Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Cloud Learning based on User Acceptance using DeLone and McLean Model for Higher Education

Mobile learning has been used in the learning process in several tertiary institutions in Indonesia. However, several universities have not been able to implement mobile learning due to the limitations of computer and network infrastructure. Cloud computing is a solution for agencies that experience limitations in computer infrastructure in the form of internet-based services for their customers. This paper discusses the implementation of mobile cloud learning which is a combination of mobile learning technology and cloud computing using the renewal DeLone and McLean Model which is a successful model to measure how important the implementation of a mobile cloud learning system is. The results showed that from the F test results obtained Fcount of 13,222, then Fcount> Ftable (13,222> 3.01), then Ho was rejected and H1 was accepted. So it can be concluded Information Quality, System Quality, and Service Quality together affect the Intensity of Use.
Potential Natural Compounds for Preventing 2019-nCoV Infection

2019-nCoV, a novel coronavirus, caused the pneumonia outbreak in China and continue to expand. The host receptor for 2019-nCoV Angiotensin-converting enzyme 2 (ACE2), which is the same as the host receptor of SARS-CoV. Targeting ACE2 holds the promise for preventing 2019-nCoV infection. Chinese Medicine herbs could be a valuable pool for identifying active compounds for treating infection of 2019-nCoV. In this study, we summarize several active compounds including baicalin, Scutellarin, Hesperetin, Nicotianamine and glycyrrhizin that could have potential anti-2019-nCoV effects, and we conduct molecular docking to predict their capacity for binding ACE2, which may subsequently prevent the 2019nCoV infection. We propose that these selected compounds worth further investigation for preventing 2019-nCoV.
Where next for the NHS reforms? The case for integrated care-Ham, Imison, Goodwin, Dixon, South-The King's Fund, May 2011

This paper sets out the challenges facing the English NHS now and in the future and identi! es the reforms we believe are needed to meet these challenges. It has been written as a contribution to the listening exercise initiated by the coalition government following the announcement at the beginning of April of a pause in the parliamentary passage of the Health and Social Care Bill. It builds on The King’s Fund’s response to the health White Paper published in July 2010 (Dixon and Ham 2010), our brie! ng for the second reading debate on the Bill in the House of Commons (The King’s Fund 2011a), and our extensive programme of policy analysis and research.
SELF-ASSEMBLING MICELLES OBTAINED FROM PLLA/PEG AND PDLA/PEG BLOCK COPOLYMERS IN AQUEOUS SOLUTIONS

A series of polylactide-poly(ethylene glycol) (PLAPEG) block copolymers were synthesized by ring-opening polymerization of Lor D-lactide in the presence of monoor dihydroxyl PEG, using nontoxic zinc lactate as catalyst. Micelles were then prepared by direct dissolution of the obtained copolymers in aqueous medium without heating or using any organic solvents. Aqueous gel permeation chromatography and dynamic light scattering measurements were carried out to characterize the resulting micelles. Generally, mixed micelles containing both PLLA/PEG and PDLA/PEG copolymers appear lager and more compact compared to single ones. However, the size of mixed micelles is smaller than that of single ones which exhibit an anisotropic structure since stereocomplexation disfavors the formation of anisotropic micelles. The copolymer parameters such as structures, molar mass and PEG fraction strongly influence the formation of anisotropic micelles, and thus lead to various micellar sizes.
THE NEW ZEALAND EXPERIENCE WITH ÔNO-TAKEÕ MARINE RESERVES

Marine fisheries management is trapped by two assumptions. First, that fishing must be allowed everywhere until demonstrable problems occur. Second, that detailed scientific data on fish stocks can define and then solve these problems in some acceptable way. In fact, there is no convincing factual evidence for either assumption, and the first would prevent the operation of the second, even if the latter was true (no unconfounded controls on which to base valid analysis).
Approaches to Supply Chain Coordination: Decomposed and Decentralised Decision Making Models

Modern supply chains have shifted from hierarchical, one-dimensional supply chains and increasingly operate in a global supply network. Supply Chain Coordination (SCC) focuses on optimising operations such as supply, manufacturing and distribution in the supply network of an enterprise. The major concern in such networks is to achieve coordination without compromising the autonomy of individual units or partner organisations. This area provides ample opportunities for research. Different modelling approaches based on operational and decision-making aspects of the supply chain can be used to address SCC issues. An integrated model (IM) is one which combines the constraints and objectives of different decision making units (DMUs) into a single, huge optimisation model. Integration allows the supply chain to solve all its sub-components (DMUs) simultaneously and hence, it can guarantee feasibility across DMUs compared to solving the DMUs separately. However, such models, being large and complex, are computationally difficult to solve. The solution, if any, need not be optimal for all DMUs. Often, the independent players in the system are reluctant to share all their competitive information in public. If the players are not willing to share complete information, then an attempt at an IM is extremely difficult. Therefore, the development of alternative approaches for SCC becomes increasingly necessary. In this thesis, new approaches for SCC are proposed using decomposed and decentralised decision making models based on Lagrangian relaxation (LR) and column generation (CG) methods. This is motivated by a real-world coal mining example, which is generalised to a multi-resource constrained scheduling problem. The transition of solution approaches from integrated approaches to decomposed approaches and then to decentralised ones is presented. The industry seeks a coordination approach that can deliver quality solutions in a reasonable amount of time without compromising their autonomy and their confidential and competent information. Therefore, decentralised decision-making will be the driving force in the future for supply chain coordination. A two-party coordinated production-planning and resource-scheduling problem involving a set of independent producers (multiple mines) and a shared resource manager (rail operator) is considered. The decisions in this SC are decomposed by relaxing the resource
Classification Models for Determining Types of Academic Risk and Predicting Dropout in University Students

Academic performance is a topic studied not only to identify those students who could drop out of their studies, but also to classify them according to the type of academic risk they could find themselves. An application has been implemented that uses academic information provided by the university and generates classification models from three different algorithms: artificial neural networks, ID3 and C4.5. The models created use a set of variables and criteria for their construction and can be used to classify student desertion and more specifically to predict their type of academic risk. The performance of these models was compared to define the one that provided the best results and that will serve to make the classification of students. Decision tree algorithms, C4.5 and ID3, presented better measurements with respect to the artificial neural network. The tree generated using the C4.5 algorithm presented the best performance metrics with correctness, accuracy, and sensitivity equal to 0.83, 0.87, and 0.90 respectively. As a result of the classification to determine student desertion it was concluded, according to the model generated using the C4.5 algorithm, that the ratio of credits approved by a student to the credits that he should have taken is the variable more significant. The classification, depending on the type of academic risk, generated a tree model indicating that the number of abandoned subjects is the most significant variable. The admission scan modality through which the student entered the university did not turn out to be significant, as it does not appear in the generated decision tree.
Interactive Learning of a Dynamic Structure

We propose a general framework for interactively learning combinatorial structures, such as (binary or non-binary) classiﬁers, orderings/rankings of items, or clusterings, when the underlying structure changes over time. Inspired by Angluin’s equivalence query model, the algorithm proposes a structure in each round, and it either learns that its proposal is the true structure in this round, or it observes a speciﬁc mistake in the proposal. The feedback is correct only with probability 1 − p , and adversarially incorrect with probability p . The algorithm’s goal is to minimize its number of mistakes over the course of R rounds. Our general framework is based on a graph representation of the structures and feedback in a static environment, proposed by Emamjomeh-Zadeh and Kempe (2017). To be able to learn efﬁciently, it is sufﬁcient that there be a graph G whose nodes are the candidate structures and whose (weighted) edges capture the possible feedback, satisfying a certain natural shortest paths property. To model the evolution of the underlying structure, we consider two natural models, which we term the Shifting Target model and Drifting Target model. In the former, the true structure always belongs to a small pool of candidate structures. In the latter, the structure can change only by transitioning along the edges of a known evolution graph. In order to achieve non-trivial results, we bound the total number of times the underlying structure can change, denoted by B . We provide upper and lower bounds on the number of mistakes, which depend on the total number of changes B , the total number of structures n , and natural measures of complexity of the dynamic models: the size of the pool of candidate structures in the Shifting Target model, and the maximum degree of the evolution graph in the Drifting Target model.
Quantum Markov Semigroups of Quasi-generic Quantum Spin Systems

We prove existence of the Quantum Markov Semigroups associated with the class of generators deduced by L. Accardi and S. Kozyrev in [1] through the stochastic limit of quasi-generic quantum spin systems. We introduce new conditions of polynomial and exponential decay of the coefficients, that replace the more restrictive finite range condition used by the above mentioned authors.
Effectiveness of low-cost reminder package combined with case-based health education to improve hypertensive patients' medication adherence: a clustered randomized controlled trial

Purpose: Medication adherence (MA) is a key factor for hypertensive patients ’ blood pressure control and forgetfulness is one of the main reasons that cause medication nonadherence. If effective, low-cost reminder package (LCRP) has great potentials for large-scale promotion. Therefore, this study aims to evaluate the effectiveness of combining LCRP and health education to improve MA among hypertensive patients. Patients and methods: A clustered randomized controlled trial was performed in Beijing. A total of 518 hypertensive patients recruited from 8 community health care centers were randomized to receive LCRP combined with case-based health education or usual care. Randomization was performed at community level. Multilevel modeling was used to evaluate the study effect. Results: MA scores did not differ signi ﬁ cantly at baseline between the intervention group and the control group. The results of multilevel modeling indicated that MA scores increased more in the intervention group, and the intervention effect on MA was 0.287 (95% CI: [0.103, 0.471], P =0.002). Patients ’ systolic blood pressure (SBP) and diastolic blood pressure (DBP) were not improved (SBP: difference=0.536, 95% CI [ − 3.207, 4.278]; DBP: difference= − 0.927, 95% CI [ − 3.283, 1.428]). Conclusion: LCRP combined with case-based health education could signi ﬁ cantly improve hypertensive patients ’ MA.
Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices †

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.
Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.
Unexplained Hepatic Cirrhosis in A 7-Year-Old Girl: Rare Case With a Review of Literature

Chronic liver disease (CLD) presents as jaundice and abdominal distention. It has significant morbidity and mortality. CLD is often associated with cirrhosis of the liver and causes of CLD vary from infection to metabolic diseases. Here we present a case of a 7-years-old female who presented with abdominal distention and shortness of breath. Her liver function tests were not normal. An enlarged liver was found on ultrasound and computed tomography scan showed moderate ascites with heterogeneous nodular enhancement on the liver surface. Workup for Wilson disease, viral infections, and autoimmune causes was found to be negative. There was no evidence of metabolic diseases. Biopsy of the liver showed evidence of cirrhosis. No evidence of esophageal varices was found. Supportive management for CLD was started. The case highlights the importance of detailed workup for identification of the cause which can be difficult in a resource-limited setting.
Semi-Supervised Self-Training with Decision Trees: An Empirical Study

We consider semi-supervised learning, using a pool of unlabeled data to augment performance of a supervised learning algorithm. In particular, we consider semi-supervised learning with decision tree learners. Experiments show that standard decision tree learners do not perform well when used as “base” classifier in semi-supervised learning by self-training. We argue that this is because they provide poor probability estimates with their classifications. Decision tree as the “base” classifier in self-training faces two obstacles to producing a good ranking of instances: the first is that the sample size on the leaves is almost always small, and the second is that all instances at a leaf get the same probability. This leads to poor selection of new labeled data during self-training. In this paper, we study the effect of four improvements to the standard decision tree learners: Grafting, Reduced pruning, Naive Bayes Tree, and Laplacian Correction. Experiments show that these improvements are helpful in the selection step of self-training where the selection of most reliable predictions is done for next iteration by self-training.
Author Prop: Assisting the Creative Process with an Automated Intelligent Cognitive Prop for Writers

INTRODUCTION: Writing is a complex, metacognitive task which requires planning, execution, and evaluation of an evolving text simultaneously. OBJECTIVES: This paper presents Author Prop, a web application for writing that aims to address the need for “cognitive props” during composition. The software particularly targets students, whose relative inexperience may lead to high levels of cognitive load and low engagement during writing. METHODS: To provide a mental prop to writers, Author Prop provides topic-based decomposition of texts using intelligent highlighting. Furthermore, the software allows users to interact with topics (described as keyword lists) during periods of reflection, with the aim to ease and maintain creative thinking. RESULTS: Of the 78 surveyed users, 42% said they would use the app for writing, and 70% said they enjoyed using the app. CONCLUSION: Initial survey results from beta testing of the web application indicate encouraging reception of the software.
Cloud-Based Autonomic Computing Framework for Securing SCADA Systems

This chapter proposes an autonomic computing security framework for protecting cloud-based SCADA systems against cyber threats. Autonomic computing paradigm is based on intelligent computing that can autonomously take actions under given conditions. These technologies have been successfully applied to many problem domains requiring autonomous operations. One such area of national interest is SCADA systems that monitor critical infrastructures such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks. The SCADA systems have evolved from isolated systems into a complex, highly connected systems requiring constant availability. The migration of such systems from in-house to cloud infrastructures has gradually gained prominence. The deployments over cloud infrastructures have brought new cyber security threats, challenges and mitigation opportunities. SCADA deployment to cloud makes it imperative to adopt newer architectures and measures that can proactively and autonomously react to an impending threat.
Design of a Human Performance Evaluation Support System

This work proposes a design of human performance evaluation support system (HUPESS) to support the Integrated System Validation activity, especially, for the APR 1400 plant. The HUPESS has been designed for the license of Shin-Kori Unit 3&4. The HUPESS involves the data acquisition from test facility, measurement of variables related to the performance of plant and personnel, and statistical analysis of the data. The validation support system has the capability to support human performance evaluation during Human Factors Engineering Verification &Validation, operator training, and operator qualification test by way of collection, reproduction, and analysis of plant/operator performance data including operator events, plant events, and audiovisual recording.
Evaluation of the effects of diabetes mellitus and metformin usage on serum vitamin B12 levels in cobalamin deficient subjects

Abstract: Introduction: Although there are studies evaluating vitamin B12 levels in different patient groups, there is none encountered in Turkish adult patients with or without DM reported in the English literature.The aim of the study was to evaluate the possible additional effects of diabetes and metformin usage on serum levels of vitamin B12 in cobalamin deficient Turkish adult patients. Material and Methods: Medical records of subjects ≥18 years of age, having a vitamin B12 level <180 pg/mL were screened, consecutive 98 subjects were included in the study. Results: Among a total of 75 female and 23 male subjects with a mean age of 51.3±15.9 years and vitamin B12 level of 139.3±29.2 pg/mL, 34 had the diagnosis of type 2 diabetes mellitus and 64 had no diabetes diagnosis. Mean ages were 59.0±10.8 years for diabetics and 47.2±16.8 years for nondiabetics. Vitamin B12 levels were found to be insignificantly low in people with the diagnosis of diabetes compared to without diabetes (131.2±30.6 and 143.5±27.7 pg/mL, respectively, p=0.05). Vitamin B12 levels had no correlation with diabetes duration, presence of complications, metformin usage duration. Conclusions: In conclusion, our results demonstrated that people with diabetes had lower levels of vitamin B12 compared to nondiabetics but this fact could not solely be explained by the duration of disease, accompanying complications, metformin treatment duration. All patients with or without the diagnosis of diabetes should be encouraged for sufficient vitamin B12 intake and all possible factors that lead to deficiency should be eliminated.
Automatic Navigation Mesh Generation in Configuration Space

Since their introduction by Greg Snook [13], navigation meshes have proved to be a particularly successful approach due to their ability to represent the free space available around paths through the world (this is extremely useful because it provides the pathfinder with the information it needs to successfully avoid local obstacles). As a result, they have seen widespread use in both games themselves, and popular games engines such as Source and Unreal, and many games Figure 1: An example navigation mesh and its links: cyan = walk link; magenta = step up link; yellow = step down link.