Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception towards Online and Face to Face Learning

The COVID-19 pandemic scenario suspended educational activities. The traditional face-to-face educational model was replaced by an online one. The online mode of instruction has been adopted by higher education institutions. The direct conversation was broken off. To understand the justification for continuing the online method in higher education in the context of Nepal, it is reasonable to examine how both students and teachers feel about face-to-face learning that is done online. For the present study mixed method study design was adopted aimed to identify the perception of faculties and students at the university level towards online learning. Only 119 students and 44 faculties of the Tribhuvan University of Nepal were surveyed based on accidental sampling through the questionnaire. Only 6students and 6 faculties were included in the focus group discussion. The perception of students and faculties was identified through the Likert-type scale and focus group discussion. Faculties have experienced stressful, painful, transformative experiences toward online learning. However, students have experienced effective, enjoyable experiences except for connectivity problems and less interactive environments towards online learning. From the comparative perspective, the perception of faculty members includes the lack of students’ participation in the learning process, inactivity and difficulty to expose on the screen for a long time.
Numerical investigation of the water entry of cylinders without and with spin

We consider the water entry of horizontal cylinders with vertical impact velocity, either kept constant or freely falling, without and with spin, into quiescent water under the effect of gravity. We focus on the flow and cavity forming stages with non-dimensional submergence time $t$ , Froude numbers $Fr$ , spin ratios $\unicode[STIX]{x1D6FC}$ and mass ratios $m$ , all of $O(1)$ . We develop numerical simulations using a modified smoothed particle hydrodynamics method to obtain predictions for the impact kinematics and dynamics. These are in detailed agreement with available experiments. We elucidate the evolutions of the free surface, contact point positions, flow field, forces and trajectories and their dependence on $Fr$ , $\unicode[STIX]{x1D6FC}$ and $m$ . We define and quantify the contact point location $\unicode[STIX]{x1D703}(t)$ as a function of $Fr$ , clarifying the qualitative difference between sub- and supercritical $Fr$ and the observed absence of air-entrained trailing cavities at low $Fr$ . By subtracting the buoyancy associated with $\unicode[STIX]{x1D703}(t)$ , we show that, unlike the total drag, the remaining dynamic components are qualitatively similar for all $Fr$ . For a freely falling cylinder, we show that the total drag can be predicted from the constant velocity case with the same instantaneous velocity, providing a simple way to predict its trajectory based on the latter. The presence of spin results in lift, even when the asymmetry in $\unicode[STIX]{x1D703}$ is small. For fixed $\unicode[STIX]{x1D6FC}$ , lift increases with subcritical $Fr$ . For a freely falling cylinder, the lateral motion causes an appreciable asymmetry in $\unicode[STIX]{x1D703}$ and a reduction in lift.
Douglas-Rachford splitting and ADMM for nonconvex optimization: new convergence results and accelerated versions

Although originally designed and analyzed for convex problems, the alternating direction method of multipliers (ADMM) and its close relatives, Douglas-Rachford Splitting (DRS) and Peaceman-Rachford Splitting (PRS), have been observed to perform remarkably well when applied to certain classes of structured nonconvex optimization problems. However, partial global convergence results in the nonconvex setting have only recently emerged. The goal of this work is two-fold: first, we show how the Douglas-Rachford Envelope (DRE), introduced by the authors in 2014, can be employed to devise global convergence guarantees for ADMM, DRS, PRS applied to nonconvex problems under less restrictive conditions, larger prox-stepsizes and over-relaxation parameters than what was previously known. Other than greatly simplify the theory, exploiting properties of the DRE we propose new globally convergent algorithmic schemes that greatly accelerates convergence. For instance, the new schemes can accommodate for quasi-Newton directions such as (L-) BFGS or extend extrapolation steps \`a-la-Nesterov to fully nonconvex problems.
The Impact of Electronic Customer Relationship Management on Customer Satisfaction in Turkey

The main purpose of this thesis is to examine the impacts of E-CRM features on customer satisfaction and perceived usefulness as a mediator variable within ecommerce sector of Turkey. Having 46 mln of internet users promises bright future to Turkey’s e-commerce businesses. Overall, e-commerce businesses are operating in constantly changing environment and trends, thus E-CRM features may serve not only as a way for maintaining stable relationship with customers, but a tool that may provide competitive advantage in order to keep customers satisfied and loyal. Within the framework of this study quantitative research methods were applied. Primary data from 210 respondents has been collected through self-administered, Likert type online survey. Research model constructs were evaluated and analyzed with a help of confirmatory factor analysis (CFA) and structural equational model (SEM) processed in statistical software. The findings of the study indicated that E-CRM features represented in current research (complaint handling, communication, information content, security and privacy) demonstrate both direct and indirect impact (through perceived usefulness variable) on customer satisfaction. Additionally, the results of this study should alert eretailers that do not have strong E-CRM framework, to rethink existing strategies and reconsider available E-CRM approach for further improvements.
Inhibition Effect of Spatial Attention on Surroundings in Discrimination Task

The cue size effect which is characterized as a reverse relationship between the cue size and the detection speed is regarded as a proof for adjustment of the attentional field according to the cue size. This effect was mainly found in detection experiments. As several studies have shown that cues had discrepant effects in visual detection and discrimination task, itâ€™s natural to ask whether cue size effect still exist in discrimination task. The probability exists that the influence of cue size differ in these two basic types of behavioral tasks. To solve the problem, we assessed the interference of surrounding ring on discrimination at its center with a multiple cuing design. The results show that in distributed neutral condition when no single place gains overwhelming advantage from attention, ring size has a positive correlation with the discrimination speed. However, when the exact place of impending target is cued and spatial attention takes effect, variance in surrounding ring size makes no difference on discrimination. This type of result pattern doesnâ€™t depend on what is discriminated (orientation or brightness) or how the spatial attention is induced (by peripheral ring cue or central symbolic line cue). Our findings add evidence to the view that detection and discrimination should not be considered as the same. We also propose that with sufficient time, attention suppresses the impact from the vicinity.
Developing a Conceptual Framework for an Age-Friendly Health System: A Scoping Review

Background: Population aging is usually associated with increased health care needs. Developing an age-friendly health system with special features, structure, and functions to meet the special needs of older people and improving their health status and quality of life is essential. This study aimed to develop a conceptual framework for an age-friendly health system, which would offer a conceptual basis for providing the best possible care for older people in health system to let them experience a successful, healthy, and active aging. Methods: A scoping review was used to design the conceptual framework based on Arksey and O’Malley’s model, including six stages, with the final stage of using expert’s opinions to improve and validate the initial framework. The health system model of Van Olmen, was selected as the baseline model for this framework. Then, by reviewing the available evidence, the characteristics of an age-friendly health system were extracted and incorporated in the baseline mode. Results: Using the electronic searching, initially 12 316 documents were identified, of which 140 studies were selected and included in this review study. The relevant data were extracted from the 140 studies by two reviewers independently. Most studies were conducted in 2016-2020, and mostly were from United States (33.6%). To have an age-friendly health system, interventions and changes should be performed in functions, components and objectives of health systems. This system aims to provide evidence-based care through trained workforces and involves older people and their families in health policy-makings. Its consequences include better health acre for older people, with fewer healthcare-related harms, greater care satisfaction and increased use of cost-effective health services. Conclusion: To meet the needs of older people, health systems should make interventions in their functions for better performance. In line with these changes, other parts of society should work in harmony and set the health of older people as a top priority to ensure they can have a successful aging.
Strategic Monte Carlo and Variational Methods in Statistical Data Assimilation for Nonlinear Dynamical Systems

Author(s): Shirman, Aleksandra | Advisor(s): Abarbanel, Henry D. I. | Abstract: Data Assimilation (DA) is a method through which information is extracted from measured quantities and with the help of a mathematical model is transferred through a probability distribution to unknown or unmeasured states and parameters characterizing the system of study. With an estimate of the model paramters, quantitative predictions may be made and compared to subsequent data. Many recent DA efforts rely on an probability distribution optimization that locates the most probable state and parameter values given a set of data. The procedure developed and demonstrated here extends the optimization by appending a biased random walk around the states and parameters of high probability to generate an estimate of the structure in state space of the probability density function (PDF). The estimate of the structure of the PDF will facilitate more accurate estimates of expectation values of means, standard deviations and higher moments of states and parameters that characterize the behavior of the system of study. The ability to calculate these expectation values will allow for an error bar or tolerance interval to be attached to each estimated state or parameter, in turn giving significance to any results generated. The estimation method’s merits will be demonstrated on a simulated well known chaotic system, the Lorenz 96 system, and on a toy model of a neuron. In both situations the model system provides unique challenges for estimation: In chaotic systems any small error in estimation generates extremely large prediction errors while in neurons only one of the (at minimum) four dynamical variables can be measured leading to a small amount of data with which to work. This thesis will conclude with an exploration of the equivalence of machine learning and the formulation of statistical DA. The application of previous DA methods are demonstrated on the classic machine learning problem: the characterization of handwritten images from the MNIST data set. The results of this work are used to validate common assumptions in machine learning work such as the dependence of the quality of results on the amount of data presented and the size of the network used. Finally DA is proposed as a method through which to discern an `ideal' network size for a set of given data which optimizes predictive capabilities while minimizing computational costs.
Investigation of Induced Overvoltages on DC Cables of PV System Subjected to Lightning Strikes Using FDTD Method

Due to installation of photovoltaic (PV) panels in outdoor areas, they are subjected to lightning strikes which may cause degradation or complete damage, resulting in service interruption and added cost. Hence, it is crucial to provide an efficient and effective lightning protection system which requires some considerations to increase its efficiency and reduce its cost. Any damage to the PV system inverter causes service interruption, so it is important to analyze the transient voltages induced on the dc cables connected to the inverter during lightning strikes. In this article, the induced voltages on dc cables are calculated using the finite difference time domain (FDTD) method, which introduces high accuracy in transient studies. Important factors affecting the transient voltages on dc cables are studied, including the effect of lightning strike polarity and changing the separation distance between the PV panel and the isolated protection system. Moreover, the effect of modifying dc cable arrangement is studied, in addition to the impact of concrete foundation presence in the grounding system. Then, some recommendations are proposed which succeeded in reducing transient voltages on dc cables. In addition, the induced voltages calculated by FDTD are compared with those obtained by partial element equivalent circuit method.
Significant Subgraph Mining with Representative Set

Objectives: To mine significant subgraphs with user specified objective functions from a set of graphs that are useful for understanding the intrinsic characteristics of data in a scalable approach. Methods/Statistical Analysis: A large number of candidate subgraphs generated during mining process causes both computational and statistical problem. In this paper, Significant SubGraph Mining-SSGM proposes an algorithm to find significant subgraphs by using a small set of representative patterns - coreset that overcomes these problems. Furthermore, an edge graph notation is used to represent a graph that enables to mine patterns directly without using separate mining algorithm. Findings: The number of possible candidates is generally exponential in search space and techniques employed are mostly focussed on monotonic property. The proposed algorithm offers simple, yet efficient optimizations to significantly improve performance by pruning the search space and exploring representative graphs. It avoids enumeration of all frequent subgraphs which cause redundancy and extreme mining time. The identified coreset elements are extended that provide optimal solution patterns and adopted edge graph notation mine subgraphs directly. Application/Improvements: Experimental results shows that the proposed algorithm is effective and efficient for mining significant subgraphs in terms of computational cost, scalability and time over existing methods. The algorithm can be applied to find different types of significant patterns in a scalable manner by using any objective function according to the problem domain including support, correlation measure and feature set.
The Perception of Older Adults Regarding Socio-Political Issues Disseminated on Social Networks

Research on the relationship between seniors and social networks has focused mainly on the difficulties experienced by this group in accessing the internet. However, it has not examined other aspects such as participation by older adults in socio-political discourse. Although articles have been written on specific topics related to this issue, such studies are not enough. This research aims to analyse the perception of people over 60 years of age regarding the use of social networks as a channel for staying informed and participating in socio-political discourse that takes place on social media. To achieve this objective, four focus groups were conducted in July of 2022. In assessing the results, the transcripts were examined using qualitative-inductive content analysis and reinforced with topic analysis to identify shared perceptions. The co-occurrence evaluation reveals a strong relationship between negative perceptions and concepts such as tension and fake news. Positive perceptions are associated with the ease of interaction with other users and the potential for obtaining information. Differences have also been observed among social networks such as Facebook and WhatsApp, which appear to be the networks of choice for sharing information and opinions on socio-political issues.
Smartphone-based pathogen detection using simultaneous monitoring of resonant frequency and optical fluorescence

Recently, the use of a Quartz Crystal Microbalance (QCM) as a biosensor for detecting foodborne pathogens by observing changes in resonant frequency has gained popularity. However, conventional detection methods are time-consuming and require expensive equipment and trained personnel. The current trend is toward detection approaches that are quick, portable, and easy to use. In order to address this need, a dual-modality QCM system combining a smartphone, an in-situ fluorescence imaging subsystem, and a flow injection component has been proposed. This system enables a smartphone to receive real-time frequency data via Bluetooth, while a camera detects the presence of bacteria on the quartz crystal surface using a fluorescence-tagged antibody. The fluorescence imaging subsystem utilizes a camera to capture the bacteria fluorescence signal, while the flow injection subsystem employs a mini peristaltic pump and controller to introduce biochemical solutions, antibodies, and bacteria. All components are contained in a 3D cartridge that is portable. FITC images were captured with 5 MHz quartz crystals when the prototype system was tested. The developed QCM biosensor has the potential to become a portable bacteria detection approach that outperforms existing techniques.
Integration of Virtual Objects into an Image from a Single View : Geometrical Properties of Images and Panoramas

The aim of this work is the study of the integration of virtual objects into single views. The use of single views in augmented reality is much more flexible for the end-user than stereo pairs of images, because they require less intervention and preparation. A fundamental problem of augmented reality is to accurately recover the needed camera parameters. While this problem is well-known when two views are available, specific algorithms must be considered for the case of single views. These algorithms generally make use of the geometric particularities of the scene. The camera model and its geometry are therefore of great importance, and are largely analyzed in this report. The first task is to calibrate the camera by finding its internal parameters (mainly the focal length and the principal point). One method for it is the use of vanishing points. Techniques for the recovery of vanishing points in an image are presented, as well as known calibration methods based on it. Another way to calibrate the camera is to use planar structures, and an algorithm based on multiple planes is derived. The special case of panorama is also discussed, with details about its construction and ist calibration. The second task for the integration of virtual objects is the estimation of the camera position and orientation relative to a given coordinate system (pose). Again, vanishing points and plane-based methods are discussed. Two novel algorithms for pose estimation from multiple planes are presented. The pose can also be estimated in the case of known 3D points coordinates. Seven recent algorithms based on this assumption are detailed and compared. All the algorithms mentioned in this work have been implemented as a practical goal, and simple objects could be integrated into various images using different techniques. The report discusses the implementation particularities and presents the results obtained by comparative tests.
The temperature-dependency of the optical band gap of ZnO measured by electron energy-loss spectroscopy in a scanning transmission electron microscope

The optical band gap of ZnO has been measured as a function of temperature using Electron Energy-Loss Spectroscopy (EELS) in a (Scanning) Transmission Electron Microscope ((S)TEM) from approximately 100 K up towards 1000 K. The band gap narrowing shows a close to linear dependency for temperatures above 250 K and is accurately described by Varshni, Bose-Einstein, Passler and Manoogian-Woolley models. Additionally, the measured band gap is compared with both optical absorption measurements and photoluminescence data. STEM-EELS is here shown to be a viable technique to measure optical band gaps at elevated temperatures, with an available temperature range up to 1500 K and the benefit of superior spatial resolution.
Genetic algorithm based optimization for system of nonlinear equations

This work explores a perspective for solving system of nonlinear equations which is an eminent problem in all scientific disciplines. An evolutionary computational technique is used to handle the nonlinear system of equations by converting it into a multi-objective optimization problem. A new fitness function has been proposed, the parameters have been chosen by prior empirical analysis. To validate the performance of the proposed methodology, sensitivity analysis has been carried out by varying the parameters of Genetic algorithm. The results obtained by new approach are quite encouraging and are also compared with other existing work.
Effects of genetic and environmental variables on biofilm development dynamics in Achromobacter mucicolens

Background and Objectives: The study aimed to investigate whether Achromobacter mucicolens IA strain biofilm formation, which contributes to antibiotic resistance, could be enhanced by readily available nutrient sources like carbohydrates and environmental factors such as pH and NaCl. Additionally, the study aimed to identify any inherent genes that support biofilm formation in this strain, which is an opportunistic pathogen that affects immunocompromised patients and is resistant to many antibiotics. Materials and Methods: Biofilm growth in different carbohydrate, pH, and NaCl concentrated media was measured using crystal violet microtiter assay. All the treatments were subjected to biostatistics analysis for normality, Test of Homogeneity, one way ANOVA analysis. Whole-genome sequencing of our IA strain was conducted to identify various gene sequences. Results: Biofilm formation was measured at different carbohydrate concentrations, and the optimum biofilm formation was observed at 3M glucose and 0.5M NaCl, while the lowest results were seen at 2M maltose concentration. Whole-genome sequencing identified potential genes involved in biofilm formation, pathogenicity, protein metabolism, flagellar motility, cell wall component synthesis, and a multidrug efflux pump. Conclusion: These findings suggest that biofilm formation is influenced by extrinsic and intrinsic factors, which could aid in the development of effective treatments for resistant infections.