Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Transplant Education for Recipients to Increase Pursuit of Living Donation

improve patients’ health and medication compliance, increase satisfaction with medical care, and reduce healthcare costs. For renal patients, particularly, health education reduces use of emergency dialysis and increases patients’ pursuit of self-care dialysis options and transplantation. Established transplant guidelines recommend that all potential kidney transplant recipients and living donors be educated about the different transplant options, the surgical process, and the medical risks involved. However, because a considerable percentage of transplanteligible renal patients, especially racial minorities, are not coming to transplant centers for evaluation or are not completing their transplant evaluation, current transplant education may be inadequate. Racial disparities in access to care, economic disincentives for dialysis centers to promote transplantation, and lack of funding for transplant education can impede some renal patients from receiving comprehensive information. Education about living donation is particularly complex, requiring discussion about how transplantation will affect both the recipient and the living donor. In light of the increasing prevalence of chronic kidney disease, the long waiting times for deceased donor transplants, and the higher graft survival rates of living donor kidneys, more patients also have begun to consider living donation. However, recipients, even those with living donor volunteers, are extremely concerned about harming the health of or pressuring living donors. Some patients may rule out the option of living donation before they understand how long they may have to wait on the waiting list, what steps are taken by transplant centers to protect donors’ health, and the medical advantages to receiving a living donor transplant compared with other treatments. Successful transplant education interventions involve both prospective recipients and their available donors, are tailored for ethnic minorities, use videos and print materials, provide education in recipients’ Optimal transplant education for recipients to increase pursuit of living donation
Content Determination for Chess as a Source for Suspenseful Narratives

In this paper, we explore a way of viewing a chess game as a narrative. Using the model of suspense described in Doust (2015), Doust and Piwek (2017) and Doust (2017), we derive a ﬂuctuating step-by-step prediction of the suspense level evoked by a series of steps in a particular chess endgame scenario. Based on these predictions, particular variations from a given set-up of the game may be chosen to induce a story for the game that optimises the value of the suspense perceived by an observer following the narrative that results from the game. This provides a content determination strategy for a system that constructs chess games speciﬁcally to support suspenseful stories.
Multi-tenant database framework validation and implementation into an expert system

Expert Systems (ES) is a branch of Artificial Intelligence (AI) that makes extensive use of specialized knowledge to solve problems at the level of a human expert. It uses a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice. These systems represent a programming methodology by which a computer can be instructed to perform tasks that were previously considered to require the intelligence of a human expert. The development of the Multi-Tenant Database (MTD) adoption framework involved the accumulation of extensive specialised knowledge of experts, hence there is a need for this to be implemented in an ES. This paper presents a forward chaining method used in the implementing of the MTD framework into an expert system. A free web-based expert system shell called ES-BUILDER was adopted. The framework was validated via a survey and analysed with the aid of SPSS software. The findings obtained from the validation procedure indicate that the framework is valuable and suitable for use in practice since the research shows that the majority of respondents accepted the research findings and recommendations for success. Likewise, the ES was also validated using a survey with the majority of participants accepting it and embraces the high level of its usability.
Confidence Level for Finite Blocklength Ultra Reliable Communication over Fading Channels

We present the reliability confidence level as a way to quantify reliability for ultra reliable connections in uncertain environments subject to random block-error rate fluctuations. As ultra reliability is usually linked to the low latency constraint, we analyze the reliability confidence level using the latest results on block-error rate in the finite blocklength regime. The analysis is carried out for OFDM-based systems over Rayleigh slow frequency block-fading channels. The reliability confidence level is bounded using analytic expressions which are then applied to solve two optimization problems. We first find the minimal resources (or codeword length) to guarantee a target reliability with a given confidence. We then investigate an optimal resource sharing strategy within the context of 5G New Radio. The solutions for optimization problems are obtained without resorting to cumbersome Monte-Carlo simulations. Numerical evaluations validate the relevance as well as the tightness of the proposed bounds and approximations.
Concepts Of Modern Physics

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Navigate Symbol Assisted Channel Estimation Algorithms under Various Channel Distribution

In order to reduce the effect of elements of noise, navigate symbol assisted (NSA) channel estimation (CE) algorithms based on the transform domain such as discrete Fourier transform (DFT) and discrete cosine transform (DCT)seemenchanting owing to their capacity. Here, an improved algorithm for a better channel estimation has been proposed based on expurgated discrete cosine transform (E-DCT) with additive white Gaussian noise (AWGN), Rayleigh distribution (RD) and Rician distribution (RcD) to obviate the leakage of energy using the property of symmetric and also compared with the conventional channel estimation algorithms such as Least Square (LS), DFT and mirror weighted DCT even that of E-DCT with additive white Gaussian noise (AWGN) distribution. Simulation results demonstrate that the E-DCT-RD can reduce the energy leakage more efficiently, and performed far better than the existing CE algorithms.
Improvements of Warehouse Operations to Reduce the Impact of Quality Deficiencies

In the increasingly competitive market of 3PL:s, it is important to have efficient processes limiting non value adding time as much as possible while at the same time delivering high quality services. There are a number of different quality deficiencies that may occur in the warehouse operations and DB Schenker Logistics AB wants to understand their current situation and how the impact of quality deficiencies can be reduced to make their operations more efficient. The purpose of this thesis is therefore to increase the understanding of quality deficiencies occurring in the warehouse operations and how the operations can be improved to reduce the impact of the deficiencies. Three of Schenker’s larger customers were the focus for the thesis. To be able to fulfil the purpose, five different data collection methods were used which were interviews, observations, time studies, surveys and data from Schenker’s databases. The results from the data collection were the identification and quantification of the time consumption for managing a number of different quality deficiencies. Based on the quantification, suggestions for how to reduce the impact of the deficiencies with the largest impact were presented. The main areas which Schenker can work with and study further are picking errors and their consequences, transportation and the education of new employees.
Effect on keyboard-based English word acquisition

ABSTRACT Recent years, dramatic changes in the mode of writing have occurred. Computers and other digital devices are increasingly replacing writing by hand. The sensory-motor experiences of typing (e.g. visual, haptic, motor) are different from those used in handwriting. Therefore, the influence and effect of keyboarding on linguistic performance, compared to handwriting or letter tiles, has seen much inquiry. The results of these studies have been mixed. In this study, researchers used grounded theory to explore the experiences and perceptions of keyboard-based English word acquisition learners. The participants came from various ages and backgrounds (30 English training institute kids, 46 elementary school students, 7 college students, 14 in-service teachers), as well as 10 parents of lower age learners (N = 107). Audio of the interviews was recorded digitally, transcribed verbatim and imported into NVivo 11.0 for coding and analysis. Overall, the participants confirmed the effect of keyboarding on English word learning, and a majority expressed positive attitudes towards keyboarding. Participants also listed which aspects of keyboarding interested them, such as improved learning efficiency, engaging learning activities or other incidental benefits.
Experimental analysis of vectorized instructions impact on energy and power consumption under thermal design power constraints

Vectorized instructions were introduced to improve the performance of applications. However, they come with an increase in the power consumption cost. As a consequence, processors are designed to limit the frequency of the processors when such instructions are used in order to maintain the thermal design power. In this paper, we study and compare the impact of thermal design power and SIMD instructions on performance, power and energy consumption of processors and memory. The study is performed on three different architectures providing different characteristics and four applications with different profiles (including one application with different phases, each phase having a different profile). The study shows that, because of processor frequency, performance and power consumption are strongly related under thermal design power. It also shows that AVX512 has unexpected behavior regarding processor power consumption, while DRAM power consumption is impacted by SIMD instructions because of the generated memory throughput.
FITness Assessment - Hardware Algorithm Safety Validation

—Error Correction Codes (ECC) are important safety methods for digital data to gain control of Single Event Upsets (SEU) in integrated digital circuits. SEU are responsible for single bit ﬂips inside a digital circuit caused by ionizing radiation. This effect does not affect the physical structure of the components but the correctness of data inside ﬂip ﬂops. Consequently, data gets corrupted and the correct program ﬂow gets disturbed. This effect needs to be considered especially for safety-critical systems. In the new ISO 26262 2 nd Edition, the automotive domain suggests controlling SEU effects by algorithms that correct Single Bit Errors and Detect Double Bit Errors (SEC-DED). This raises the question what kind of impact Double Bit Error Correction (DEC) will have on the overall safety level for LiDAR (Light Detection and Ranging) systems. In this publication, we determine the difference between two ECC algorithms from a safety point of view: Hamming’s code (SEC-DED) and Bose–Chaudhuri–Hocquenghem-Code (DEC). For this purpose, we developed a novel method for algorithm safety validation and applied it to both algorithms.
An Approach to the Assessment of Transformational Leadership Competencies among Undergraduate Students

Faculty members from a higher education institution in northwestern Mexico created the FORTES program, designed to develop leadership competencies among undergraduate students and thus contribute to their future success in life. To address the need to assess the FORTES program, educators asked the students enrolled in the program to respond to the Student Leadership Practices Inventory (IPL-E). The results of the survey suggest that the leadership competencies of the students in the program are consistent with a transformational leadership model. This type of effort holds some promise to help narrow the gender gap in leadership education. The main contribution of this study is to provide educators involved in student leadership development
Interpreting faces with neurally inspired generative models

Becoming a face expert takes years of learning and development. Many research programs are devoted to studying face perception, particularly given its prerequisite role in social interaction, yet its fundamental neural operations are poorly understood. One reason is that there are many possible explanations for a change in facial appearance, such as lighting, expression, or identity. Despite general agreement that the brain extracts multiple layers of feature detectors arranged into hierarchies to interpret causes of sensory information, very little work has been done to develop computational models of these processes, especially for complex stimuli like faces. The studies presented in this thesis used nonlinear generative models developed within machine learning to solve several face perception problems. Applying a deep hierarchical neural network, we showed that it is possible to learn representations capable of perceiving facial actions, expressions, and identities, better than similar non-hierarchical architectures. We then demonstrated that a generative architecture can be used to interpret high-level neural activity by synthesizing images in a top-down pass. Using this approach we showed that deep layers of a network can be activated to generate faces corresponding to particular categories. To facilitate training models to learn rich and varied facial features, we introduced a new expression database with the largest number of labeled faces collected to date. We found that a model trained on these images learned to recognize expressions comparably to human observers. Next we considered models trained on pairs of images, making it possible to learn how faces change appearance to take on different expressions. Modeling higher-order associations between images allowed us to efficiently match images of the same type according to a learned pairwise similarity measure. These models performed well on several tasks, including matching expressions and identities, and demonstrated performance superior to competing models. In sum, these studies showed that neural networks that extract highly nonlinear features from images using architectures inspired by the brain can solve difficult face perception tasks with minimal guidance by human experts.
Practicing multiliteracies to enhance EFL learners’ meaning making process and language development: a multimodal Problem-based approach

Abstract Research has shown that a multiliteracies pedagogy creates more chance for students to meaningfully read and write to develop their critical perspective on cultural issues. This study experimented such a practice in an EFL college context applying a problem-based approach. Sophomore English majors were engaged in recursive reading and writing processes to relearn Taiwan through orchestrating the collected multimodal resources and creating a multimodal text so as to awaken the target audience’s interest. Findings from both corpus and qualitative analyses revealed that (1) students developed language richness in vocabulary use, sentential complexity, and overall expressive fluency through the practice and (2) the interactive and recursive reading-writing processes had a great impact on the design of the project with multimodal materials, which exerted intertextuality among texts and enhanced the quality of the final project. By supporting with a meticulous scaffolding mechanism, such a multiliteracies project indeed encouraged more autonomous learning among students who experienced a full authorship during the process and finally created works that were authentic and unique.
Pengembangan Pembelajaran Blended Learning Pada Generasi Z

Every generation has beliefs, values, cultures, perspectives, interests, and different skills for life and work. The generation born in the early 2000s when the rapid development of such technology referred to as generation-z or net generation. Characters of this generation is very sensitive to technology and communication, meaning they have an advantage in the field of information and knowledge development. While the educators who were born in an earlier era are still not familiar with it so that educators often claimed to be "clueless" (stuttering technology). To address this need no new innovations in the learning process so that it complies with these characters. Combines conventional learning with communication media such as whatsapp and google drive is one easy solution social studies lesson on the generation-z. Learners who are accustomed to communicate using social networks can access the material and lesson plans that have been prepared with structured each meeting. So that they can read or prepare questions before the learning begins. The proportion of the use of e-learning in this study reached 35% so that it can be summed up as learning blended learning. This learning to stand on its information technology infrastructure and can be done anytime and anywhere. So learning blended learning has characteristics that are open, flexible, and can occur anywhere. Keywords: Generation Z and blended learning http://dx.doi. org/10.17977/um022v1i12016p070
Feedback Equilibria in Differential Games with Structural and Modal Uncertainties

In this study we introduce a general definition of an equilibrium concept (called "strong equilibrium") for both discrete-and continuous-time dynamic games and under varying (symmetrical and asymmetrical) modes of play. The underlying Pystem is stochastic, with structural and modal uncertainties determined by a finite state jump process. The new equilibri~~m concept encompasses both the feedback Nash and feedback Stackelberg solution concepts for the special cases of deterministic discrete-time games with syn~metrical and asymmetrical modes of play. respectively: and it also provides a convenient framework for the introduction of a feedback Stack-elberg solution concept in deterministic differential games. For the general class of stochastic nonzero-sum games with struct~~ral and modal uncer-TAMER BASAR and ALAlN HAURIE tainties, and under the feedback closed-loop information, we obtain the optimality conditions in both discrete and continuous time. Certain special cases are also studied, and the intrinsic relationship between information patterns and possible definitions of value in nonzero-sum differential games is clarified.
Identifying Key Classes Using h-Index and its Variants

When maintaining an unfamiliar software system,software engineers usually need to consume much time to understand the architecture and function of the system.In order to save their efforts,this paper proposes a light-weight approach to automatically identify key classes from source codes using h-index and its invariants.Software engineers can begin to understand the architecture and function of the system from the key classes identified by the proposed approach.To this end,for a given system,this paper first constructs a dependence graph to represent the dependence relationships among the classes in the system.Then,it computes h-index and its invariants based on this graph to measure the importance of classes.The experimental results in two open source software systems show that the approach can quickly identify key classes while maintaining the accuracy of identification compared to ex-isting work.
Expanding usability testing to evaluate complex systems

When you think of usability testing, do you think about working with someone for an hour or two, watching and listening as they do a series of short, discrete scenarios where there is a clear ending or a correct answer for each scenario? For most of us that's a typical usability test. It's the type of usability test assumed by the Common Industry Format (ISO/IEC 25062, see http://zing.ncsl.nist.gov/iusr/). It's the type being discussed for the CIF-Formative (Theofanos and Quesenbery, 2005). It's the type that the various CUE studies have focused on (see http://www.dialogdesign.dk/cue.html).
Exploring EEG based Authentication for Imaginary and Non-imaginary tasks using Power Spectral Density Method

Biometric technology has swiftly emerged as a go-to solution for improving cyber security especially in financial fraud and security threats. EEG based-authentication is best of security in cyber security application as it is unique and cannot be replicated. The aim of this study is to investigate the possibility of adopting imaginary or non-imaginary task for human authentication. In this study, twenty subjects were recruited from undergraduate students with age ranging from 19 to 30 years old. The subject must be healthy and right-handed. The subjects were required to perform non-imaginary task (left hand or right hand movement) and imaginary task (just need to imagine the movement of left hand or right hand). Duration for each task is 1 minute and take rest for 1 minute between the tasks. Truscan EEG device (Deymed Diagnostic, Alien Technic, Czech Republic) with 19 channels were used to collect EEG data with 1024 Hz frequency sampling and the impedance is kept below 5 kOhm. Bandpass filter was used in pre-processing to extract alpha (8-13Hz) and beta (14-30Hz) waves. The signal was segmented and the power spectral density were calculated by Welch’s method and Burg’s method. The statistical features (mean, median, mode, variance, standard deviation, minimum and maximum) were obtained from PSD were used as input of classifier. K-nearest neighbour classifier (KNN) and Linear Discriminant Analysis (LDA) were applied for classification. In conclusion, Welch method gives the highest classification accuracy which is 98% for beta waves from channel C4 with the K-nearest neighbour classifier. Imaginary task shows the higher classification accuracy which is 98.03% instead of non-imaginary task which is 94.95%. Thus, imaginary task is more suitable for authentication.