Miracle-dz/UniFAR-FTUs huggingface dataset:
“Because I’m Old”: The Role of Ageism in Older Adults’ Experiences of Digital Literacy Training in Public Libraries

Abstract This article explores the experiences of older adults attending digital literacy training sessions offered by the public library system in one city in Ontario. Semi-structured interviews with 12 older adults (age 60+) demonstrated the significance of societal and internalized ageism in shaping the experiences of participants, as well as the construction of the training as a “safe space” for experimentation. Implications for participants and future public library digital literacy training sessions are discussed, with particular attention to asset-based approaches, the need for skill development, and addressing the effects of ageism on participants. Supplemental data for this article is available online at https://doi.org/10.1080/15228835.2021. 1962477 .
A review of transfer learning for medical image classification

21 This review paper provides an overview of the peer-reviewed articles using transfer learning for 22 medical image analysis, while also providing guidelines for selecting a convolutional neural 23 network model and its configurations for the image classification task. The data characteristics 24 and the trend of models and transfer learning types in the medical domain are additionally 25 analyzed. Publications were retrieved from the databases PubMed and Web of Science of peer- 26 reviewed articles published in English until December 31, 2020. We followed the PRISMA 27 guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this 28 review. With respect to the model, the majority of studies (n=57) empirically evaluated numerous models followed by deep (n=33) and shallow (n=24) models. With respect to the 30 transfer learning approaches, the majority of studies (n=46) empirically searched for the optimal 31 transfer learning configuration followed by feature extractor (n=38) and fine-tuning scratch 32 (n=27), feature extractor hybrid (n=7) and fine-tuning (n=3). The investigated studies showed 33 that transfer learning demonstrates either a better or at least a similar performance compared to 34 medical experts despite the limited data sets. We hence encourage data scientists and 35 practitioners to use models such as ResNet or Inception with a feature extractor approach, 36 which saves computational costs and time without degrading the predictive power.
Senior High School Students' Perception on the Use of Masks to Prevent COVID-19 in Ghana

The present study examines the perception of senior high school (SHS) students on the coronavirus and the use of masks to prevent the disease. Semi-structured interviews were employed as a form of data collection with purposively sampled twenty-four (24) senior high school students comprising twelve (12) males and twelve (12) females aged sixteen to nineteen (16-19) years. Thematic analysis was used in analysing the qualitative data gathered. Six (6) themes emerged that centered around SHS students' perception of the coronavirus, wearing of masks, health effects and other general ideas on the pandemic. The main themes were the mode of spread, symptoms, prevention, reasons for wearing masks, the health effects of wearing masks and other issues. The findings were discussed in line with recent literature and recommendations have been made to the stakeholders for policy direction. In sum, the students usually follow the safety protocols instituted by the World Health Organisation (WHO) and the government to stay alive and protected as they are aware of the adverse effects of contracting the disease which include death and liver damage when one even recovers.
Partition-Aware Graph Pattern Based Node Matching With Updates

<italic>Graph Pattern based Node Matching</italic> (GPNM) is to find all the matches of the nodes in a data graph <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq1-3103914.gif"/></alternatives></inline-formula> based on a given pattern graph <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>P</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq2-3103914.gif"/></alternatives></inline-formula>. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>P</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq3-3103914.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq4-3103914.gif"/></alternatives></inline-formula> are updated frequently. However, the existing GPNM methods either need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>P</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq5-3103914.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq6-3103914.gif"/></alternatives></inline-formula> or incrementally perform the GPNM procedure for each of the updates, leading to low efficiency. Although the elimination relations between updates and partitions of data graphs are considered in the state-of-the-art method, it still suffers from low efficiency as only the labels of nodes are considered in the partitions. Therefore, there is a pressing need for a new method to efficiently deliver the node matching results on the updated graphs. In this paper, we propose a new Partition-aware GPNM algorithm, called P-GPNM, where we propose two new partition methods, i.e., <italic>connection-based partition</italic> and <italic>density-based partition</italic>. In these two methods, P-GPNM considers the dense connections between partitions and the inner connections inside a single partition, respectively. The experimental results on five real-world social graphs demonstrate that our proposed P-GPNM is much more efficient than the state-of-the-art GPNM methods.
Integrating Social Skills in Traditional Games with Physical Education Interventions

This study was based on the previous research which found that traditional games given at the elementary level were significantly effective for the improvement of students' socialization in the classroom and the improvement of the quality of students' relationships between classes. This study aimed to examine whether traditional games could affect social skills. The design used in this research was a 3x2 factorial study with the randomized pretest-posttest control group design, while 90 samples were divided into three groups. The result of this research revealed that: 1) there were differences in social skills between groups given traditional games with social elements and groups given traditional games without any social elements. 2) Students with Dayak ethnic/cultural background showed better social skills and social behaviour than students with Malay and Javanese ethnic/cultural backgrounds. 3) Traditional games that contain social elements with Dayak cultural background had a better influence on elementary school students' skills and social behaviour than other groups. 4) There was an interaction between the types of traditional games with cultural/ethnic backgrounds of social skills, while for social behavior; there was no interactional relationship between the two.
Students’ frequency of access to school library materials in transformative times

The COVID-19 pandemic triggered many school and school library closures, resulting in shifts to online and/or hybrid instruction and limited school library access. This survey of parents of PreK–12 students (aged 2–18 years) investigated students’ frequency of access to school library materials prior to (T1), during (T2), and predicted after (T3) the pandemic (n = 230). Demographics such as age, gender, race, and ethnicity and other factors such as household income, community type, geographic location, type of school, school environment, and number of books in home were collected. Frequency of access to school library materials was compared at T1, T2, and T3 by demographic and other factors. Results demonstrate that frequency of access to school library materials differed significantly between time points. Repeated-measures analysis of variance showed a significant difference between T1 and T2 and between T2 and T3 but not between T1 and T3. Significant interactions were found for age and frequency of access over time, with age group 6–10 years showing the most change. Significant differences were found for all three types of school environment, with face-to-face students showing less disruption in their access than online and hybrid students. Students’ frequency of access to school library materials was negatively influenced by closures and limited access, with the expectation of a return to similar frequency of access on resumption of normal operations. Given the strong associations between access to school library resources and academic performance, these results suggest student learning may have suffered during these closures.
MADRASAH INFRASTRUCTURE MANAGEMENT BEFORE FACE-TO-FACE LEARNING IN THE PANDEMIC

This current study aimed to analyze and comprehend the management of the madrasa's facilities and infrastructure to hold face-to-face learning during the Covid-19 pandemic. This study was conducted at MI Soebono Mantofani, which is located in Ciputat, South Tangerang. The study employed a descriptive approach with qualitative methods. Data were obtained through interviews, observation, and documentation. The data analysis technique in this study followed the procedures: data reduction, data presentation, conclusion drawing, and verification. The study results highlighted the importance of needs analysis before preparing facilities and infrastructure planning policy to procure facilities and infrastructure by available safety procedures. Besides, the school management system also endorsed the principal's critical role in preparing the recipes, organizing the facilities during face-to-face learning, and holding the maintenance process accountable. This study also provided implications for managing the facilities and infrastructure amidst pandemics and hopefully shed some light on other relevant stakeholders in facing the upcoming face-to-face learning in the middle of the pandemic.
5G UFMC Scheme Performance with Different Numerologies

5G is the latest mobile communications standard that is spreading fast across the world. Recently defined requirements for 5G systems have led to higher applications’ requirements regarding data rates, lower requirements for latency, and higher efficiency regarding the spectrum usage. Universal Filtered Multi-Carrier (UFMC) is one new candidate modulation scheme for emergent Fifth Generation (5G) communication systems. This paper focuses on Universal Filtered Multi-Carrier (UFMC) design aspects in terms of Bit Error Rate (BER) performance in relation to the filter length used in subband filtering. Simulation results show that BER and CCDF performance varies for different filter lengths and modulation schemes. The main achievement of this work is that the results show that different Dolph–Chebyshev FIR filter lengths do not affect the BER performance both for the 64 and 256 QAM.
Variation of Radar-Observed Precipitation Characteristics in Relation to the Simultaneous Passages of a Madden-Julian Oscillation Event and Convectively Coupled Equatorial Waves during the Years of the Maritime Continent Pilot Study

In this study, we examined the variations of precipitation morphology and rainfall in relation to the simultaneous passages of a Madden-Julian Oscillation (MJO) event and convectively coupled equatorial waves (CCEWs) observed during the Years of the Maritime Continent pilot study. We utilized globally merged infrared brightness temperature data and the radiosonde and radar data observed aboard the research vessel Mirai at 4°4′S, 101°54′E. As well as the observed MJO event, equatorial Rossby waves (ERWs), Kelvin waves (KWs), and mixed Rossby-gravity waves (MRGWs) were identified. The radar data exhibited high-frequency variation, mainly caused by KWs and MRGWs, and low-frequency variation, mainly caused by the MJO and ERWs. The MRGWs predominantly modulated convective echo areas and both convective and stratiform volumetric rainfall. In contrast, the MJO event had little influence on the variance of convective echoes. Moreover, stratiform echo areas and volumetric rainfall were more strongly modulated by the combined effects of the MJO, ERWs, KWs, and MRGWs than their convective counterparts. The intense development of stratiform echo areas and volumetric rainfall was coherent with the superimposition of the active phases of the MJO event and all the analyzed CCEWs. The strongest development and a significant reduction of convective echo-top heights before and after the peak MJO date, respectively, were coherent with the passages of ERWs and MRGWs, which were the dominant wave types in modulating echo-top heights. Thus, it appears that the superimposition of the CCEWs on the MJO event exerted complex modulations on the convective activities within the MJO event.
Frontal bone pneumatisation in Tragoportax and Miotragocerus (Mammalia, Bovidae) from the Late Miocene of Greece

ABSTRACT Bovidae display a distinctive cranial architecture, characterised by multifarious frontal sinuses varying in size and shape. These features are usually considered to be of phylogenetic importance and their functional importance still remains elusive. Pneumatisation is the process in which cranial sinuses result from the resorption and deposition of the bone in response to biomechanical stress. In this study, we use high resolution computed tomography (CT-scanning) to identify these internal cranial structures on 12 bovid cranial specimens from Upper Miocene Greek localities. The present study focuses on two genera, Tragoportax and Miotragocerus, so as to explore the variance inside this group in terms of frontal sinuses. The non-destructive nature of this method allows 3D digital representation of cranial internal features of fossilised material in high resolution, providing also volumetric data of the sinuses. We demonstrate the unique internal morphology that these species possess. This morphology is comprised by wide frontal sinuses conforming closely to the shape of the frontal bone and that extend up to the base of their horn cores. The volume of the sinuses was strongly correlated to the frontal bone size and cranial volume.
A review of how we assess denitrification in oyster habitats and proposed guidelines for future studies

Excess nitrogen (N) loading and resulting eutrophication plague coastal ecosystems globally. Much work is being done to remove N before it enters coastal receiving waters, yet these efforts are not enough. Novel techniques to remove N from within the coastal ecosystem are now being explored. One of these techniques involves using oysters and their habitats to remove N via denitrification. There is substantial interest in incorporating oyster‐mediated enhancement of benthic denitrification into N management plans and trading schemes. Measuring denitrification, however, is expensive and time consuming. For large‐scale adoption of oyster‐mediated denitrification into nutrient management plans, we need an accurate model that can be applied across ecosystems. Despite significant effort to measure and report rates of denitrification in oyster habitats, we are unable to create such a model, due to methodological differences between studies, incomplete data reporting, and inconsistent measurements of environmental variables that may be used to predict denitrification. To make a model that can predict denitrification in oyster habitats a reality, a common sampling and reporting scheme is needed across studies. Here, we provide relevant background on how oysters may stimulate denitrification, and the importance of oyster‐mediated denitrification in remediating excess N loading to coastal systems. We then summarize methods commonly used to measure denitrification in oyster habitats, discuss the importance of various environmental variables that may be useful for predicting denitrification, and present a set of guidelines for measuring denitrification in oyster habitats, allowing development of models to support incorporation of oyster‐mediated denitrification into future policy decisions.
Consistency or Hypocrisy? The Impact of Internal Corporate Social Responsibility on Employee Behavior: A Moderated Mediation Model

Adopting social identity theory, this study examined the process linking the relations between internal corporate social responsibility (InCSR), work engagement, and turnover intention by focusing on the mediating influence of organizational identification and the moderating role of perceived corporate hypocrisy. Data were obtained from 311 medical staff (excluding supervisors and managers) of a public regional teaching hospital in Taiwan. The results revealed that employees are more dedicated to work and less inclined to leave the firm if they perceive that InCSR is implemented within the firm. However, if an employee perceives corporate hypocrisy of inconsistency between communication and actual actions, it may have the opposite effect on employees. Likewise, the higher the level of perceived corporate hypocrisy, the lesser the positive effect of InCSR on employee behavior. Finally, the implications, limitations, and suggestions for future research were discussed.
Genetic Algorithm Based Improved ESTAR Nonlinear Models for Modelling Sunspot Numbers and Global Temperatures

Smooth Transition Autoregressive (STAR) models are employed to describe cyclical data. As estimation of parameters of STAR using nonlinear methods was time-consuming, Genetic algorithm (GA), a powerful optimization procedure was applied for the same. Further, optimal one step and two step ahead forecasts along with their forecast error variances are derived theoretically for fitted STAR model using conditional expectations. Given the importance of the issue of global warming, the current paper aims to model the sunspot numbers and global mean temperatures. Further, appropriate tests are carried out to see if the model employed is appropriate for the datasets.
“White” Self-Identification: A Source of Uniqueness Threat

The present research introduces the possibility that Whiteness can threaten majority group members’ sense of uniqueness and reduce their support for multiculturalism, an ideology that emphasizes recognition of distinctive cultural identities and is seen as primarily relevant to racial/ethnic minorities. Across three studies, being induced to self-identify as “White” (vs. “European American”) led majority group members high, but not low, in need for uniqueness (measured in Study 1, manipulated in Studies 2 and 3) to express less positivity toward multiculturalism. Further, the effect of uniqueness motives on reduced support for multiculturalism among participants self-identifying as “White” was mediated by reduced personal feelings of diversity (Study 3). Implications for optimal distinctiveness theory and the functions of White identity are discussed.
Review on Machine Learning Techniques for Diagnosis of Heart Disease

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning
Pharmacists’ perceptions of the Canadian opioid regulatory exemptions on patient care and opioid stewardship

Background: This study explored the perceptions of Canadian pharmacists about the barriers and facilitators of providing opioid stewardship activities in pharmacy practice, considering the subsection 56(1) class exemption under Health Canada’s Controlled Drugs and Substances Act (CDSA). Methods: Qualitative key informant telephone interviews were conducted with a convenience sample of pharmacists from across Canada. We included community or primary health care team-based pharmacists who self-identified as having experience with providing care for patients using opioids via the exemptions. All transcripts were de-identified, and thematic analysis was conducted to identify themes. Ethics approval was obtained. Results: Twenty pharmacists from community and primary health care teams, from all provinces and from urban and rural practices were interviewed. The following themes emerged: 1) optimization of opioid-related patient care, 2) jurisdictional impact and 3) awareness and education. Barriers and facilitators for opioid stewardship activities were identified. Discussion: The exemptions facilitated the pharmacists’ ability to provide opioid stewardship and positively affect patient care by providing continuity of and timely access to care. Our research demonstrated that pharmacists can responsibly and independently manage opioid prescriptions within this expanded scope, demonstrating the valuable contribution pharmacists can have in opioid stewardship. Conclusion: Pharmacists were willing and able to care for patients receiving opioid medication and thereby played a role in helping address the opioid crisis. The benefits of these exemptions were demonstrated beyond situations related to the COVID-19 pandemic and warrant consideration for consistent implementation across provincial and territorial jurisdictions, thereby ensuring equitable access to care for all Canadians.
Paying for Sustainable Coffee in a Developing Country: Consumers’ Profile in Costa Rica

This article analyzes the willingness to pay of Costa Rican consumers for three environmental certifications in the coffee market, namely, Carbon Neutral, Fairtrade, and ISO 14001. A face-to-face survey was applied to 1191 Costa Rican inhabitants. The results show that Costa Rican consumers are willing to pay price premiums around 30% for all the considered environmental certifications. In addition, a Cragg’s hurdle model shows that household income, the level of education, and environmental or community activism increase the likelihood of consumers paying price premiums for environmental certifications, while men are less likely to pay than women. It was also found that the size of the price premiums that respondents are willing to pay are positively related to income, gender (female), and education, and negatively related to age. Once the effect of socioeconomic variables has been controlled for, we conclude that consumers are more willing to pay for the CN certification than for the other two. The results can be useful for participants in the coffee value chain; coffee producers can use environmental certifications both to enhance their participation in green markets, particularly in developing countries, and to improve their environmental performance.