Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of financial literacy: case study of business students

The study of financial literacy on business students aims to highlight the financial literacy level of senior students of bachelor degree and postgraduate students. Although the study illustrates how financial literacy associated with socio- demographic characteristics, financial education, financial behavior, family background, and social capital. The study employs mixed research methodology in which an administered questionnaire and focus group tools are used. The study finds that students’ interest for obtaining financial information is very low which reflects a low level of financial literacy. Although, theresults show a critical red flag of warning in understanding the basic financial concepts (inflation, simple interest rate calculation and portfolio diversification). Moreover, the results find that family background and financial socialization play a significant role in shaping family members’ financial literacy weather intentionally or spontaneously. Based on theresults of the analysis and focus group justification, theresearcher recommends that there is an insisting need for; enhancing the role of the universities in improving financial literacy, and recommends future studies to expand the range of study in light of the role of high education ministry, universities and financial institutions.
The Appearance of Matter Fields from Quantum Fluctuations of 2D Gravity

It is established that various critical regimes may occur for a model of two-dimensional pure quantum gravity. These regimes correspond to the presence of effective fields with scaling dimensions Δk=−γstr·k/2, k=1, 2, 3 . , where γstr=−1/m, m=2, 3, 4 . is the critical exponent of “string susceptibility” (with respect to the cosmological constant). This behaviour is typical for unitary conformal fields with the central charge c=1−6/m(m+1) in the presence of 2D-quantum gravity. We use the framework of loop equations for the invariant boundary functional, which are exactly solvable in this case.
Dispersion of Particles Released into a Neutral Planetary Boundary Layer Using a Markov Chain–Monte Carlo Model

Abstract The dispersion and concentration of particles (fluid elements) that are continuously released into a neutral planetary boundary layer is presented. The velocity fluctuations of the particles are generated using a Markov chain–Monte Carlo (MCMC) process at random time intervals with a one-step memory. The local mean concentration of the particles is calculated by using a fully Lagrangian method, which performs an efficient near-neighbor search and employs a smoothing kernel for eliminating the statistical noise. The predicted vertical and transversal root-mean-square of the particles’ deviation from their mean position [()1/2 and ()1/2] for an elevated continuous release source in a neutral atmosphere are compared with empirical parameters like the Pasquill–Gifford σz and σy. The numerical predictions of the particle concentration are compared with a Gaussian model and field measurement data on the ground concentration obtained during the Green Glow Program. The comparison between the numerical pr.
Magnetic reconnection and modification of the Hall physics due to cold ions at the magnetopause

Observations by the four Magnetospheric Multiscale spacecraft are used to investigate the Hall physics of a magnetopause magnetic reconnection separatrix layer. Inside this layer of currents and strong normal electric fields, cold (eV) ions of ionospheric origin can remain frozen‐in together with the electrons. The cold ions reduce the Hall current. Using a generalized Ohm's law, the electric field is balanced by the sum of the terms corresponding to the Hall current, the v × B drifting cold ions, and the divergence of the electron pressure tensor. A mixture of hot and cold ions is common at the subsolar magnetopause. A mixture of length scales caused by a mixture of ion temperatures has significant effects on the Hall physics of magnetic reconnection.
Academic Supervision Model Based on Edmodo Application

The Covid 19 pandemic has had an impact on the implementation of academic supervision. This study aims to develop an online-based academic supervision model. The type of research conducted is research and development (Research and Development). The research subjects were 4 (four) school principals and 17 (seventeen) elementary school teachers. Data collection techniques used were interviews, documentation studies, and questionnaires. Qualitative data were analyzed using the Miles and Huberman model. Quantitative data obtained through a questionnaire. Quantitative data were analyzed by validation tests and limited trials using quantitative descriptive techniques with mean and percentage. The results showed that the MARIA academic supervision model (M - Mapping, A - Arranging the solution, R - Running the observation, I - Identifying the data, A – Acting the follow up) based on the Edmodo application has an impact on improving the performance of school principals and techers in elemantary schools, with the result of limited product trials on school principals at the 88% level and techers at the 86% level with a very valid and feasible category to use. The implications of this research were facilitating the implementation of information technology-based supervision, improving the performance of school principals, increasing teacher competence in carrying out learning, and reducing teacher stress when supervised.
Role of microRNAs in the crosstalk between the gut microbiota and intestinal immune system

MicroRNAs (miRNAs) are small non-coding RNA species involved in diverse physiological processes, including immunity. Accumulating evidence suggests that miRNA-induced gene silencing plays a significant role in the regulation of the intestinal immune system by the gut commensal microbiota. This review aims to provide an overview of the intestinal miRNA-mediated crosstalk between the gut microbiota and the host intestinal immune system. First, we describe the role of miRNAs in regulating the intestinal immune system. Then we describe the effect of the gut microbiota on intestinal miRNA expression. Subsequently, we describe the role of miRNAs in the modulation of the intestinal immune system by the gut microbiota. Finally, we describe the effect of host miRNAs on the gut microbiota. Although the entire picture of this complex crosstalk remains unclear, efforts to unravel it will contribute significantly to developing new strategies for preventing and treating intestinal immune disorders such as inflammatory bowel disease.
Interactions enhance the acoustic streaming around flattened microfluidic bubbles

The vibration of bubbles can produce intense microstreaming when excited by ultrasound near resonance. In order to study freely oscillating bubbles in steady conditions, we have confined bubbles between the two walls of a silicone microchannel and anchored them on micropits. We were thus able to analyse the microstreaming flow generated around an isolated bubble or a pair of interacting bubbles. In the case of an isolated bubble, a short-range microstreaming occurs in the channel gap, with additional in-plane vortices at high amplitude when Faraday waves are excited on the bubble periphery. For a pair of bubbles, we have observed long-range microstreaming and large recirculations describing a ‘butterfly’ pattern. We propose a model based on secondary acoustic Bjerknes forces mediated by Rayleigh waves on the silicone walls. These forces lead to attraction or repulsion of bubbles and thus to the excitation of a translational mode in addition to the breathing mode of the bubble. The mixed-mode streaming induced by the interaction of these two modes is shown to generate fountain or anti-fountain vortex pairs, depending on the relative distance between the bubbles.
Immersed surfaces and pencils of planes in 3-space

Let M be a compact connected boundaryless surface and f: M → ℝ3 a smooth immersion transverse to a straight line L. Thus there is an even number p of points xεM such that f(x)εL. Under further transversality assumptions on f (see §3) there is a finite number q of points x of M such that the plane containing f(x) and L touches f(M) at f(x). These assumptions are mild in the sense that they hold for any f in an open dense subset of the space of smooth immersions under consideration. Suppose that the Gaussian curvature of f(M) is positive at q+ of these points and negative at q−, with q = q++ q−. Then where e(M) denotes the Euler number of M.
Analysis of gross motoric analysis of elementary school students: A comparative study of students in hill and coastal areas

Background and Study Aim. Motor skills are a very important ability for every student to have because they can affect their full development. However, the growth and development of elementary school-age children is likely to be influenced by environmental factors such as family, social environment, school environment. This study aims to determine differences in gross motor skills in elementary school students in lower grades in hilly and coastal areas. Material and Methods. This research includes quantitative research with a cross sectional study approach. In this study were given tests and measurements to measure gross motor skills in elementary school students using the Gross Motor Development-2 Test (TGMD-2): running tests, gallops, hops, horizontal jumps, leaps, slides, striking a stationary ball, stationary dribble, catch, kick, overhand throw, and underhand roll. The subjects in this study were male students in the lower grades of SD Negeri 38 Nanga Tayap in the hilly area and SD Negeri 27 Sungai Kakap in the coastal area. The purposive sampling technique resulted in a sample of 45 samples consisting of 21 students from hilly areas and 24 students from coastal areas. Data analysis in this study was assisted by the SPSS Version 26 application. Results. Based on these results, it can be concluded that there are significant differences in gross motor skills between students in hilly areas and coastal areas. The results also show that the gross motor skills of students in hilly areas are classified as low. These results tend to be in the Poor category. Compared to the gross motor skills of students in coastal areas which show a Very Superior dominance. Conclusions. The results of the study proved that the gross motor skills of students in hilly and coastal areas had significant differences. These findings have provided additional references regarding gross motor skills of students. This can be used as material for evaluation by teachers and sports practitioners in designing suitable programs to stimulate gross motor skills in elementary school students.
Attitude and Perception towards Coffee Consumption Effects: Insights from College Students in Mumbai

Coffee is one of the most consumed beverages in the world. It is highly consumed among many parts of India. Various studies concentrate on the effects of consuming coffee and consumer’s behavior in purchasing products related to coffee. Less studies focus on their effect among college students. Therefore, the aim of this study was to determine the coffee consumption habits and the perception of its effect among college students in Mumbai. The study was performed using convenient sampling method with a sample size of 234 students studying in different colleges from Mumbai. This study collected information about the characteristics of the students participated, coffee intake habits and the perception of its effect on consuming coffee. The majority of the students in this study consume coffee. Using factor analysis, the major factors affecting the consumption of coffee on daily basis was identified. The consumption of coffee was more during their important academic activities such as project work or examination days. Due to the burden of exams, students consume coffee to increase their study hours and academic performance.
Investigation of multi-frame image super-resolution based on adaptive self-learning methods

ABSTRACT The goal of this paper is to introduce and demonstrate a new high-performance super-resolution (SR) method for multi-frame images. By combining learning-based and reconstruction-based SR methods, this paper proposes a multi-frame image super-resolution method based on adaptive self-learning. Using the adaptive self-learning method and recovery of high-frequency edge information, an initial high-resolution (HR) image containing effective texture information is obtained. The edge smoothness prior is then used to satisfy the global reconstruction constraint and enhance the quality of the HR image. Our results indicate that this method achieves better performance than several other methods for both simulated data and real-scene images.
Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.

AIM To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). METHODS The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. RESULTS Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and modest harmonization for CHR-P criteria. The semi-structured interview, named Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. CONCLUSIONS Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.
Background Investigations of the KATRIN Pre-Spectrometer

Measurements and analysis methods of this thesis have revealed that an electrostatic retarding spectrometer (MAC-E filter) with a rather large magnetic shielding is essentially a background-free device, if both the effects from Penning traps and Radon emanation processes are controlled by careful design and appropriate counter measures (e.g. LN2 baffles).
Properties of Cherednik algebras and graded Hecke algebras

Acknowledgements Above all I am grateful to my supervisors Ken A. Brown and Iain Gordon for their continuous assistance and encouragement. Their guidance and advice has been invaluable. My special thanks go to my family for their love and support, which kept me grounded. I also wish to thank all the postgraduate students and friends I met along the way, most notably Mo, without whom my stay in Glasgow could not have been as enjoyable. Finally, I thank EPSRC and the University of Glasgow for their financial support throughout my studies. ii Summary The objects of investigation in this thesis are four distinct but related types of algebras, namely the Cherednik algebras and graded Hecke algebras of the title. There are two aspects which connect these four kinds of algebras. Firstly, they are all non-commutative deformations of some skew group algebras. Secondly, symplectic reflection algebras appear in all cases as certain specialisations or degenerations of the algebras in question. Our interest lies in examining ring-theoretic properties of these algebras with a view towards applications to geometric questions. In Chapter 1 we present some basic definitions and background material, which we will refer to in the remainder of this document. Chapter 2 is dedicated to introducing the three types of algebras that are subsumed under the name Cherednik algebras. These are the double affine Hecke algebra, the trigono-metric double affine Hecke algebra and the rational Cherednik algebra, see [Che04] for an overview. We begin a ring-theoretic study of the double affine Hecke algebra and the trigonometric double affine Hecke algebra. In particular, we provide at least partial answers to the questions whether these rings are noetherian and prime. We also investigate their global dimensions. Cherednik algebras all display a dichotomy in their behaviour depending on the specialisation of certain parameters. The parameter specialisations for which the Cherednik algebras are finitely generated over their centres and thus PI algebras are of particular interest to us, because in these cases connections to algebraic geometry come to the forefront. Therefore, we investigate the ring-theoretic properties of the PI Cherednik algebras in more detail and provide faithful representations for these algebras. Our description of these representations is very explicit and makes statements by [Obl04] precise. From these representations we obtain embeddings of the PI Cherednik algebras iii iv into skew group algebras. The second aim of our study of Cherednik algebras is to understand the …
Organic Food Consumption among Generation Y in Malaysia: A Conceptual Framework

Awareness of organic food consumption and green environment sustainability toward a healthy living has become a crucial area of study in recent years. Despite a large number of empirical literatures explaining the multi cultural food consumption behavior among different ethnic groups in Malaysia, the puzzling reality remains, that knowledge on Malaysian Generation ‘Y’ customers’ attitude and buying behavior towards organic food is not well addressed. Therefore, this study aims to fill the research gap by investigating the organic food consumption intention of Generation ‘Y’ customer segment in Malaysian context. The Theory of Planned Behavior underpins the conceptual framework and hypotheses developed for this study. A total of 600 personally administered questionnaires will be distributed to young adults, aged from 20 to 25 years old, based in Selangor, Kuala Lumpur and Malacca via convenience sampling
Approximating submodular k-partition via principal partition sequence

In submodular $k$-partition, the input is a non-negative submodular function $f$ defined over a finite ground set $V$ (given by an evaluation oracle) along with a positive integer $k$ and the goal is to find a partition of the ground set $V$ into $k$ non-empty parts $V_1, V_2, . , V_k$ in order to minimize $\sum_{i=1}^k f(V_i)$. Narayanan, Roy, and Patkar (Journal of Algorithms, 1996) designed an algorithm for submodular $k$-partition based on the principal partition sequence and showed that the approximation factor of their algorithm is $2$ for the special case of graph cut functions (subsequently rediscovered by Ravi and Sinha (Journal of Operational Research, 2008)). In this work, we study the approximation factor of their algorithm for three subfamilies of submodular functions -- monotone, symmetric, and posimodular, and show the following results: 1. The approximation factor of their algorithm for monotone submodular $k$-partition is $4/3$. This result improves on the $2$-factor achievable via other algorithms. Moreover, our upper bound of $4/3$ matches the recently shown lower bound under polynomial number of function evaluation queries (Santiago, IWOCA 2021). Our upper bound of $4/3$ is also the first improvement beyond $2$ for a certain graph partitioning problem that is a special case of monotone submodular $k$-partition. 2. The approximation factor of their algorithm for symmetric submodular $k$-partition is $2$. This result generalizes their approximation factor analysis beyond graph cut functions. 3. The approximation factor of their algorithm for posimodular submodular $k$-partition is $2$. We also construct an example to show that the approximation factor of their algorithm for arbitrary submodular functions is $\Omega(n/k)$.
Oil Spill Segmentation using Deep Encoder-Decoder models

Crude oil is an integral component of the modern world economy. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unavoidable. Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills. The work compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data. Multiple combinations of models are used in running the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and a class IoU of 61.549% for the"oil spill"class when compared with the current benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the"oil spill"class.
Personalized Federated Learning under Mixture of Distributions

The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept shift), which can result in suboptimal performance when the distribution of input data across clients diverges (i.e. covariate shift). Additionally, these techniques often lack the ability to adapt to unseen data, further limiting their effectiveness in real-world scenarios. To address these limitations, we propose a novel approach, FedGMM, which utilizes Gaussian mixture models (GMM) to effectively fit the input data distributions across diverse clients. The model parameters are estimated by maximum likelihood estimation utilizing a federated Expectation-Maximization algorithm, which is solved in closed form and does not assume gradient similarity. Furthermore, FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education?

This paper explores the potential of artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, the study provides a comprehensive perspective on the future role of educators in the face of advancing AI technologies. Findings suggest that although some believe AI may eventually replace teachers, the majority of participants argue that human teachers possess unique qualities, such as critical thinking, creativity, and emotions, which make them irreplaceable. The study also emphasizes the importance of social-emotional competencies developed through human interactions, which AI technologies cannot currently replicate. The research proposes that teachers can effectively integrate AI to enhance teaching and learning without viewing it as a replacement. To do so, teachers need to understand how AI can work well with teachers and students while avoiding potential pitfalls, develop AI literacy, and address practical issues such as data protection, ethics, and privacy. The study reveals that students value and respect human teachers, even as AI becomes more prevalent in education. The study also introduces a roadmap for students, teachers, and universities. This roadmap serves as a valuable guide for refining teaching skills, fostering personal connections, and designing curriculums that effectively balance the strengths of human educators with AI technologies. The future of education lies in the synergy between human teachers and AI. By understanding and refining their unique qualities, teachers, students, and universities can effectively navigate the integration of AI, ensuring a well-rounded and impactful learning experience.