Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Layered Flipped Learning Model on Students' Attitudes and Self-Regulation Skills

The aim of this study is to determine the views of students regarding their attitudes towards English lesson and the contributions to self-regulation skills while using a layered flipped learning model. One of the qualitative research methods, the ‘action research’ method was used in this research. The research was conducted with students in the fifth year of a secondary school in the Western Black Sea region of Turkey. In the research process, self-regulation interview form, attitude interview form, focus group interview, student letters, self-assessment form were used as data collection tools. According to the results of the research, a positive change was observed in the motivational, self-efficacy, strategy selection, environmental arrangement, time management components of self-regulation. Also, it was concluded that there were positive changes at the end of the semester in all three components of the attitude. In addition, at the end of the semester, it was observed that students started doing extra activities to learn English better, they started to deal with English outside of the classroom and to create new opportunities to be exposed to English.
Cyclic Microirrigation in Container-grown Landscape Plants Improves Plant Growth and Water Conservation

Abstract Four tree (Acer rubrum L., Ulmus alata Michx., Quercus virginiana Mill, and Lagerstroemia indica L.) and two landscape shrub species (Rhododendron indicum L. ‘Formosa’ and Elaeagnus pungens Thunb.) were grown in 10-liter (#3) polyethylene containers. Plants were irrigated with overhead impact sprinklers (control) or with individual low volume spray stakes. Microirrigated treatments consisted of same or double volume per day per container as controls applied as one to three cyclic subvolumes. Shrub growth was seldom influenced by irrigation treatment. Xeric tree species (U. alata and Q. virginiana) grew as well with single volumes applied in 2 cycles as double volumes applied in 3 cycles; both produced significantly larger trees than the control. Mesic species (A. rubrum and L. indica) irrigated with double volumes in 3 cycles produced the largest trees that were significantly larger than single volume microirrigated or control trees. Growth of single cycle, single volume trees (overhead and micro.
Revisiting the ‘university image model’ for higher education institutions’ sustainability

ABSTRACT Global student mobility has reached far beyond Europe, North America, and Australia. Malaysia, Singapore, and South Korea have become the ‘emerging contenders’ focusing on attracting international students, primarily from other Asian countries. This heightened competition among higher education institutions (HEIs) challenges their sustainability. This study, therefore, aims to investigate Alcaide-Pulido, Alves, and Gutiérrez-Villar's university image model and its influence on students’ citizenship behaviour in the context of HEIs’ achieving sustainability. Data were collected from 223 international students at two reputable Malaysian universities. The results, showing that ‘image’ is a ‘higher-order construct’, extend the university image model and reveal a reformation of the ‘image’ variables. The findings further reveal that the extended and reformed model is capable of building satisfaction among students, which, in turn, contributes to enhancing students’ citizenship behavioural outcomes (advocacy, feedback, helping behaviour, and tolerance). This can indirectly support HEIs’ sustainability.
Review on remote sensing methods for landslide detection using machine and deep learning

Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement. In the past decades, due to inflation of urbanized area and climate change, a compelling expansion in landslide prevalence took place which is also termed as mass/slope movement and mass wasting, causing extensive collapse around the world. The principal reason for its pursuance is a reduction in the internal resistance of soil and rocks, classified as a slide, topple, fall, and flow. Slopes can be differentiated based on earth material and the nature of its movements. The downward flow of landslides occurs due to excessive rainfall, snowmelt, earthquake, volcanic eruption, and so on. This review article revisits the conventional approaches for identification of landslides, predicting future risk, associated with slope failures, followed by emphasizing the advantages of modern geospatial techniques such as aerial photogrammetry, satellite remote sensing images (ie, panchromatic, multispectral, radar images), Terrestrial laser scanning, and High‐Resolution Digital Elevation Model (HR‐DEM) in updating landslide inventory maps. Machine learning techniques like Support Vector Machine, Artificial neural network, deep learning has been extensively used with geographical data producing effective results for assessment of natural hazard/resources and environmental research. Based on recent studies, deep learning is a reliable tool addressing remote sensing challenges such as trade‐off in imaging system producing poor quality investigation, in addition, to expedite consequent task such as image recognition, object detection, classification, and so on. Conventional methods, like pixel and object‐based machine learning methods, have been broadly explored. Advanced development in deep learning technique like CNN (Convolutional neural network) has been extensively successful in information extraction from an image and has exceeded other traditional approaches. Over the past few years, minor attempts have been made for landslide susceptibility mapping using CNN. In addition, small sample sizes for training purpose will be major drawback and notably remarkable while using deep learning techniques. Also, assessment of the model's performance with diverse training and testing proportion other than commonly utilized ratio, that is, 70/30 needs to be explored further. The review article briefly highlights the remote sensing methods for landslide detection using machine learning and deep learning.
Vortex induced vibrations of wind turbine blades: Influence of the tip geometry

The present investigation used numerical simulations to study the vortex induced vibrations (VIVs) of a 96 m long wind turbine blade. The results of this baseline shape were compared with four additional geometry variants featuring different tip extensions. The geometry of the tip extensions was generated through the variation of two design parameters: the dihedral angle bending the blade out of the rotor plane and the sweep angle bending the blade in the rotor plane. The applied numerical methods relied on a fluid structure interaction (FSI) approach, coupling a computational fluid dynamics solver with a multi-body structural solver. The methodology followed for locating VIV regions was based on the variation of the inclination angle. This variable was defined as the angle between the freestream velocity and the blade axis, being 0° when these vectors were normal and positive when a velocity component from tip to root was introduced. For the baseline geometry, the FSI simulations predicted significant blade vibrations for inclination angles between 47.5° and 60° with a maximum peak-to-peak amplitude of 2.3 m. The installation of the different tip extensions on the blade geometry was found to significantly modify the inclination angles where VIV was observed. In particular, the simulations of three of the tip designs showed a shifting of several degrees for the point where the maximum vibrations were recorded. For the specific tip geometry where only the sweep angle was taken into account, a total mitigation of the VIV was observed.
Machine Learning Algorithms and Ensemble Technique to Improve Prediction of Students Performance

Measuring Student’s performance is necessary for the mordent society. Applications of Machine learning algorithms increased the growth in various fields like disease prediction, student’s performance prediction, and crop productions prediction and in various other fields. The main aim of this study is to improve the prediction of students’ performance using various machine learning algorithms and ensemble technique to get better accuracy over individual machine learning algorithms. We have used the student’s dataset, which consists of 1000 instances and 22 attributes for evaluating the performance of students. In this paper we have applied four machine learning algorithms Decision Tree (DT), Naïve Bayesian (NB), K-Nearest Neighbors (KNN) and Extra Tree (ET) and then we have developed a model to combine the results of each individual base learner using Bagging and Boosting ensemble methods. The results obtained using bagging and boosting ensemble techniques were compared to select the best model. The results of all machine learning algorithms and ensemble techniques are tested with various factors like accuracy, sensitivity, specificity and f1-score. After comparison of results we find that bagging is the best method which gives the better result as compared to bagging ensemble techniques. The developed model can be applied on the admission seeking students to identify the perdition of their performance in the selected course, which can be beneficial for both the students and Institution.
Economic effects of migrant labor on industrialized building system (IBS) adoption in the Malaysian construction industry

ABSTRACT The Malaysian government has been encouraging industrialized building system (IBS) adoption as a modern method of construction to improve the precision of material and workmanship. However, the migration of foreign labor to Malaysia from its neighboring countries that offer comparatively lower income has affected IBS adoption in construction projects. In this study, economic perspectives were explored in conjunction with migrant labor, as this is vital to evaluate their influences on IBS adoption. Interview surveys were conducted to gather inputs on the real scenario of IBS implementation in the Malaysian construction industry, with the utilization of a qualitative narrative analysis technique. QSR NVivo Version 12.0 software was used to aid the organization and analysis of the data from the taped and transcribed interviews. Qualitative results show that the most significant economic factor related to migrant labor that influenced IBS adoption is labor cost, followed by project price, labor availability and project competition. These factors can be considered in the development of manpower guidelines which then can be used to assist the construction stakeholders in the decision-making of IBS adoption in construction projects.
A case of large-cell neuroendocrine carcinoma harboring rare ALK fusion with initial response to the ALK inhibitor crizotinib and acquired F1174L mutation after resistance

Abstract A 51-year-old, male, non-smoker with a 3.4 cm mass in the right middle lobe was diagnosed with large cell neuroendocrine carcinoma (LCNEC). Fluorescence in situ hybridization revealed anaplastic lymphoma kinase (ALK) gene translocation, in agreement with the immunohistochemistry result obtained with use of ALK-Ventana. Radiographic examinations showed both bone and brain metastasis. After two cycles of chemotherapy consisting of etoposide and cisplatin, the patient achieved stable disease, and was subsequently switched to crizotinib. Both computed tomography and magnetic resonance imaging revealed partial response after 4 months of crizotinib, but progressed after treatment for 10 months, when several hard lymph nodes were palpable in the left supraclavicular fossa. Lymph node biopsy showed similar histology of tumor cells and targeted next-generation sequencing revealed ALK F1174L on exon 23 with two rare forms of ALK rearrangements. This case provides evidence of responsiveness of ALK inhibitors for a rare pattern of ALK-rearranged LCNEC, and suggests that F1174L, a common resistant mutation found in non-small-cell lung cancer, also causes crizotinib resistance in LCNEC.
Visual exposure optimizes stimulus encoding in primary visual cortex

Sensory exposure alters the response properties of individual neurons in primary sensory cortices. However, it remains unclear how these changes affect stimulus encoding by populations of sensory cells. Here, recording from populations of neurons in cat primary visual cortex, we demonstrate that visual exposure enhances stimulus encoding and discrimination. We find that repeated presentation of brief, high-contrast shapes results in a stereotyped, biphasic population response consisting of a short-latency transient, followed by a late and extended period of reverberatory activity. Visual exposure selectively improves the stimulus specificity of the reverberatory activity, by increasing the magnitude and decreasing the trial-to-trial variability of the neuronal response. Critically, this improved stimulus encoding is distributed across the population and depends on precise temporal coordination. Our findings provide evidence for the existence of an exposure-driven optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.
How Do Visitors from Different Cultural Backgrounds Perceive the Messages Conveyed to Them by Their Local Zoo?

As educational institutions, zoos provide an informal, free-choice learning environment. To understand the complex processes of learning in the zoo we must therefore take into account the visiting family’s culture. The study presented here, conducted in the Tisch Family Zoological Gardens in Jerusalem, investigates how visitors from different cultural backgrounds experience the zoo and interpret its intended messages. We found that, ultimately, the zoo is perceived similarly by the Arab and Jewish visitors as an educational institution, although what they come there to learn is different. Moreover, with regards to the message of conservation, neither population sees it as a major, prominent message. Despite the overall similarity in our participants’ response to the zoo as an enjoyable, cultural educational institute, there were some differences in the experiences of Jewish vs. the Arab visitors, reflected primarily in their animal preferences, and also in the types of messages that they suggest the zoo is conveying to them.
The botanical multiverse of Peter Barlow

ABSTRACT Dr Peter Barlow, who died in 2017, was one of the most respected botanists and biologists of the latter half of the 20th Century. His interests covered a wide range of plant biological topics, e.g. root growth and development, plant cytoskeleton, effects of gravity, plant intelligence, pattern formation, and evolution of eukaryotic cells. Here we consider Peter’s numerous contributions to the: elucidation of plant patterns; understanding of root biology; role of the plant cytoskeleton in growth and development; influence of the Moon on terrestrial vegetation; Cell Body concept; and plant neurobiology. In so doing we attempt not only to provide an overview of Peter’s important work in many areas of plant biology, but also to place that work in the context of recent advances in plant and biological sciences.
Correlation study of relative parameters of intracranial pressure and prognosis of patient with craniocerebral injury

Objective: To analyze the correlation of the relative parameters of intracranial pressure to the prognosis in patients with craniocerebral injury.Methods: The clinical data of 80 patients with closed craniocerebral injury were retrospectively analyzed, and all of these patients underwent conventional examinations of arterial blood pressure and intracranial pressure. Neumatic DCR system was used to monitor relative parameters of intracranial pressure from patients. According to the score of Glasgow outcome scale (GOS) upon discharge, they were divided into favorable prognosis group (GOS III-V, n = 46) and unfavorable prognosis group (GOS I-II, n = 34). The relative parameters of intracranial pressure of two groups were compared so as to analyze the correlation of the prognosis in patients to ICP-related parameters.Results: Pressure reactivity index (PRx) and intracranial pressure (ICP) of favorable prognosis group were significantly higher than those of unfavorable prognosis group (t = 12.27, t = 5.22, p < .05). Meanwhile, cerebral perfusion pressure (CPP) and ICP-ABP wave amplitude correlation (IAAC) of favorable prognosis group were significantly lower than those of unfavorable prognosis group (t = 14.54, t = 14.78, p < .05). The average age, gender, duration of admission to neurosurgical intensive care unit (NICU) and GCS (Glasgow coma scale) score on admission of the two groups were not statistically significant.Conclusions: The prognosis and ICP-related parameters (such as PRx, ICP, CPP, etc.) in patients with craniocerebral injury are risk factors for the prognosis effect. Therefore, to monitor the above-mentioned indicators has an important clinical value for assessing the prognosis of craniocerebral injury.
Saving Behavior Within and Across Developing Nations: Implications for Public Policy Makers

Although the idea that people should save some portion of their incomes is undisputed, much extant consumer and public policy research on this topic concentrates on citizens in the developed world. Whether saving behavior is similar or different in developing countries has yet to be adequately explored. To address this research gap, the authors investigate how the proclivity to save money may influence consumption adequacy and life satisfaction across these nations and, even more fundamentally, how the ability to earn income may ultimately influence the ability to save. They compare these relationships across BRICS countries with the United States as a surrogate for developed nations. Using the University of Michigan’s World Values Survey, the authors investigate Wave 6 to determine what may underlie differences across countries. The results demonstrate that motivations and their relationships with saving behavior and consumption adequacy vary significantly. Each then plays a role in how satisfied individuals are with their lives. Findings highlight how to develop public policies to enhance saving behavior, along with research directions consonant with the study objectives.
An Empirical Assessment of Organizational Commitment and Job Performance: Vietnam Small and Medium-Sized Enterprises

Research on employee commitment to the organization is necessary for human resource management, and the result is applied in practice to improve organizational eﬀectiveness. The aim of the present study is to explore factors aﬀecting organizational commitment at the small and medium-sized enterprises (SMEs) in Vietnam. Besides, the relationship between organizational commitment and job performance is examined as well. The research was conducted on a sample consisting of 67 white-collar workers and 260 blue-collar workers at SMEs. A total of 327 valid complete questionnaires were input into SPSS 20 database for processing to provide evidence. The research model and hypotheses were tested using the technique of the hierarchical multiple regression analysis. The research results revealed that income, reward and welfare, direct manager, working environment, coworker, and promotion opportunity tended to associate positively with organizational commitment. Besides, the fnding also showed that, when the employee has a high organizational commitment, it would lead to high job performance. The main fndings of this study provided some managerial implications for SMEs, in general, and managers, in particular. It implies that Vietnam’s small and medium-sized enterprises should improve these six factors to retain employees as well as enhance their job performance.
Too Late to Apologize? Collateral Damage, Post-Harm Compensation, and Insurgent Violence in Iraq

Abstract A key piece of conventional wisdom among scholars of modern armed conflict is that collateral damage is often strategically costly in war. Yet most combatants already know this and take actions after mistakes—most prominently, the distribution of “condolence payments” to civilian victims—in order to mitigate these costs. Do these payments work? This question is important not only for policymakers but also for deeper theoretical debates about how civilians respond to combatant signals in war. To examine these issues, I use micro-level conflict event data on 4,046 condolence payments made by Coalition forces to civilian victims during the Iraq War from 2004 to 2008, matching it with corresponding data on collateral damage and insurgent violence. The results of this analysis reveal that post-harm compensation does significantly diminish local rates of insurgent violence, and that this is true across different types of payments (cash handouts or in-kind assistance). Ultimately, these patterns can be best explained by a rationalist mechanism in which civilians update their beliefs about violent events based on new information about combatants’ wartime intentions. The results thus provide a compelling strategic rationale for combatants to compensate their victims in war, and suggest that civilians are not blinded to new information about conflict dynamics by their preexisting biases.
Defining the neural correlates of spontaneous theory of mind (ToM): An fMRI mega-analytic investigation

There is a major debate in the theory of mind (ToM) field, concerning whether spontaneous and explicit ToM are based on the same or two distinct cognitive systems. While there is extensive research on the neural correlates of explicit ToM, revealing consistent activation of the temporo-parietal junction (TPJ) and medial prefrontal cortex (mPFC), few studies investigated spontaneous ToM, with conflicting results, probably due to having small samples of participants. Here, we implemented a mega-analytic approach by pooling data from three fMRI studies, to achieve enough statistical power to better define the neurocognitive mechanisms underlying spontaneous ToM. Participants watched videos in which an agent acquires a true or false belief about the location of a ball. By analysing the blood-oxygen level dependent signal during the belief formation phase for false versus true beliefs, we found a cluster of activation in the right, and to a lesser extent, left posterior parietal cortex spanning the TPJ, but no mPFC activation. Region of interest (ROI) analysis on bilateral TPJ and mPFC confirmed these results and added evidence to the content-selectivity for false beliefs with positive content and asymmetry in laterality of the spontaneous ToM system. Interestingly, the whole brain analysis, supported by an overlap with brain maps, revealed maximum activation in areas involved in visuospatial working memory and attention switching functions, such as the supramarginal gyrus, the middle temporal gyrus, and the inferior frontal gyrus. Taken together, these findings suggest that spontaneous and explicit ToM rely on partially overlapping brain systems. However, spontaneous ToM tasks also show clear differences with explicit ToM tasks.
Detecting Multicollinearity in Regression Analysis

Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of the significant variables under study to be statistically insignificant. This paper discusses on the three primary techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other predictors. Furthermore, advanced regression procedures such as principal components regression, weighted regression, and ridge regression method can be used to determine the presence of multicollinearity.