strategy on the marketing performance of fashion SMEs in the Bandung area. This research use quantitative methods. Primary and secondary data were analyzed in this study. The study's preliminary results comes from questionnaires administered to ten respondents using the Likert scale. Validation tests, reliability tests, and hypothesis testing were used in this study's data analysis. According to the statistics and study findings, external environmental variables have a positive but non-significant effect on marketing performance; and marketing strategy has a positive but non-significant effect on marketing performance.Keywords: Analysis, Influence, External Environment, Marketing Strategy, Marketing Performance, MSMEs. AbstrakTujuan penelitian ini adalah untuk mengetahui pengaruh lingkungan eksternal dan strategi pemasaran terhadap kinerja pemasaran UKM fashion di wilayah Bandung. Penelitian ini menggunakan metode kuantitatif. Data primer dan sekunder dianalisis dalam penelitian ini. Hasil awal penelitian ini berasal dari kuesioner yang diberikan kepada sepuluh responden dengan menggunakan skala Likert. Uji validasi, uji reliabilitas, dan uji hipotesis digunakan dalam analisis data penelitian ini. Menurut statistik dan temuan studi, variabel lingkungan eksternal memiliki pengaruh positif tetapi tidak signifikan terhadap kinerja pemasaran; dan strategi pemasaran berpengaruh positif namun tidak signifikan terhadap kinerja pemasaran.Kata kunci: Analisis, Pengaruh, Lingkungan Eksternal, Strategi Pemasaran, Kinerja Pemasaran, UMKM.
NOVEL SINGLE TREE DETECTION BY TRANSFORMERS USING UAV-BASED MULTISPECTRAL IMAGERY

Abstract. Single tree detection has been a major research topic concerning automatic forest inventory using remote sensing data. Recently, deep learning-based approaches in remote sensing forestry have gained attention because of the prospect of improved accuracy. In this study, we present a novel tree detection method based on the detection transformer (DETR), which applies a transformer in combination with a pre-trained convolutional neural network to detect individual trees using high-resolution multispectral imagery. The test site (Kranzberg Forest Roof Experiment - KROOF) is located in Bavaria, north of Munich, and is characterised by a mixed forest which consists of large groups of European beeches (Fagus sylvatica) surrounded by Norway spruces (Picea abies). The image data were acquired with a MicaSense RedEdge-MX Dual camera mounted to UAV. Two flight mission were conducted at an altitude of around 85 m with a flight speed of 5 m/sec, resulting in a ground resolution of about 5 cm. 125 trees were surveyed by tacheometric means in the field for testing, and 1390 trees were labelled by visual interpretation of the multispectral imagery for training and validation. The novel tree detection method based on DETR shows promising results and outperforms the standard, well-known object detection method YOLOv4 in mixed and deciduous test plots. More detailed, F1-scores were evaluated for coniferous plot at 83%, for mixed plot at 86% and for deciduous plot at 71%. The corresponding figures for YOLOv4 are 87% coniferous, 65% mixed and 67% deciduous. In terms of accuracy, DETR is inferior by 6% in coniferous plot, however superior by 28% and 5% in mixed and deciduous plot, respectively. Compared to YOLOv4, we found that DETR sometimes failed to detect small coniferous trees. Moreover, both deep learning-based methods tend to over-detect single trees in deciduous test areas. In sum, transformer-based tree detection shows great potential to improve single tree detection.
Cryo-Structuring of Polymeric Systems. Poly(Vinyl Alcohol)-Based Cryogels Loaded with the Poly(3-hydroxybutyrate) Microbeads and the Evaluation of Such Composites as the Delivery Vehicles for Simvastatin

Highly porous composite poly(vinyl alcohol) (PVA) cryogels loaded with the poly(3-hydroxybutyrate) (PHB) microbeads containing the drug, simvastatin (SVN), were prepared via cryogenic processing (freezing—storing frozen—defrosting) of the beads’ suspensions in aqueous PVA solution. The rigidity of the resultant composite cryogels increased with increasing the filler content. Optical microscopy of the thin section of such gel matrices revealed macro-porous morphology of both continuous (PVA cryogels) and discrete (PHB-microbeads) phases. Kinetic studies of the SVN release from the drug-loaded microbeads, the non-filled PVA cryogel and the composite material showed that the cryogel-based composite system could potentially serve as a candidate for the long-term therapeutic system for controlled drug delivery. Such PHB-microbeads-containing PVA-cryogel-based composite drug delivery carriers were unknown earlier; their preparation and studies have been performed for the first time.
Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico

In this study, we analyzed the sequences of SARS-CoV-2 isolates of the Delta variant in Mexico, which has completely replaced other previously circulating variants in the country due to its transmission advantage. Among all the Delta sublineages that were detected, 81.5 % were classified as AY.20, AY.26, and AY.100. According to publicly available data, these only reached a world prevalence of less than 1%, suggesting a possible Mexican origin. The signature mutations of these sublineages are described herein, and phylogenetic analyses and haplotype networks are used to track their spread across the country. Other frequently detected sublineages include AY.3, AY.62, AY.103, and AY.113. Over time, the main sublineages showed different geographical distributions, with AY.20 predominant in Central Mexico, AY.26 in the North, and AY.100 in the Northwest and South/Southeast. This work describes the circulation, from May to November 2021, of the primary sublineages of the Delta variant associated with the third wave of the COVID-19 pandemic in Mexico and highlights the importance of SARS-CoV-2 genomic surveillance for the timely identification of emerging variants that may impact public health.
The Effects of COVID-19 on Family Climate: A Fuzzy Clustering Approach to Examine Spanish Households

Spain was one of the countries in which more severe lockdown policies were imposed during the second term of 2020 to mitigate the unprecedented health crisis. The measures restricted citizens’ mobility, obliging families to stay confined at homes for 99 days since 15 March 2020. The measures created a number of challenges that affect the family climate. This paper aims to empirically analyse how the family climate in Spain has been affected by COVID-19. The family climate assessment was based on an online questionnaire answered by 2034 citizens. A multi-criteria decision-making method rooted in fuzzy logic and TOPSIS, and a fuzzy clustering method, are applied to analyse the effects of the COVID-19 on the family climate. The fuzzy clustering method reveals that there are three different family climate profiles, namely (1) extreme positive, (2) extreme negative, and (3) intermediate. Our results show that some traits affect having a more or less positive family climate. The authors discuss the main contributions and the policy implications that could provide insights into future measures.
A comparative Use of Traditional and Multimedia Modes of Teaching Curriculum Studies in English

This study explored students’ preferences on the use of traditional versus multimedia modes of course delivery at the University of Eswatini. Since the advent of technology, the use of traditional and multimedia modes of teaching has received a lot of attention in research. Students’ preferences regarding either being taught in a traditional or technological way is one of the areas that has been investigated intensively, yielding different conclusions. This study was a cross-sectional survey. For data collection, the study used a five-point anchored Likert-scale. Forty-three (43) participants participated in the study. They completed a questionnaire that sought their preferences on the mode of teaching between two courses, CTE 319/519 and CTE320/520. CTE319/519 was taught using multimedia, and CTE 320/520 was taught using the traditional mode. To establish the trends in the students’ preferences, data were analyzed using the statistical package for social science (SPSS), mainly descriptive statistics. The mean, frequencies, modes, and standard deviation were the major domains for data interpretation. The results revealed that the students preferred the use of multimedia over the traditional mode of course delivery. However, when the data were further analyzed using the age-range, the older generation performed better than the younger generation in their preferences on the use of multimedia when teaching. Overall, the results have implications for the integration of multimedia technology when teaching English courses at tertiary.
The Effect of Soil Water Deficiency on Water Use Strategies and Response Mechanisms of Glycyrrhiza uralensis Fisch

We aimed to investigate the water use strategies and the responses to water shortages in Glycyrrhiza uralensis, which is a dominant species in the desert steppe. Water stress gradients included control, mild, moderate, and severe. The time intervals were 15, 30, 45, and 60 d. Our study suggested that with the aggravation of water stress intensity, the total biomass of Glycyrrhiza uralensis gradually decreased and allometric growth was preferred to underground biomass accumulation. From 30 d and mild to moderate water stress, the water potential (WP) of leaves decreased considerably compared to the CK. The relative water content (EWC) decreased over time and had a narrow range of variation. Proline (PR) was continuously increased, then declined at 45–60 d under severe and more severe water stress. The δ13C values increased in all organs, showed roots > stems > leaves. The net photosynthetic rate (Pn) and transpiration rate (Tr) decreased to varying degrees. The instantaneous water use efficiency (WUEi) and limiting value of stomata (Ls) increased continuously at first and decreased under severe water stress. Meanwhile, severe water stress triggered the most significant changes in chloroplast and guard cell morphology. In summary, Glycyrrhiza uralensis could maintain water content and turgor pressure under water stress, promote root biomass accumulation, and improve water use efficiency, a water-conservation strategy indicating a mechanism both avoidable dehydration and tolerable drought.
The Problematic Distribution of the Irish Past Tense Marker d’ : Development of a case of phonologically conditioned allomorphy

The initial mutations of Irish and the other Celtic languages have been the subject of numerous analyses from both phonological and morphological perspectives. These mutations interact with the Irish verbal system in an apparently paradoxical way for a set of verbs with an initial fC- consonant cluster, such that there is no straightforward way to reconcile the operation of mutation and the (non-)deletion of the past tense marker d’ in verbs such as d’fhreagair ‘answered’. This article presents two analyses to account for this phenomenon, one in an autosegmental framework of phonology using Gussmann’s (1986) mechanism of latent segments, and one building on Pyatt’s (1997) Distributed Morphology account of mutations. I argue that the allomorphy of d’ may be considered a case of non-optimising Phonologically Conditioned Suppletive Allomorphy (PCSA, Paster 2006), and, drawing comparisons to a similar phenomenon in Welsh, that a staggered insertion analysis within DM may therefore be most suitable. In general, these data suggest that the flexibility of DM in allowing for phonological subcategorisation may render it the best choice for modelling Celtic mutations. Furthermore, they contribute to existing literature arguing for the appropriateness of subcategorisation models over constraint-satisfaction models in cases of non-optimising PCSA. Finally, I suggest two historical explanations for the Irish data: one in terms of abductive reanalysis of a previous regular pattern, and another in which orthographic norms could have influenced the spoken language – leaving this final question open to future study.
Targeting TOR and SnRK1 Genes in Rice with CRISPR/Cas9

Genome targeting with CRISPR/Cas9 is a popular method for introducing mutations and creating knock-out effects. However, limited information is currently available on the mutagenesis of essential genes. This study investigated the efficiency of CRISPR/Cas9 in targeting rice essential genes: the singleton TARGET OF RAPAMYCIN (OsTOR) and the three paralogs of the Sucrose non-fermenting-1 (SNF1)-related kinase 1 (OsSnRK1α), OsSnRK1αA, OsSnRK1αB and OsSnRK1αC. Strong activity of constitutively expressed CRISPR/Cas9 was effective in creating mutations in OsTOR and OsSnRK1α genes, but inducible CRISPR/Cas9 failed to generate detectable mutations. The rate of OsTOR mutagenesis was relatively lower and only the kinase domain of OsTOR could be targeted, while mutations in the HEAT region were unrecoverable. OsSnRK1α paralogs could be targeted at higher rates; however, sterility or early senescence was observed in >50% of the primary mutants. Additionally, OsSnRK1αB and OsSnRK1αC, which bear high sequence homologies, could be targeted simultaneously to generate double-mutants. Further, although limited types of mutations were found in the surviving mutants, the recovered lines displayed loss-of-function or knockdown tor or snrk1 phenotypes. Overall, our data show that mutations in these essential genes can be created by CRISPR/Cas9 to facilitate investigations on their roles in plant development and environmental response in rice.
A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection

In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.
Nonlinear Creep Amplification Factor Considering Damage Evolution of Concrete under Compression

Creep affects the long-term deformation of concrete structures. Nonlinear creep further overestimates the safety factor of structures and affects the safety service performance. The coupling of creep and a damage model considering the rate effect is conducive to accurate prediction of nonlinear creep, but the iterative process of strain makes the calculation method more complex. The purpose of this study is to propose a nonlinear creep explicit method that considers the damage evolution of concrete under compression. Two groups of axial compression members with compressive stresses of 0.2 fc and 0.4 fc were made. Considering the law of concrete damage evolution under uniaxial compression, coupled with elastic creep and damage incremental strain, the lower limit of the medium stress level that gives rise to nonlinear creep is analyzed. The concrete nonlinear creep amplification coefficient with a loading age of 28 days and loading duration of 360 days is studied with consideration for the uncertainty of relative humidity and the theoretical thickness of the component. On this basis, the explicit calculation formula of the nonlinear creep amplification coefficient related to the concrete axial compressive strength and stress level is given. The results indicate that the nonlinear creep amplification coefficient increases nonlinearly with an increase in the stress level, and, when the compressive stress level ratio is higher than 0.6, the nonlinear creep amplification coefficient increases significantly; when the stress level is determined, the creep amplification coefficient decreases gradually with an increase in the compressive strength of the concrete. It is suggested that a stress level range of 0.35~0.75 should be used for the study of a nonlinear creep amplification factor under the medium stress state.
Survival Analysis and Associated Factors for Pulmonary Tuberculosis Death: Evidence from the Information System of Tuberculosis Disease and Mortality Surveillance in China

Background Pulmonary tuberculosis (PTB) is a severe infection, and its factors that influence survival have not been explored in some developed regions. Therefore, this study aimed to identify the factors that influence TB survival in China. Methods We acquired data of PTB deaths through matching two manage systems of PTB and mortality surveillance. The Kaplan–Meier method and Cox regression model were used to identify the significant factors under various survival times. Results A total of 283 deaths were caused by PTB, in which 53.4% occurred during the first year. In the Cox regression model, regular sputum smear test could reduce the risk of PTB death from the result of one-, three-, and five-year survival; and history of PTB treatment could decline the risk of PTB death in the first year. Additionally, age of “75–90 years” and positive result of sputum smear test significantly increased the risk of PTB death for the findings of three- and five-year survival, while age of “60–75 years” could increase the probability of PTB death for five-year survival. Conclusion To reduce PTB deaths, more attention should be paid to the older group especially in the first year. Regular sputum smear test could effectively reduce the mortality of PTB cases. More evidence-based interventions should be implemented to enhance the survival of patients with PTB.