Students' Perceptions in Using the Kahoot! Game on Reading Comprehension Learning

Reading comprehension is an activity to build people's understanding through texts by combining information obtained through texts with their knowledge and experience. Students face problems in reading comprehension, such as difficulty answering questions related to descriptive text, not being able to read the entire text, and confusing new vocabulary. This study aimed to determine how students perceive and improve reading comprehension after using Kahoot! Games as learning media for reading comprehension on description text at State Junior High School in Cilacap. This study uses an experimental sequential mix method with a qualitative descriptive method as the first order followed by a pre-experimental method using a one-group pretest-posttest design. Based on the results of the study, it was found that students' perceptions of using Kahoot! Games tend to be positive, students find learning fun, increase focus and caution, eliminate boredom, and motivate students to read the entire text. Furthermore, there was an increase in students' reading ability as indicated by the paired-sample t-test results where the sig value. the count obtained was 0.002 0.05, and the count obtained was 6.837 table 2.776. Supported by an 18% increase in the average student training results from 66.4 to 81.

Broadband Market Sizing in Brazil

The objective of this study is to predict the potential broadband market in Brazil. This is done by combining information from two national databases: the 2010 census and the 2015 PNAD (National Household Survey). The 2015 PNAD is used to estimate the probability of the household accessing internet by broadband technology, using a logit regression. The broadband market is predicted using the estimated model with the same covariates found in the 2010 census. The prediction indicated a potential market of 45 million households, an additional of 6 million households in relation to the current situation. The new size of the broadband market is estimated if there is a 10% increase in the average penetration of broadband services in the main metropolitan areas. In this scenario, the new market covers 50.7 million households.

The Autonomy-validity Dilemma in Mechanical Prediction Procedures: The Quest for a Compromise

A robust finding in psychological research is that combining information with a mechanical rule results in more valid predictions than combining information holistically in the mind. Nevertheless, information is typically combined holistically in practice, resulting in suboptimal predictions and decisions. Earlier research showed that decision makers are more likely to use mechanical prediction procedures when they retain autonomy in the decision-making process. However, it remains largely unknown how different autonomy-enhancing features affect predictive validity. Therefore, in two pre-registered studies (total N = 342), we investigated if and how prediction procedures can be designed such that they satisfy decision-makers’ autonomy needs and acceptance without reducing predictive validity. Based on archival application data from a university admission procedure, participants predicted applicants’ first-year GPA and chance of dropout. The results of Bayesian analyses showed that participants preferred prediction procedures in which they retained autonomy by choosing consistent predictor weights of a mechanical rule or by holistically adjusting the predictions of an optimal regression model. In general, these prediction procedures resulted in slightly higher predictive validity compared to fully holistic prediction. Providing participants with predictor validity information slightly increased predictive validity when participants could choose predictor weights, but not when making holistic predictions or adjusting optimal model predictions. Giving decision makers a role in designing mechanical rules through choosing weights based on explicit predictive validity information could help promote the implementation and validity of mechanical prediction in practice.

Compound faults feature extraction of inter-shaft bearing based on vibration signal of whole aero-engine

To effectively extract the information of compound faults of inter-shaft bearing of an aero-engine based on casing vibration signals, the paper has introduced the concept of weighted Katz fractal dimension and proposed the method combining information fusion, wavelet transform (WT), singular value decomposition (SVD), and Katz fractal dimension, the cross-correlation function (CCF-WT-SVD-Katz algorithm). The method includes homologous information fusion achieved by the CCF of horizontal and vertical vibration signals of the rotor from the same section; signal separation and denoising of blended signals through WT and SVD; reinforcement of fault characteristics of signals according to weighted Katz fractal dimension; and extraction of characteristic frequencies of compound faults of inter-shaft bearing by frequency spectrum of weighted and reconstructed signals. The result indicates that the proposed CCF-WT-SVD-Katz algorithm is capable of effectively extracting compound fault characteristics of inter-shaft bearing and precisely identifying a fault type based on whole casing vibration signals and will be of very good application value in engineering.

When Refugee Exposure Increases Incumbent Support through Development: Evidence from Uganda

How does exposure to refugees affect elections, development, and citizen support for migration within the Global South? In the context of wealthy consolidated democracies, recent studies have found that when voters are more exposed to refugees, they punish incumbents and turn to far-right parties. Yet there is a dearth of studies on the electoral consequences of refugee-hosting in developing countries, where the majority of refugees reside and politics often do not fall on a left-right divide. We explore this question in Uganda, one of the largest refugee-hosting countries. Combining information on the populations and locations of refugee settlements with four waves of national elections data at the parish level, we find that greater exposure to refugees increases incumbent support. Unique longitudinal data on access to healthcare, schools, and roads coupled with national survey data suggest that this effect is due to positive externalities of refugee-hosting on local public goods provision.

Analysis of furans and pyridines from new generation heated tobacco product in Japan

Background In recent years, heated tobacco products (HTPs), which are widely used in Japan, have been sold by various brands using additives such as flavors. It has been reported that the components of mainstream smoke are different from those of conventional cigarettes. In this study, we established an analytical method for furans and pyridines in the mainstream smoke, which are characteristic of HTPs and particularly harmful among the generated components, and investigated the amount of component to which the smokers are exposed. Methods We established a simple analytical method for simultaneous analysis of gaseous and particulate compounds in the mainstream smoke of HTPs (IQOS, glo, ploom S) in Japan by combining a sorbent cartridge and glass fiber filter (Cambridge filter pad (CFP)). Both the sorbent cartridge and CFP were extracted using 2-propanol and analyzed via GC-MS/MS to determine the concentration of furans and pyridines generated from each HTP. Results The results showed that the levels of target furans such as furfural, 2-furanmethanol, 2(5H)-furanone, and 5-methylfurfural tended to be higher in the mainstream smoke of glo than in standard cigarettes (3R4F). Pyridine, which is generated at a high level in 3R4F as a combustion component, and 4-ethenylpyridine (EP), which is a known marker of environmental tobacco smoke, were detected. Among these components, 2-furanmethanol and pyridine are classified as Group 2B (possibly carcinogenic to humans) by the International Agency for Research on Cancer (IARC). Therefore, it is possible that they will contribute to the health effects caused by use of HTPs. Conclusions Using the new collection and analytical method for furans and pyridines in the mainstream smoke of HTPs, the level of each compound to which smokers are exposed could be clarified. By comprehensively combining information on the amount of ingredients and toxicity, it will be possible to perform a more detailed calculation of the health risks of using HTPs. In addition, the components detected in this study may be the causative substances of indoor pollution through exhaled smoke and sidestream smoke; therefore, environmental research on the chemicals generated from HTPs would be warranted in future studies.

To keep or divest a division: Canon’s strategy in an oligopolistic market

The issue in this teaching case is the future of the Lithography Division of Canon. The case is situated in 2015 when this division represents around 7.5% of the company’s assets, 13.8% of its sales, and 10% of its capital expenditures. The division is loss-making. This issue is faced by two persons in the organization: the CEO and the CFO of Canon. The CEO would like to keep the division, while the CFO prefers to divest it. The case is particularly interesting because of the market structure: the division operates in the lithography market, which is a clear example of an oligopoly. The case can be used in Strategy, Technology, or Digital Business courses in MSc or MBA programs. The learning objectives of the case are (1) combining information provided via academic journals with facts from the case and the collective knowledge and creativity of a team of students, (2) analyzing the collected information and building a case with strong arguments, (3) creating a convincing strategic plan including a persuasive presentation, and (4) practicing the skills of presenting. The information provided in this case is based on the annual reports of Advanced Semiconductor Materials Lithography (ASML), Canon, and Nikon, and on other publicly available information on the lithography industry. The case itself is fictional. The authors are not aware of any discussions within Canon about the future of the company’s lithography division.

Computing how-provenance for SPARQL queries via query rewriting

Over the past few years, we have witnessed the emergence of large knowledge graphs built by extracting and combining information from multiple sources. This has propelled many advances in query processing over knowledge graphs, however the aspect of providing provenance explanations for query results has so far been mostly neglected. We therefore propose a novel method, SPARQLprov, based on query rewriting, to compute how-provenance polynomials for SPARQL queries over knowledge graphs. Contrary to existing works, SPARQLprov is system-agnostic and can be applied to standard and already deployed SPARQL engines without the need of customized extensions. We rely on spm-semirings to compute polynomial annotations that respect the property of commutation with homomorphisms on monotonic and non-monotonic SPARQL queries without aggregate functions. Our evaluation on real and synthetic data shows that SPARQLprov over standard engines incurs an acceptable runtime overhead w.r.t. the original query, competing with state-of-the-art solutions for how-provenance computation.

Cancer Detection based on Microarray Data Classification Using FLNN and Hybrid Feature Selection

Cancer is one of the second deadliest diseases in the world after heart disease. Citing from the WHO's report on cancer, in 2018 there were around 18.1 million cases of cancer in the world with a total of 9.6 million deaths. Now that bioinformatics technology is growing and based on WHO’s report on cancer, an early detection is needed where bioinformatics technology can be used to diagnose cancer and to help to reduce the number of deaths from cancer by immediately treating the person. Microarray DNA data as one of the bioinformatics technology is becoming popular for use in the analysis and diagnosis of cancer in the medical world. Microarray DNA data has a very large number of genes, so a dimensional reduction method is needed to reduce the use of features for the classification process by selecting the most influential features. After the most influential features are selected, these features are going to be used for the classification and predict whether a person has cancer or not. In this research, hybridization is carried out by combining Information Gain as a filtering method and Genetic Algorithm as a wrapping method to reduce dimensions, and lastly FLNN as a classification method. The test results get colon cancer data to get the highest accuracy value of 90.26%, breast cancer by 85.63%, lung cancer and ovarian cancer by 100%, and prostate cancer by 94.10%.