Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring student teachers’ experiences of engaging in Hands of the World, a contextualised global intercultural eTwinning project

This paper considers the impact of student teachers’ participation and engagement in the award-winning eTwinning international Hands of the World (HOTW) project which connects over 2000 students and their teachers in 50 schools across the world. Students participated in a wide range of educational collaborative experiences, before and during the first COVID-19 lockdown of 2020, to develop their knowledge and understanding of intercultural education. Student teachers live and work in an interconnected and intercultural world and having a knowledge of how to embed diversity, inclusion and cultural awareness into their practice is important to ensure that their pupils develop a positive intercultural mindset. An explanatory design analysed students’ end of year reflections on participating in the project, which were publicly available on the project’s Padlet™ page. Data were analysed thematically focusing on participation and derived benefits. The findings revealed a continuum of engagement with some students participating at a lower level, where they would observe and question, whilst others had a higher level of engagement engaging in the activities and/or applying the project to practice. Our analysis enabled us to identify that although there were various barriers to participation, the design of the project enabled students to engage at various levels with a higher level of engagement evidenced during the COVID-19 lockdown. Our findings suggest that effective intercultural learning in teacher education requires a range of intercultural opportunities that can be accessed at a variety of levels and at different times throughout the year to provide a rich learning experience.
On the Analysis and Assessment of First-Order Group Contribution Models for the Calculation of Normal Boiling Point and Critical Properties of Pure Compounds

The thermodynamic properties of pure compounds are relevant data for process systems engineering. Different first-order group contribution models have been reported in the literature to calculate these properties and they are also widely employed in commercial process simulators. However, they may have some limitations and, consequently, a reliable comparison of these models is required to analyze their performance and to determine the best alternative for the calculation of pure compound properties. This paper reports the implementation and evaluation of several first-order group contribution models to calculate the normal boiling point and critical properties (temperature, pressure, and volume) of pure compounds. The performance of these models was characterized and compared for several compound families using a standardized approach to determine their group contributions and parameters. An artificial neural network model was also applied and assessed to improve the estimations obtained with the best group contribution models. Results showed that the calculation of critical temperature was challenging for several compound families where AARD values ranged from 0.05 to 56.28%, while the group contribution models were more accurate to estimate the critical volume with AARD values ranging from 0.48 to 35.99%. This study allows us to identify the limitations and gaps of this type of thermodynamic models with the objective of improving its performance for the calculation of pure compound thermodynamic properties. The findings of this study can help to enhance the capabilities of thermodynamic models for the calculation of the normal boiling point and critical properties of pure compounds, which are relevant for the process systems engineering of new operations and products.
Regularization of the inverse medium problem : on nonstandard methods for sparse reconstruction

In this thesis, we investigate nonstandard methods for the stable solution of the inverse medium problem. Particularly, we consider the linearization of the model of the scattering process given by the Born approximation and investigate regularization methods that are designed for sparse reconstruction. In numerical experiments we demonstrate that sparsity constraints contribute to meaningful reconstructions from synthetic and even measurement data. In our investigations, we consider both iterative and variational methods for the solution of the inverse problem. Starting from the Landweber iteration, we discuss existing variants of this approach and develop a novel sparsity-enforcing method which is based on the Bregman projection. Furthermore, we consider a variational regularization scheme. First, we develop a novel parameter choice rule based on the L-curve criterion designed for sparse reconstruction. We then propose to replace the variational problem by some smooth approximation and provide an exhaustive investigation regarding stability of this approach. The theoretical investigations of each of the methods proposed in this work are complemented by a numerical evaluation.
The Role of Ethnopedagogy in Shaping Positive Attitudes towards Traditional Values of Kazakh People among Future Teachers in Kazakhstan

The aim of the research is to establish whether the attitudes held by master’s students majoring in Education towards the traditional values of the Kazakh people change as a result of studying an elective course in Ethnopedagogy. Based on an analysis of works by Kazakh researchers, a classification of the traditional values of the Kazakh people was made. On the basis of this classification, the authors developed a questionnaire to identify attitudes towards these values. A survey was conducted among master’s degree students majoring in Education at S. Toraighyrov Pavlodar State University and L.N. Gumilyov Eurasian National University. The sample of the study was 119 people: 59 students formed the experimental group (the one where the elective course was taught), the remaining 60 were in the control group (that did not study the course). The survey was conducted twice: before the beginning and after the end of the course. The results of the first survey showed no statistically significant differences between the experimental and control groups. The second survey clearly showed positive changes in the attitudes towards the traditional values of the Kazakh people in the experimental group, while in the control group the results remained approximately at the level of the first survey. The authors came to the conclusion that it is necessary to strengthen the national and cultural components in the content of pedagogical education in the Republic of Kazakhstan.
Detection Scheme for Tampering Behavior on Distributed Controller of Electric-Thermal Integrated Energy System Based on Relation Network

In recent years, with the development of smart grid, the power systems and other energy systems are gradually forming integrated energy systems. The electric-thermal integrated energy system is a mature integrated energy system at present. The electric-thermal integrated energy system uses modern communication technology to realize the comprehensive regulation of electric energy and thermal energy, which greatly improves the efficiency of energy use. However, this also greatly increases the risk of malicious tampering with the energy dispatch system. In this paper, we study the regulation of electric-thermal integrated energy systems considering false data injection attacks. First, we establish a compromised model of an electric-thermal integrated energy system considering false data injection attacks. Then, we designed vulnerable variable observers for different tampering scenarios to observe the tampered variables. Finally, considering the relationship between the observed data and the measured data, we design a tampering behavior detection method based on relation network. The simulation results verify the effectiveness of the detection method proposed in this paper.
Praktikum Kimia SMA Kelas XI pada Materi Asam Basa Sesuai Model Discovery dan Project Based Learning

Penelitian ini dilakukan dengan tujuan mengembangkan penuntun praktikum asam basa sesuai discoverylearning dan project based learning . Pengembangan penuntun praktikum berdasarkan Langkah-langkahdiscovery learning dan project based learning. Penuntun praktikum disusun oleh penulis dan divalidasioleh dosen. Penuntun praktikum kemudian diuji cobakan ke SMA Muhammadiyah 8 Kisaran sebagailokasi penelitian. Penelitian dilaksanakan pada bulan Februari sampai Maret 2019. Penelitianmenggunakan sampel sebanyak dua kelas yaitu kelas eksperimen I yang diajar menggunakan penuntunpraktikum sesuai discovery learning dan kelas eksperimen II yang diajar menggunakan penuntunpraktikum sesuai project based learning. Angket yang diberikan kepada dosen dan guru menunjukkanhasil nilai rata-rata penuntun project based learning lebih tinggi dibandingkan dengan discoverylearning. Sedangkan peningkatan hasil belajar peserta didik yang diajar dengan menggunakan penuntundiscovery learning lebih tinggi dibandingkan project based learning yaitu masing-masing sebesar79,48% dan 60,33%. Hal ini terjadi disebabkan peserta didik belum terbiasa menyusun sendirirancangan praktikum sebagaimana yang harus dilakukan pada kelas yang menerapkan project basedlearning
Symmetric Modeling of Diversiﬁcation Strategy and Organizational Structure on Financial Performance: Evidence from China

: Diversiﬁcation is a strategy adopted by many enterprises in the process of expansion. The success of the diversiﬁcation of an enterprise mainly depends on the choice and implement of strategy; choosing an organizational structure that ﬁts the type of diversiﬁcation strategy used is fundamental to improving ﬁnancial performance. Based on the empirical research method, this study establishes a symmetric model of diversiﬁcation strategy and organizational structure on ﬁnancial performance and selects data from 613 A-share-listed companies in China, from 2012 to 2016, to test the impacts of unrelated and related diversiﬁcation strategies on ﬁnancial performance, as well as the moderating effects of united company, holding company, and multidivisional structures on such relationships. The results show that there is asymmetry between the diversiﬁcation strategy adopted and ﬁnancial performance, and a related diversiﬁcation strategy should be adopted as a priority; the symmetry of an unrelated diversiﬁcation strategy and holding company structure on ﬁnancial performance is partially conﬁrmed, and other elements should be adopted, simultaneously, to improve this symmetry; a related diversiﬁcation strategy and multidivisional structure on ﬁnancial performance is symmetric. The above ﬁndings will provide references for the diversiﬁcation strategy choice and the organizational structure design of enterprises. the use of a related diversiﬁcation strategy on ﬁnancial performance than the united and holding company structures
Brain-Cognition Relationship: Integrating Task-Based fMRI Across Tasks Reliability

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction ( r =.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=~.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities ( r =.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction ( r =.56) while still providing excellent test-retest reliability (ICC=~.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.
Renormalization and asymptotic freedom in quantum gravity through the equivalence theorem

A perturbative method of renormalization is developed for the Einstein-Hilbert Lagrangian with a cosmological constant. In the method described, the renormalizable Lagrangian of higher-derivative gravity is rewritten such that the higher-derivative terms are redundant (i.e., vanish on the Einstein shell), allowing them to be removed after renormalization by performing a local field transformation and redefining the parameters. At each order in perturbation theory, this allows the renormalization of the couplings in the Einstein-Hilbert Lagrangian to be calculated from the renormalized couplings of the higher-derivative theory. It is shown that the Lagrangian obtained by this method is asymptotically free at one-loop order in the essential dimensionless coupling $\Lambda G$, where $G$ is Newton's constant and $\Lambda$ is the cosmological constant.
Corporate Marketing Strategy Analysis with Machine Learning Algorithms

In the continuous development of Chinese enterprises, the ability to use reasonable and scientific marketing strategies is directly related to the economic benefits and social development of the whole enterprise and even society. This paper explains the importance of standardized enterprise marketing strategies based on machine learning, and on the basis of grasping the characteristics of large capacity, variety, fast flow, and high value of big data, this paper outlines and analyzes the problems that generally exist in China’s enterprises in machine learning-based marketing, so as to provide improvement ideas for measures to improve enterprise marketing and provide benchmarks for enterprise promotion, in order to achieve a market economy based on machine learning, maximize the overall benefits, and achieve healthy development.
Enabling efficient execution of a variational data assimilation application

Remote sensing observational instruments are critical for better understanding and predicting severe weather. Observational data from such instruments, such as Doppler radar data, for example, are often processed for assimilation into numerical weather prediction models. As such instruments become more sophisticated, the amount of data to be processed grows and requires efficient variational analysis tools. Here we examine the code that implements the popular SAMURAI (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation) technique for estimating the atmospheric state for a given set of observations. We employ a number of techniques to significantly improve the code’s performance, including porting it to run on standard HPC clusters, analyzing and optimizing its single-node performance, implementing a more efficient nonlinear optimization method, and enabling the use of GPUs via OpenACC. Our efforts thus far have yielded more than 100x improvement over the original code on large test problems of interest to the community.
A 28-GHZ ANTENNA FOR 5G MIMO APPLICATIONS

In this letter a four-port multi-input-multi-output (MIMO) antenna for 5G applications is proposed. This antenna is compact with a size of 11 mm × 31 mm excluding feed lines. The radiation patterns of the antenna show pattern diversity in the azimuthal plane, and each antenna element has an end-fire gain about 10 dBi by employing an array of metamaterial unit cells. The isolation between the antenna elements with edge to edge separation < λ0/5.5 at 28 GHz is enhanced by trimming the corners of the rectangular high refractive index metamaterial region along with a ground stub between antennas. The proposed antenna is fabricated, and each antenna element has return loss, Snn < −10 dB with isolation, Snm > 21 dB in the frequency range 26 GHz to 31 GHz, which makes this antenna potential candidate for MIMO application at 28 GHz band enabling 5G cellular communications.
Karlhede's invariant and the black hole firewall proposal

The Karlhede invariant is formed from the contraction of the covariant derivative of the Riemann tensor. It is a coordinate invariant that vanishes at the Schwarzschild event horizon r = 2m. The vanishing of the invariant allows an observer to construct a local measuring device and use it to detect an event horizon while falling into a black hole. Recent proposals postulate the existence of a “firewall” at the event horizon that may incinerate an infalling observer. These proposals face an apparent paradox if a freely falling observer detects nothing special in the vicinity of the horizon. The behavior of Karlhede’s invariant raises the possibility that the event horizon is a real physical membrane with measurable properties that are detectable by a freely falling observer. PACS numbers: 04.70. -s It is widely assumed that a freely falling observer cannot determine his distance from the source of a gravitational field using local measurements of the field and no outside knowledge. For instance, while an observer is able to measure tidal forces using a local experiment, in the absence of knowledge of the mass of the source toward which he is falling, tidal force observations are insufficient to determine the distance from that source. A broadly accepted consequence is that an observer falling toward a black hole singularity will not be able to determine by performing onlylocal measurements if, and when, he crossed the horizon. This is consistent with our understanding that the peculiar properties of the Schwarzschild metric at the event horizon are purely artifacts of the coordinates; no physical singularity exists at the horizon. Yet more recently, there have been several proposals [1] that suggest that the event horizon is a physically special region of spacetime. A resolution of the black hole information loss paradox is offered by positing the existence of a “firewall”, a region of spacetime at or near the horizon that would incinerate an observer close to the horizon [1]. The firewall proposal is quantum mechanical in nature. However, the possible existence of such a firewall raises renewed interest in the possibility that classical general relativity might also predict unique properties associated with the location of the event horizon. In 1982, Karlhede et al., [2] investigated the properties of local geometry in terms of the Riemann curvature tensor and its higher derivatives. They discovered that the lowest-order non-trivial scalar term, constructed by contracting the covariant derivative of the curvature tensor with itself, has unexpected properties. Karlhede’s invariant can be written as
Smart Shopping Cart Learning Agents

—The paper describes the design, implementation and user evaluation of utility-based and goal-based intelligent learning agents for smart shopping cart. In keeping user’s shopping habits or user’s shopping list, they guide visitors through the shops and the goods in the shopping center or according to new promotions in the shops, respectively. It is envisaged that concrete implementation of the shopping agents will be running on each shopping cart in the shopping centers or on holographic displays. The k-d decision tree, the best identification tree, and reinforcement-learning algorithm are used for agents learning. The task environment is partially observable, cooperative, deterministic, and a multi - agent environment, with some stochastic and uncertainty elements. It incorporates text-to-speech and speech recognizing technology, Bluetooth low energy technology, holographic technology, picture exchange communication system. Machine learning techniques are used for agents modeling. This kind of intelligent system enables people with different communication capabilities to navigate in large buildings and in particular to shop in the large shopping centers and maximize user comfort. Some initial user opinions of the shopping cart agents are presented. Different embodiments of the shopping agents are discussed like holographic agent embodiment, embodied virtual agent or social robot embodiment. Some approaches of realization of a smart robotic shopping cart that can follow the user are discussed too. The performance of the Q learning algorithm with an introduced environment measures model (a model of the environment criteria) is proposed and explored. Study of the learning parameter is presented. Smart Shopping Cart Learning Agents modeling and development task allows for applying and improving the learning algorithms.
Effect of weld-line on tensile properties for thermoplastic glass filled material

In an injection molding process, a weld-line forms when two flow fronts meet each other. Weld-line is a weak area which reduces the strength of the part locally. For multiple gate and complex part, molding weld-lines are unavoidable, therefore mechanical behavior of the weld-line needs to be predicted. This paper presents the effect of weld-lines on tensile properties of glass fibers reinforced polyamide-6 composite. An injection molding plaque tool has been designed and manufactured with the inputs from mold flow simulation software. The gating system is designed in such a way that the angle between two flow fronts is as minimum as possible, which theoretically gives the lowest strength at weld locations. The plaques are manufactured with BASF material Ultramid B3WG6 grade (glass filled 30%) which is a widely used engineering plastic material. Test specimens have been cut on a plaque for various angular positions. Experimental evaluation for tensile testing, tensile modulus, and stressstrain behavior for specimens with and without weld-line at different angular positions was evaluated as per ISO 527-2 standards. It has been established that the weld line significantly influences the tensile properties of the part. The presence of a weld line results in a significant decrease in the tensile strength of the part. Experimental results show approximately 58% reduction in tensile modulus and 49% reduction in stress at break values in specimen with weld-lines as compared to specimen without weld-lines.
Uniaxial strain-modulated conductivity in manganite superlattice (LaMnO3/SrMnO3)

We have investigated the magnetic ordering and electrical conductivity transitions of (LaMnO3)2/(SrMnO3)2 superlattices grown on SrTiO3 substrate based on density-functional theory. It is found that the uniaxial tensile strain along the z axis of about 1.4% induced a magnetic transition from antiferromagnetic to ferromagnetic ordering. At the interface the orbital order changes from a combination of x2−y2 and 3z2−r2 to x2−y2 as strain becomes more compressive; as a result the electrical transport is transformed from three-dimensions to two-dimensions at the high uniaxial compressive strain. Our results suggest that the out-of-plane electrical conductivity can be modulated and controlled by uniaxial strain.