Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic Mathematics Education (RME) untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Siswa di Indonesia

Problem-solving ability in the real world is the main competency that students must possess in the 21st century. RME can bridge abstract mathematical concepts obtained in the classroom with the real world. This research is a meta-analytic study aimed at analyzing the effect of the RME learning model on problem-solving abilities. The data was obtained from a search of scientific articles that have been published in Science and Technology Index (SINTA) 2, 3, and 4 accredited journals and national proceedings in the period 2016-2021 and is an experimental study with a non-equivalent pre-test and post-test control group design. The research samples that matched the exclusion and inclusion criteria were seven units. The data were analyzed using the JASP V-0.11 application. The results of the heterogeneity test with a value of Q=10.277 and p=0.113>0.05. The combined effect size model used is the fixed effect model. The results showed that the average effect size was 0.42 in the medium effect category. The Funnel Plot and Egger's Test tests with a value of z=0.075 and p=0.940>0.05 indicated no publication bias. In conclusion, the RME learning model affects students' mathematical problem-solving abilities with moderate influence. The results of this study contribute to strengthening the findings of previous studies.
Demand of 21st Century Skills in the Construction Workforce

This paper analyzes the job postings in the construction sector to identify the nature and demand of the soft skills that the construction industry is looking for in the employees. The construction industry, like other industries, is witnessing rapid change in the working environment because of factors such as increasing cultural diversity of the workforce, advancement in information technology and introduction of automation and artificial intelligence to perform routine jobs. Because of these changes, individuals need soft skills to succeed in the 21 century workplace. The job postings on the Job Bank Canada website in 2019 that were related to the construction sector were analyzed to identify the soft skills that are in demand in the construction industry. Out of the top 12 skills extracted from the job postings, seven skills are identified as the soft skills. “Team player” is the most sought-after skill in the construction industry, which appears in 56.4% of the job postings. Other soft skills in demand are “effective interpersonal skills,” “excellent oral communication,” “attention to detail,” “reliability,” “flexibility,” and working in a “fast-paced environment. ” The analysis shows that the interpersonal and intrapersonal soft skills are in high demand in the construction industry.
How group composition affects gifted students: Theory and evidence from school effectiveness studies

ABSTRACT In this paper, I analyze the theoretical and empirical foundations for how class composition affects gifted students’ academic outcomes. First, I define the term composition effect and distinguish it from other context effects. Based on this definition, I present the mechanisms behind composition effects while drawing distinctions between resource-related, peer-related and teacher-related composition effects. Subsequently, I discuss theories and empirical results of international school effectiveness studies in terms of their relevance to increasing equity in gifted education. Ability groups, especially gifted classes, have an advantage for the promotion of gifted students. However, due to selection mechanisms, low-income students drop out of these support programs very early in their school careers. Behind the supposed negative influence of ethnic composition on school performance, a socioeconomic effect can be assumed. This is thus the most important composition effect in regard to inequality in talent development. However, there are ongoing efforts to ensure greater equity specifically in gifted education.
An Intermetallic NiTi-Based Shape Memory Coil Spring for Actuator Technologies

Amongst various intermetallic shape memory alloys (SMAs), nickel–titanium-based SMAs (NiTi) are known for their unique elastocaloric property. This widely used shape remembering material demonstrates excellent mechanical and electrical properties with superior corrosion resistance and super-long fatigue life. The straight-drawn wire form of NiTi has a maximum restorable strain limit of ~4%. However, a maximum linear strain of ~20% can be attained in its coil spring structure. Various material/mechanical engineers have widely exploited this superior mechanic characteristic and stress-triggered heating/cooling efficiency of NiTi to design smart engineering structures, especially in actuator technologies. This short technical note reflects the characteristics of the NiTi coil spring structure with its phase transformations and thermal transformation properties. The micro-actuators based on NiTi have been found to be possible, suggesting uses from biomedical to advanced high-tech applications. In recent years, the technical advancements in modular robotic systems involving NiTi-based SMAs have gained speculative commercial interest.
Phylogenetic Study and Divergence of Weaver Ant, Oecophylla smaragdina Fabricius in Bangladesh (Hymenoptera: Formicidae)

The weaver ant species, Oecophylla smaragdina, is widely distributed from India through Southeast Asia to northern Australia including many tropical Western Pacific islands. Recent phylogenetic study of O. smaragdina showed that the species is divided into 7 groups based on mtDNA and the Bangladesh populations belong to SE Asian mainland clade despite its geographical proximity to India. The present study aims to reveal the phylogenetic aspect of O. smaragdina in Bangladesh with its divergence time estimation based on extensive materials. Sampling was executed according to zonation of 5 areas which are demarcated by 3 main rivers and a total of 95 colonies from 87 localities of 47 Districts were collected during 2013 to 2018 from Bangladesh. The molecular analyses, using 2 mitochondrial loci: Cytochrome b oxidase subunit 2 (Cytb) consisting of 507 bp and Cytochrome c oxidase subunit I (COI) consisting of 639 bp done in Ku, Japan. The analysis of 84 colonies revealed that 47 are the Indian clade (Indian type) and 37 are the SE Asian mainland clade (SE Asian type). The distribution of mtDNA types showed the occurrence of both the two types with some overlapping distribution in the central part of Bangladesh. The divergence time analysis in Bangladesh populations, resulted that Indian type diverged ca. 2.2 Ma and SE Asian type diverged ca. 0.20 Ma corresponding to early to late Pleistocene. This study suggested a comprehensive phylogenetic scenario of O. smaragdina in Bangladesh.
Determinants of household energy choice in West Shoa Zone: in the case of Ambo Town

ABSTRACT The objective of the study was to identify the determinants of household energy choice in Ambo town. Both primary and secondary data were gathered from respondents through questionnaires and interviews. The secondary data was collected from the Ambo administration, the Ambo health office, and the Ambo town energy and mineral office. Descriptive statistics and Multinomimial logistics regression were used to analyze the data. Multiple stage sampling was used to draw the sample. Firstly, three Kebeles were purposively selected from six Kebeles as center and distance from the center of town, and then systematic sampling was employed to collect data from 201 sample size. The result of descriptive statistics showed that gender, educational level, income, occupation, and livestock affect the energy choice whereas the F-test result showed that gender and educational level, income, occupation, and livestock were significant variables. In econometrics analysis, the results of multinomial logistics regression the analysis showed that gender, education, and income were statistically significant at a 5% level of significance. Occupation and marital status were significant at a 5% and 10% level of significance with different signs of coefficient, respectively. Based on the findings, the study suggests that influencing households’ energy choice and improvement of modern energy accessibility will play a critical role in the transition of households from traditional to modern energy sources to protect the health and environment of the residents of the town.
Drought‐Induced Salinity Enhancement Weakens Mangrove Greenhouse Gas Cycling

The importance of tidal mangroves in mitigating greenhouse gas (GHG) via sequestering atmospheric carbon dioxide (CO2) has been increasingly recognized, but this climate benefit comes at a biogeochemical cost of methane (CH4) emissions. Previous studies have assessed the net radiative effect of mangrove GHG fluxes, however, large uncertainty still exists due to the very limited availability of long‐term continuous measurements. In this study, we analyzed the temporal variations of GHG (CO2 and CH4) fluxes and their environmental controls based on eddy covariance measurements in a subtropical estuarine mangrove in the Southeast China during 2019 and 2020, when a severe drought occurred. The results showed (a) annually this mangrove acted as a CO2 sink of −1,075.8 g C m−2 and a CH4 source of 3.1 g C m−2, and the CH4‐induced warming effect can offset 4.6% (9.8%) of the CO2‐induced cooling effect at a 100‐year (20‐year) time horizon using the metric of sustained‐flux global warming potentials; (b) net CO2 and CH4 fluxes showed different diurnal and seasonal variation patterns, with stronger CO2 sink and CH4 source in colder and warmer seasons, respectively; (c) drought‐induced salinity enhancement due to reduced rainfall and river discharge weakened GHG cycling, lowering both CO2 sink and CH4 source in the drier year. This study confirms that ecosystem‐level CH4 emissions from estuarine mangroves are not negligible and could substantially offset the CO2‐induced cooling effect. Future increases in temperature and salinity with expected global warming and sea level rise will likely weaken the climate benefits of mangroves.
Can unconditional cash transfers encourage voters to cast ballots? Evidence from elections in Brazil

Low-income voters are typically underrepresented in elections. Even in countries adopting compulsory voting, there is a consolidated pattern of voter turnout decline among the poor. I argue that implementing an unconditional cash transfer (UCT) can decrease both direct and indirect costs of voting and thus enhance the electoral participation of low-income voters. I test these claims by leveraging Marica’s unconditional cash transfer (MUCT), the largest UCT in Latin America implemented in Brazil in 2013. Causal estimates from a difference-in-differences design show that voter abstention decreased by 15% after the cash transfer intervention in Marica. Furthermore, the share of undecided voters - typically those who decide to discard their votes instead of voting for one of the candidates displayed on the ballot - decreased by 33% after the MUCT implementation. These findings suggest that paying periodic unconditional benefits can mobilize low-income voters and encourage them to participate in elections.
Reliable Connectionist Tools to Determine Biodiesel Cetane Number Based on Fatty Acids Methyl Esters Content

The self-ignition ability of fuels is characterized using a property called cetane number (CN). The main objective of this research work is to develop accurate models using smart algorithms to predict the CNs associated with 149 samples of biofuels collected from the literature. In this study, we use two machine learning (ML) algorithms, namely least squares support vector machine (LSSVM) and Extra Trees (ET) and an experimental dataset containing a variety of fatty acids methyl esters (FAME). To assess the predictive performance of the developed models, various statistical parameters including average relative deviation in percentage (%ARD), average absolute relative deviation in percentage (%AARD), and coefficient of determination (R2) are calculated. The statistical and graphical analyses presented in this study reveal that the ET model outperforms LSSVM model in predicting the CN associated with the biodiesel systems from the collected dataset so that the value of %AARD is less than 3% for the ET method. It is also found that Linoleic (18:02) is the most important factor affecting the CN magnitude of biodiesel. The CN value decreases upon an increase in concentration of Palmitoleic (16:01), Oleic (18:01), Linoleic (18:02), and Linolenic (18:03); this is in agreement with the experimental studies.
Media coverage of the Finnish basic income experiment

In 2017–2018 Finland conducted a basic income experiment. The randomly selected experiment group consisted of 2000 unemployed persons between 25 and 58 years. The group received a monthly payment of €560, unconditionally and without means testing. The main purpose of the experiment was to study the effects of the basic income on employment and well-being. The Finnish basic income experiment was exceptional in many ways and generated interest worldwide. Coverage of the Finnish experiment, in both the Finnish and international media, has been remarkable before, during and after the experiment. Social experiments cannot be implemented in a laboratory, but rather take place within communities, and their participants are individuals living their everyday lives. Social experiments occur in public; therefore, publicity is one characteristic of such experiments, and it is important to shed light on it. One way to study publicity is to study the media coverage of the issue. The news media, among other forms of mediated communication, has a significant role in the construction of reality (Couldry and Hepp, 2018). One way the news media can construct reality is by framing how an issue is approached. Frames can be described as ‘organizing principles that are socially shared and persistent over time that work symbolically to meaningfully structure the social world’ (Reese, 2001: 11). In this chapter, I explore coverage of the Finnish basic income experiment in both the Finnish and international media. Specifically, I address the research question of how the Finnish and international media have framed the Finnish basic income experiment. To study media coverage, its characteristics, and prevailing perspectives, I used media framing analysis, concentrating on identification of media frames and frame-building. In this chapter, I first present media framing theory, and the theoretical and conceptual approaches to media frames, and the media framing process. Thereafter, sections on data and methods used in this study are presented. In
Spectral Classification Based on Deep Learning Algorithms

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we proposed a new CNN architecture specially designed for 2D spectral data. Firstly, we collected the reflectance spectra of five samples using a portable optical fiber spectrometer and converted them into 2D matrix data to adapt to the deep learning algorithms’ feature extraction. Secondly, the number of convolutional layers and pooling layers were adjusted according to the characteristics of the spectral data to enhance the feature extraction ability. Finally, the discard rate selection principle of the dropout layer was determined by visual analysis to improve the classification accuracy. Experimental results demonstrate our CNN system, which has advantages over the traditional AlexNet, Unet, and support vector machine (SVM)-based approaches in many aspects, such as easy implementation, short time, higher accuracy, and strong robustness.
Investor sentiment and mutual fund stock picking

ABSTRACT The active share of mutual funds drops significantly when investor sentiment is high, indicating that fund managers reduce their active stock selection and stay closer to their benchmarks during such periods. Our evidence is consistent with fund managers being sentiment-prone – challenging the conventional view that it is only the preponderance of retail investors during high sentiment periods that allows sentiment to influence asset prices.
A Comprehensive Survey of Cognitive Graphs: Techniques, Applications, Challenges

The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.
Inferring the Hidden Cascade Infection over Erdös-Rényi (ER) Random Graph

Finding hidden infected nodes is extremely important when information or diseases spread rapidly in a network because hints regarding the global properties of the diffusion dynamics can be provided, and effective control strategies for mitigating such spread can be derived. In this study, to understand the impact of the structure of the underlying network, a cascade infection-recovery problem is considered over an Erdös-Rényi (ER) random graph when a subset of infected nodes is partially observed. The goal is to reconstruct the underlying cascade that is likely to generate these observations. To address this, two algorithms are proposed: (i) a Neighbor-based recovery algorithm (NBRA(α)), where 0≤α≤1 is a control parameter, and (ii) a BFS tree-source-based recovery algorithm (BSRA). The first one simply counts the number of infected neighbors for candidate hidden cascade nodes and computes the possibility of infection from the neighbors by controlling the parameter α. The latter estimates the cascade sources first and computes the infection probability from the sources. A BFS tree approximation is used for the underlying ER random graph with respect to the sources for computing the infection probability because of the computational complexity in general loopy graphs. We then conducted various simulations to obtain the recovery performance of the two proposed algorithms. As a result, although the NBRA(α) uses only local information of the neighboring infection status, it recovers the hidden cascade infection well and is not significantly affected by the average degree of the ER random graph, whereas the BSRA works well on a local tree-like structure.
Optical Losses of Frontal Layers in Superstrate CdS/CdTe Solar Cells Using OPAL2

In this paper, optical losses in CdS/CdTe solar cells are calculated on the basis of the designated reflective index of various frontal layers using an OPAL2 calculator for the first time. Two types of glass (0.1 mm ultra-thin Schott and 1.1 mm standard borosilicate glass) were assumed to be coated by different Transparent-Conducting-Oxides (TCOs) such as SnO2:F, ZnO:Al, and ITO forming frontal layers for CdS/CdTe solar cells in superstrate configuration. Absorption, reflectance, transmittance, and consequently optical bandgap energies are calculated as a function of common thicknesses, used in the literature. The results show that an increase in TCO thickness led to a decrease in optical band gap as well as an enhancement in contact potential difference, which can deteriorate device performance. The optimum thickness of 100 nm for SnO2:F was calculated, while 200 nm for ZnO:Al and ITO show reasonable optical losses caused by reflections at the interfaces’ and the layer’s absorption. It is seen that 80 to 150 nm CdS on ITO might be an effective range to satisfy a high short circuit current and low defect densities at the CdS/CdTe interface. Finally, a minimum 2 μm thickness for the CdTe on the ultra-thin Schott glass coated by optimum layers can result in the highest short circuit current of 28.69 mA/cm2. This work offers a practical equivalent strategy to be applied for any superstrate solar cells containing TCO and CdS frontal layers.
TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

A novel two‐branched deep convolutional (TWEEC) network is developed for computer‐aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8‐15%. This work paves the way for the development of efficient deep learning based computer‐aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.
The cultural politics of naming outdoor rock climbing routes

ABSTRACT In outdoor rock climbing, the first person who successfully ascends and sets up a route – the first ascensionist – chooses a name for it. Some first ascensionists issue discriminatory route names. This article explores how one group of climbing women negotiates misogynistic route names. I qualitatively analyse seventeen, individual, semi-structured interviews and four focus group interviews, and six themes emerge: frustration, helplessness, exclusion, internalized sexism, pushback, and intersection of sexism and settler colonialism. Adopting an intersectional feminist approach, I argue that the politics of naming routes cannot be divorced from a settler-colonial logic that has long used (re)naming land as a strategy for nation-building. I suggest incorporating decolonial theories into outdoor rock climbing to create more inclusive leisure environments.