Miracle-dz/UniFAR-FTUs huggingface dataset:
OKUL ÖNCESİ ÖĞRETMENLERİNİN PEDAGOJİK İNANÇLARININ ÖZ-YETERLİK VE ELEŞTİREL DÜŞÜNME AÇISINDAN İNCELENMESİ EXAMİNİNG THE PEDAGOGİCAL BELİEFS OF PRESCHOOL TEACHERS İN TERMS OF SELF-EFFİCACY AND CRİTİCAL THİNKİNG

2 MEB, Öğretmen, Sakarya, aykutarslan66@gmail.com, ORCID: 0000 0001-9042-3514 3 Dr. Öğr. Üyesi, İstanbul Aydın Üniversitesi, Eğitim Fakültesi, İstanbul-Türkiye, alikutluca@aydin.edu. tr, ORCID: 0000-0002-1341-3432 Öz: Bu araştırmanın temel amacı; okul öncesi öğretmenlerinin öğretme-öğrenmeye yönelik pedagojik inançların öz-yeterlik ve eleştirel düşünme becerileri açısından incelenmektir. Bununla birlikte okul öncesi öğretmenlerinin eleştirel düşünme becerileri, pedagojik ve öz-yeterlik inançlarının kıdem ve eğitim düzeyi değişkenlerine göre anlamlı farklılık gösterip göstermediği incelenmiştir. Araştırma Sakarya’nın Adapazarı, Arifiye, Erenler ve Serdivan ilçelerinde görev yapan 272 okul öncesi öğretmenine uygulanmıştır. Araştırma ilişkisel tarama modelin de gerçekleştirilmiştir. Araştırmada veri toplamak amacıyla “Pedagojik İnanç Sistemleri Ölçeği, Eleştirel Düşünme Tutum Ölçeği ve Öğretmen Öz-Yeterlik Ölçeği” kullanılmıştır. Ölçekler yardımıyla toplanan verilerin analizi SPSS 22 yardımıyla çözümlenmiştir. Bu araştırmadaki problem sorularını yanıtlamak için, betimsel istatistikler yapılmıştır. Araştırmanın sonucuna göre okul öncesi öğretmenlerinin pedagojik ve öz-yeterlik inançlarının yüksek olduğu, eleştirel düşünme tutumlarının ortalamanın üzerinde olduğu sonucuna ulaşılmış, okul öncesi öğretmenlerinin eleştirel düşünme tutumları ve öz-yeterlik inançlarının, pedagojik inanç ile orta düzeyde ve anlamlı bir ilişkisi olduğu sonucuna ulaşılmıştır. Buna ek olarak ilgili bağımsız değişkenlerin birlikte, okul öncesi öğretmenlerinin pedagojik inanç düzeyi üzerindeki toplam varyansın yaklaşık %35’ini açıkladığı sonucuna ulaşılmıştır
In vitro propagation and tissue culture of selected monocots

A procedure for the in vitro propagation of Lilium regale, Hemero­ callis, and Hippeastrum varieties was developed. A reliable method for the procurement of excellent explants for initiating cultures was established for a representative member of each group. L . regale explants were obtained from within quiescent bulbs. Ex­ plants grew well in vitro, producing callus tissue and bulblets on a variety of media types, even in the absence of exogenous growth regula­ tors. Explants cultured on IAA and kinetin supplemented media exhibited a greater degree of variation of bulblet production and callus prolifera­ tion; however, average bulblet production was increased slightly. 2,^-D medium inhibited organogenesis, in general, but promoted callus prolifera­ tion at low concentrations (0.5 and. 1.0 mg/l). Hemerocallis explants were derived from the scape and floral bud tissues. Explants grew slowly in vitro; necrosis and contamination was often a problem. Plantlet regeneration occurred from both types of explants when cultured on high kinetin (1.0 mg/l) medium. Subsequent experimentation focused on the morphogenetic potential of scape explants, since shoot initiation occurred freely in response to high kinetin medium. 2,4-D medium (l.O mg/l) promoted growth as a mixture of abnormal roots interspersed with callus. No plantlet regeneration was obtained from this tissue. Hippeastrum explants were derived from the inflorescence stem tissue within quiescent bulbs. Explants cultured on medium without growth regulators developed callus and bulblets. The callus tissue, however, was not easily separated from the explants and could not be cultured independently. Bulblet production was not significantly enhanced by supplementing the basal medium with IAA and/or kinetin. 2,4-D medium induced prolific root morphogenesis with an occasional bulblet. The influence of the auxin-cytokinin ratio upon the organogenic potential of explants, using IAA and kinetin, was investigated. L. regale and Hippeastrum explants were not influenced by auxin-cytokinin manipula­ tions. However, Hemerocallis scape explants responded classically by producing shoots on high kinetin medium and roots on high IAA medium. A reliable procedure for the establishment of all plantlets in soil was developed. Reid Harvey Graves Department of Biology The College of William and Mary in Virginia
Dynamic Appearance Modelling from Minimal Cameras

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.
Public Emotional Diffusion over COVID-19 Related Tweets Posted by Major Public Health Agencies in the United States

Abstract Since the end of 2019, the COVID-19 outbreak worldwide has not only presented challenges for government agencies in addressing public health emergency, but also tested their capacity in dealing with public opinion on social media and responding to social emergencies. To understand the impact of COVID-19 related tweets posted by the major public health agencies in the United States on public emotion, this paper studied public emotional diffusion in the tweets network, including its process and characteristics, by taking Twitter users of four official public health systems in the United States as an example. We extracted the interactions between tweets in the COVID-19-TweetIds data set and drew the tweets diffusion network. We proposed a method to measure the characteristics of the emotional diffusion network, with which we analyzed the changes of the public emotional intensity and the proportion of emotional polarity, investigated the emotional influence of key nodes and users, and the emotional diffusion of tweets at different tweeting time, tweet topics and the tweet posting agencies. The results show that the emotional polarity of tweets has changed from negative to positive with the improvement of pandemic management measures. The public's emotional polarity on pandemic related topics tends to be negative, and the emotional intensity of management measures such as pandemic medical services turn from positive to negative to the greatest extent, while the emotional intensity of pandemic related knowledge changes the most. The tweets posted by the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States have a broad impact on public emotions, and the emotional spread of tweets' polarity eventually forms a very close proportion of opposite emotions.
IS TRADE OPENNESS THE REASON OF HIGH ENERGY DEMAND IN CHINA?

The present study aims to examine the short-run and long-run impact of China’s trade liberalization policies on its energy demand over the period from 1980 to 2018. The results of Autoregressive Distributed Lag approach of co-integration show that energy consumption significantly increases as a result of trade openness and increase in real Gross Domestic Product (GDP). The results of the granger causality test also confirm the unidirectional causality running from trade openness and real GDP to energy demand. The results of the study have an important implication because if China wants to continue its trade liberalization policies then it must increase its energy production.
Human Hair Inverse Rendering using Multi-View Photometric data

We introduce a hair inverse rendering framework to reconstruct high-ﬁdelity 3D geometry of human hair, as well as its re-ﬂectance, which can be readily used for photorealistic rendering of hair. We take multi-view photometric data as input, i.e., a set of images taken from various viewpoints and different lighting conditions. Our method consists of two stages. First, we propose a novel solution for line-based multi-view stereo that yields accurate hair geometry from multi-view photometric data. Speciﬁcally, a per-pixel lightcode is proposed to efﬁciently solve the hair correspondence matching problem. Our new solution enables accurate and dense strand reconstruction from a smaller number of cameras compared to the state-of-the-art work. In the second stage, we estimate hair reﬂectance properties using multi-view photometric data. A simpliﬁed BSDF model of hair strands is used for realistic appearance reproduction. Based on the 3D geometry of hair strands, we ﬁt the longitudinal roughness and ﬁnd the single strand color. We show that our method can faithfully reproduce the appearance of human hair and provide realism for digital humans. We demonstrate the accuracy and efﬁciency of our method using photorealistic synthetic hair rendering data.
Genetic Characteristics of Brown Trout Salmo trutta from the Tatra National Park in Poland

In the present study the genetic diversity and structure of the brown trout in the Tatra National Park (TNP) was analyzed by application of ten microsatellites and one mitochondrial DNA markers. For this purpose, a total of 96 fish from 7 main streams located on the territory of the TNP were sampled. Examined fish was characterized by a moderate level of the genetic diversity (I=1.196-1.549) and PIC=0.567-0.707). The mean values of observed (Ho) and expected heterozygosity (He) varied from 0,577 to 0,760 and from 0,609 to 0,723, respectively. Based on genetic analysis, five haplotypes were identified, and the computed haplotype diversity (h) and nucleotide diversity (π) values were 0.561 and 0.001, respectively. Assessed genetic differentiation between the examined groups varied from 0.016 do 0.085 and the global test of differentiation (Fst=0.062) among groups provided statistically insignificant values. Constructed Bayesian skyline plot revealed stable population size, however an evidence of a historical decline of the population size in the studied fish was detected. Similarly, to microsatellite DNA analysis, the obtained data on mtDNA analysis showed weak genetic clustering, where two genetic clusters were identified, however, haplotype frequencies did not follow geographical pattern.
Sharper Generalization Bounds for Pairwise Learning: Supplementary Material

To prove Theorem 1, we need to introduce some lemmas. The following lemma is attributed to [7], which provides far-reaching moment bounds for a summation of weakly dependent and mean-zero random functions with bounded increments under a change of any single coordinate. We denote S\{zi} the set {z1, . , zi−1, zi+1, . , zn}. The Lp-norm of a random variable Z is denoted by ‖Z‖p := ( E[|Z|] ) 1 p , p ≥ 1.
Effectiveness of Google Classroom as a Digital Tool in Teaching and Learning: Students’ Perceptions

: - The aim of this study is to evaluate Students’ perceptions on the effectiveness of Google Classroom as a Digital tool in Teaching and Learning. The study was conducted through a Survey Research Design to investigate the Students’ Perceptions. The population of study consists of all UG II Undergraduate Students, Faculty of Education, Kebbi State University of Science and Technology Aliero (KSUSTA). Data analysis was conducted using Descriptive Statistics. The results of the study indicated that Google Classroom is effective in improving Students access and attentiveness towards learning, knowledge and skills gained through Google Classroom makes Students to be active learners, as a Digital Tool, it provides meaningful feedback to both Students and Parents. However, Poor network hinders students from effective utilization of Google Classroom; thus, submitting their work late. Therefore, teachers should integrate the conventional teaching with Google Classroom to improve Students’ Performance. Google Classroom should also be a form of assessing Students’ Assessment through online Assignments and Quizzes; hence making Students to participate actively in Educational Technology Classes. The University should also provide a standard network to enable Students join Google Classroom and submit their assignments on time.
Fast Computation by MLFMM-FFT with NURBS in Large Volumetric Dielectric Structures

A refinement for the computation of the rigorous part of the multi-level fast multipole method (MLFMM) of analyzing volumetric objects is presented. A scheme based on the fast Fourier technique (FFT) is proposed with the objective of reducing the computational resources required to accurately analyze large homogeneous and non-homogeneous dielectric volumes. In order to reduce the memory requirements, the storage of the near-field terms of the method of moments (MoM) matrix is performed only for the positions corresponding to a parallelepiped with the size of the level 1 block of the MLFMM, computed with the vacuum permittivity, taking advantage of the Toeplitz symmetry present in regular hexahedral meshes. The FFT avoids applying the near-field MoM matrix in the iterative solution process. The application of this approach results in huge improvements in terms of memory usage, but also a speeds up the iterative solution process because the use of three-dimensional (3D) FFTs is very efficient for computing convolutions when the number of unknowns of the problems becomes very large as happens in volumetric problems. We also propose a new approach for the numerical treatment of the transition of the dielectric permittivity between different dielectrics or between a dielectric and a free space. To validate the computation technique, the radar cross section (RCS) of several dielectric bodies is computed using the classical MLFMM approach and it is compared with the presented FFT-based-MLFMM solution. The results demonstrate that the efficient memory and computation time usage of the proposed approach.
Get the Key Points: Summarization by Grouping Sentences

Text summarization task aims to automatically generate a summary for a given document. The input is a long document and corresponding summary usually consists of few sentences. There are mainly two methods of summarization: extraction and abstraction. The extractive method directly selects salient sentences from the document as the summary whereas the abstractive method rewrites and compresses the document into a shorter one as the summary. These two methods can also be combined in an extract-then-abstract way where salient sentences in the source article are extracted first and then rewritten to the summary.
EXAMINING THE CONTRIBUTION OF VALUABLE AND RARE RESOURCES AND CAPABILITIES TO PERFORMANCE OF MICRO ENTERPRISES

This article examined the contribution of resources and capabilities to the competitiveness and performance of micro enterprises within the context of resource-based view. Cross-sectional survey design was employed. The data came from 183 registered micro enterprises in Catarman. The result showed that the value of resource-capability combinations is inversely related to competitive advantage, while the rareness of resource-capability combinations is positively related. The direct and indirect effects of competitive advantage between value and rareness of resource-capability combinations and performance were established. The moderating effect of business age between competitive advantage and performance was determined. This study added support to the application of resource-based view to micro enterprises in rural areas in a developing economy and unravel important implications for micro enterprises in Catarman.
DEAR CUSTOMER, THANK YOU FOR YOUR REVIEW: THE SERVICE FAILURE-RECOVERY DYADIC INTERACTIONS IN THE RESTAURANT INDUSTRY

The advent of Web 2.0 has encouraged restaurant customers to post online reviews, and oftentimes, not in favor of the company. When a service failure occurs, the customer may voice their complaints publicly online. The company, on the other hand, has the opportunity to respond to these complaints and use it as a part of their service recovery strategy. While some companies are responding to negative reviews, only a few have the knowledge on how to do it effectively. Built on perceived justice framework: distributive, procedural, interactional; and service failure severity type: outcome-process, major-minor, present study intends to understand different resolution styles adopted by the company to varying types of customer complaint. The findings outline: (1) the vast majority of the company exhibits only a low level of responsiveness to complaints; (2) there seems to be a correlation between physical and psychological loss with time loss, severe emotions and switching intentions; (3) however, different strategies depending upon service failure severity are yet to be implemented by the company; (4) while components in interactional justice are mostly performed, rude responses are also frequently applied. Further elaboration of the findings and insights for marketing practice are discussed in the text.
Plug-and-play textual style transfer

Stylistic variations are intrinsic in human language and reflects the social dynamics of language use (Bell, 1984). Generally, linguistic style is a matter of word choice, whether a speaker choose to use one linguistic elements instead of another (Coupland, 2011). The task of textual style transfer is defined as rendering a source text into another linguistic style without changing its underlying semantic content (Garbacea and Mei, 2020), such as converting a negative review to positive or turning an informal sentence into a formal one. Such a task lends itself to a variety of practical applications, including but not limited to automatic review generation and assisting systems for literary creation. While great progress has been made towards fluent and content-preserving style control, the current studies still suffer from the following shortcomings. Since most studies focus on binary styles (positive/negative or formal/informal), achieving finer-grain style transfer remains challenging, both in terms of dataset creation and modeling. Secondly, generated texts are mostly short sentences as long as 15 to 20 words. Generating long and coherent texts with various controllable styles remains far from a solved problem. In this work, we plan to adopt the framework of Plug and Play Language Model (Dathathri et al., 2020) to text style transfer. We utilized the pretrained transformer to overcome the length limit in language generation and the attribute classifiers to control and guide the generation process.
Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System

Electrical energy and power demand will experience exponential increase with the rise of the global population. Power demand is predictable and can be estimated based on population and available historical data. However, renewable energy sources (RES) are intermittent, unpredictable, and environment-dependent. Interestingly, microgrids are becoming smarter but require adequate and an appropriate energy storage system (ESS) to support their smooth and optimal operation. The deep discharge caused by the charging–discharging operation of the ESS affects its state of health, depth of discharge (DOD), and life cycle, and inadvertently reduces its lifetime. Additionally, these parameters of the ESS are directly affected by the varying demand and intermittency of RES. This study presents an assessment of battery energy storage in wind-penetrated microgrids considering the DOD of the ESS. The study investigates two scenarios: a standalone microgrid, and a grid-connected microgrid. The problem is formulated based on the operation cost of the microgrid considering the DOD and the lifetime of the battery. The optimization problem is solved using non-linear programming. The scheduled operation cost of the microgrid, the daily scheduling cost of ESS, the power dispatch by distributed generators, and the DOD of the battery storage at any point in time are reported. Performance analysis showed that a power loss probability of less than 10% is achievable in all scenarios, demonstrating the effectiveness of the study.
Optimization of Message Ferrying UAV Paths using Monte Carlo Tree Search and Reinforcement Learning

Nowadays, communication is an indispensable element for the vast majority of human activities, such as economic, social, or other. Although there are several methods for this purpose, such as the Internet and mobile phones, there are situations where the connection may fail, as in natural catastrophes or in remote villages where there is no telecommunications infrastructure. In these cases, an alternative method of message transport is required, such as the use of Unmanned Aerial Vehicles (UAVs). We propose the adaptation of two groups of algorithms, one of planning (Monte Carlo Tree Search (MCTS)) and another of Machine Learning (Reinforcement Learning (RL)), for the search of efficient paths in Delay Tolerant Networks (DTNs). This communication was considered in two distinct network topologies: (1) from all nodes to a common node and (2) from all nodes to all nodes. The results show that both RL and MCTS methods present some improvements over the Travelling Salesman Problem (TSP), in smaller networks, for both topologies. RL methods proved to have more consistent results and require less memory when comparing to MCTS. For larger networks, some optimizations are still needed in order to obtain significant improvement.
Examining Serendipitous Encounters and Self-Determination in Twitter-Enabled Innovation

Serendipity refers to unexpected encounters with ideas or insights and their intentional application to achieve favorable outcomes. Despite extensive prior studies, the concept lacks theoretical logic and empirical validation regarding the role of an intentional act in the relationship between serendipitous encounters and their favorable outcomes. Drawing from self-determination theory, we develop a model that highlights the role of needs satisfaction in explaining this relationship. Positioning the empirical context to fortunate discoveries of information and social connections in professional use of Twitter, we validate the model by a cross-sectional survey study of 473 users. The model builds on the observation that individuals’ serendipitous encounters are associated with Twitter-enabled innovation, that is, a contextualized form of task innovation. The study findings support the research model revealing that serendipitous encounters are positively associated with needs satisfaction and that needs satisfaction is positively associated with Twitter-enabled innovation. In other words, fortunate discoveries of new information and contacts increase Twitter users’ intent to utilize the platform in new ways to accomplish work when the three key psychological needs of autonomy, competence, and relatedness are satisfied.