Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Faculty and Non-Traditional Student Perceptions of Self-Directed Learning in a Practical Nursing Program

This study was designed to identify and investigate nursing faculty and student perspectives of self-directed learning in a practical nursing program. It also explored the degree to which student's perceptions of self-directed learning exhibited factors consistent with that of critical thinking. This study is important because self-directed learning and its critical thinking elements are essential in providing safe, competent, effective, and efficient nursing care within practice and education. Using an explanatory case study design the practice of self-directed learning within a practical nursing program was examined through the use of: classroom observations, interviews with nursing faculty, nursing students, and document analysis. Data analysis within this study provided insight into the perceptions that exist between instructors and students and it demonstrated the differences between adult students that understand the role selfdirection plays within nursing education and those that do not. It can be argued that changes in the classroom interactions between instructors and students within early courses should focus more directly on enhancing learning experiences by providing deeper instruction on core elements of learning how to learn within a classroom setting. In finding that nursing students need better designed and managed opportunities to learn how to think about and care for patients, the study concludes with recommendations for future research in the field of self-directed learning within adult education.
Hungarian Maximum Matching Algorithm

The Hungarian algorithm finds a maximum independent edge set on a graph. The algorithm starts with any matching M and constructs a tree via a breadth-first search to find an augmenting path, namely a path P that starts and finishes at unmatched vertices whose first and last edges are not in M and whose edges alternate being outside and inside M. If the search succeeds, the symmetric difference of M and the edges in P yields a matching with one more edge than M. That edge is added, and then.
Joint FSO Fronthaul and Millimeter-Wave Access Link Optimization in Cloud Small Cell Networks: A Statistical-QoS Aware Approach

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.
An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People

Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things. Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on hypertext induced topic search improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.
An x‐band waveguide orthomode transducer with integrated filters

This letter presents a novel waveguide orthomode transducer (OMT) with integrated filters. Resonator‐based bandpass filters are used for the two channels forming a duplexing OMT. As an example, a two‐channel duplexing OMT is designed with channels at 9.9‐10.1 GHz and 10.9‐11.1 GHz. A good filter response with 20 dB return loss is achieved for both the channels. The upper and lower bands are isolated to −80 dB due to the filtering elements and a transmission zero. This new multifunction compact filter‐OMT design leads to the size reduction and improved inter‐band isolation. A CNC machined waveguide OMT is demonstrated and its performance is verified with excellent agreement between the electromagnetic wave (EM) simulation and measured results.
Numerical simulation of two-dimensional fluid flow problem using truly meshfree method

Received: 23 July 2018 Accepted: 28 August 2018 The solution of two-dimensional steady and transient fluid flow problem by the truly meshless local Petrov-Galerkin (MLPG) method has been addressed in the present article. The unknown function of velocity u(x) is approximated by moving least square approximant u(x). The essential boundary condition is imposed both by the direct and penalty function methods. Fourth order spline weight function, monomial basis function and a set of nonconstant coefficients are used to construct the approximants. The two-level  method is employed for temporal discretization. The results obtained by the MLPG method are compared with the analytical solution and also with the benchmark method results and found to be in the excellent agreement.
Bayesian learning of linear and nonlinear acoustic system models in hands-free communication

In dieser Arbeit werden einkanalige und mehrkanalige Darstellungen fur lineare akustische Systeme sowie pseudo-mehrkanalige und Kaskadenmodelle fur die einkanalige nichtlineare akustische Echokompensation im Frequenzbereich vorgestellt. Die vorgestellten Algorithmen zur Echokompensation werden mit Hilfe Variational-Bayes'scher Lernverfahren hergeleitet, die eine Beschreibung der Modellgrosen entweder als Zufallsvariablen oder als unbekannte deterministische Parameter ermoglichen. Die hergeleiteten Algorithmen enthalten damit einen Expectation-Schritt in dem die Posterior-Verteilung, d.h. der Mittelwert und die Kovarianz, der Zufallsgrosen geschatzt wird und einen Maximization-Schritt zur Punktschatzung der verbleibenden deterministischen Modellparameter. Ein weiterer Teil der Arbeit befasst sich mit der Erweiterung der entwickelten Algorithmen um eine Bayes'sche Postfilter-Stufe zur Unterdruckung des Restechos und zur Verbesserung des Nutzsignals des lokalen Sprechers.
Extraction of Concept and Concept Relation for Islamic Term Using Syntactic Pattern Approach

Ontology Learning is a semi automation step to learn ontology from text. The identification of a term become a prerequisite for all aspects of Ontology Learning. The Ontological Learning Layer is started by identifying terms, synonyms, concepts, hierarchical concepts, relationships and rules for various domain text including Islamic-based text or Islamic Glossary. The glossary of Islamic terms translated into English has been in abundance and requires extraction of important information for a clear understanding of an Islamic term. The existence of a list of Islamic terms is to minimize the spelling diversity, to seek the concept of the term and provide guidance for a unique Islamic concept. However, this electronic form provides a serious problem to achieve machine operation. The study aims to identify and extract concepts, taxonomies, relationships and rules that can be built on the domain of the terms in Islamic glossary specific to the field of Islamic Pillars. This extraction involves the use of Hearst pattern approach. The data set used is from the Dictionary or Islamic Glossary of the International Islamic University of Malaysia (DEED 2015). The dictionary consists of Islamic terms, which are the concepts and intentions of each concepts in alphabetical order. The study used six phases involving the preparation, processing and testing phase that are merged with the methodology design of the study. A total of 41 concepts were successfully extracted based on 6 Hearst pattern, 31 manually generated rules from 19 sentences and 9 non-taxonomic relationships. The result of the study concluded that the objective of this study was achieved in the scope determined when the results of the study and testing conducted by the assessors in the domain showed positive results. Research constraints are presented to enable researchers to improve the research from time to time. Research proposals for future research have been described so that this study will be more useful and expanded to the next comprehensive guide of Muslims.
Private Places - Private Shame: Women's Genital Body Image and Sexual Health.

Body image dissatisfaction is prevalent among women, affecting many components of the The increase in female genital cosmetic surgery indicates th h even those parts of their body considered to be private – their genitals. ained by lack of a reliable and valid instrument to m explore the relationship psychometrically testing a previously developed instrument – The Genital Self Image enital body image us women (N = 192), r women with rts (three gynecologic ty Index of .79. Through ems and component enital Confidence, IS-20 was satisfactory ve correlation between the GSIS-20 and sexual function (r = .330 .416) for all groups except women post childbirth (r = .050). In all groups there was a positive correlation between GSIS-20 scores and global body image (.412 .503). Women with prolapse ir sexual health. at women may be experiencing dissatisfaction wit Research has been constr easure genital body image. The purpose of this study was to between genital body image, global body image, and sexual health while Scale (GSIS). Lower scores on the GSIS indicate more g dissatisfaction. Four diverse groups were utilized: young, nulliparo women post childbirth (N = 56), women with pelvic organ prolapse (N = 47) and olde out prolapse (N = 45). Content validity was determined using a panel of five expe surgeons and two sex therapists) resulting in a Content Validi item reduction and content validity testing the GSIS was reduced to 20 it renamed the GSIS-20. Factor analysis was performed using principal analysis with oblique rotation revealing four factors, identified as G Appeal, Function and Comfort. Internal consistency for the GS across samples (.79 .89) as was test-retest reliability (r = .883). There was a positi
Effects of side slope on wheelchair performance.

: Compensation for the downhill turning moment of a wheelchair on a 2-degree side slope results in retarding force approximately equal to the rolling drag of a wheelchair on a level surface. The total drag force on the wheelchair while transversing a sloping surface is, therefore, roughly double the rolling drag. In contrast, the net energy cost of propulsion on this side slope is only 30 percent greater than for a level surface. Side slope propulsion is managed by "dragging" the uphill rim while pushing the downhill rim. Although this results in increased mechanical efficiency through greater use of a smaller muscle mass, it is more difficult and tiring for the wheelchair user.
Regime‐dependent statistical post‐processing of ensemble forecasts

A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post‐processing. Post‐processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post‐processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz ‘96 system, ensemble forecasts are subjected to well‐known post‐processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post‐processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.
Exploring the characteristics of tourism industry by analyzing consumer review contents from social media: a case study of Bamako, Mali

ABSTRACT In this Web 2.0 era, various and massive tourist experiences and reviews presented on social networks have become important information for tourism research. In this paper, we apply social media to explore and study the tourism industry of Bamako, Mali. Over 2000 reviewers and their comments about Bamako’s hotels and restaurants from TripAdvisor and Facebook were collected. Also, we integrate official tourism statistic data and field surveying data into the online review dataset. Data mining and statistic method are used to analyze the data for purpose of exploring the characteristics about tourism industry in Bamako. And we find that: (i) Most tourists are coming to Bamako for business purpose, and they incline to choose the hotels with better service and security condition; (ii) Comments on social media would greatly affect travelers’ choice on hotels; (iii) Most travelers are satisfied about Bamako’s accommodation services.
New Particle Formation: A Review of Ground-Based Observations at Mountain Research Stations

New particle formation (NPF) was predicted to contribute to a major fraction of free tropospheric particle number and cloud condensation nuclei (CCN) concentrations by global models. At high altitudes, pre-existing particle concentrations are low, leading to limited condensational sinks for nucleation precursor gases, and temperatures are cooler compared to lower altitudes, whereas radiation is higher. These factors would all be in favor of nucleation to occur with an enhanced frequency at high altitudes. In the present work, long term data from six altitude stations (and four continents) at various altitudes (from 1465 to 5240 m a.s.l) were used to derive statistically relevant NPF features (frequency, formation rates, and growth rates) and seasonal variability. The combined information together with literature data showed that the frequencies of NPF events at the two Southern hemisphere (SH) stations are some of the highest reported thus far (64% and 67%, respectively). There are indications that NPF would be favored at a preferential altitude close to the interface of the free troposphere (FT) with the planetary boundary layer (PBL) and/or at the vicinity with clouds, which otherwise inhibit the occurrence of NPF. Particle formation rates are found to be lower at high altitudes than at low altitude sites, but a higher fraction of particles are formed via the charged pathway (mainly related to positive ions) compared to boundary layer (BL) sites. Low condensational sinks (CS) are not necessarily needed at high altitudes to promote the occurrence of NPF. For stations at altitudes higher than 1000 m a.s. l. , higher CSs favor NPF and are thought to be associated with precursor gases needed to initiate nucleation and early growth.
Derivation of High Voltage-Gain Step-Up DC-DC Power Converters

Abstract: This paper presents a new method to derive high voltage-gain step-up dc-dc power converters is presented in this paper. Four step-up dc-dc power converters are derived by a unique parallel-series combination of basic dc-dc power converters. The obtained high voltagegain power converters have better efficiency than the ones that can be obtained obtained by using a conventional dc-dc boost power converter. Interleaving the combined converters can be used to further reduce the input current and output voltage ripples. Simulation and measurement results are included to show the validity of the proposed concept of high voltage-gain step-up dc-dc power converters.
Research on Cloud Hard Disk Capacity Prediction Scheme Based on Time Series Model

: OpenStack is a cloud operating system, but it does not provide the predictive functionality for cloud disk usage of the virtual machines running on it. In this paper, it propose a prediction schem of virtual machine cloud hard disk capacity. First the Libvirt API is used to obtain the disk data of the virtual machine. After data processing, the ARIMA time series model is built. Then the capacity of the disk in the future is predicted. This scheme can predict the disk capacity of virtual machine, facilitate the user to expand capacity or issue an alarm, and then reduce disk failure.
A Multi-Echelon Global Supply Chain Network Design Based on Transfer-Pricing Strategy

An inimitable global supply chain would provide multinational enterprises (MNEs) competitive advantage and transfer pricing strategy to coordinate the relationship between the facilities within the global value chain. In this paper, we present a framework of multi-echelon global supply chain consisting of supplier, manufacturer and a distribution center which enables the MNEs to coordinate the facilities within the value chain based on the maximization total profits theory. An optimal model is established to describe how the transfer price affects the performance of the global supply chain. Through the numerical analysis, we find that the network of global supply chain positively affects the total performance of MNEs and transfer price strategy as a kind of coordinate method which impacts the total profit of the global supply chain.
Cross-layer design and optimization techniques in wireless multimedia sensor networks for smart cities

The future smart cities vision can be developed through leveraging the potentials of Internet of Things (IoT) and wireless sensor network (WSN) technologies. WSN is a resource constrained network where network nodes are tiny devices that are run on battery power. Diverse types of applications such as environmental and habitual monitoring, detection, and tracking, use WSNs. The invention of new network protocols, the establishment of new models for communications, and testing the available solutions in real world environment are some of the current research issues in WSNs. Main challenges in such networks include energy conservation in an efficient way, dealing with variable channel capacity, and the resource constrained nature of such networks. The design of architecture for such networks has a vital role in solving the issues to some extent, i.e., the cross layer design approach is an architectural technique that offers the interaction of different layers together to enhance the performance, minimize the energy consumption, enhance the network life time, and provide Quality of Service (QoS) in real time communications. These are some of the current areas where cross-layer design approaches are being used. This paper presents different types of cross-layer design techniques in wireless multimedia sensor networks. Using such architectural techniques, different state of the art cross-layer optimization approaches are discussed while giving the reader an insight on prominent challenges and issues along with future directions.
Analytical Method for Calculation of Cogging Torque Reduction Due to Slot Shifting in a Dual Stator Dual Rotor Permanent Magnet Machine with Semi-Closed Slots

Radial flux Dual Stator Dual Rotor Permanent Magnet (DSDRPM) machine can be considered as an exterior rotor Permanent Magnet (PM) machine kept over an interior rotor PM machine. This facilitates with a scope for optimization of the relative placement of inner and outer stator slots of the machines to achieve cogging torque minimization. This paper deals with the analytical prediction of flux density distribution in an internal and external rotor PM machines with semi-closed slots and further utilizes it to calculate the cogging torque in DSDRPM machine. An optimal angle of shift between the stator slots of the two machines has been determined to obtain a reduction in the resultant cogging torque of DSDRPM machine. The analytical results are verified with the Finite Element Analysis (FEA) results and found to be in close agreement with each other.
Stochastic Analysis of Diffusion Dual Sign Algorithm for Cyclostationary White Gaussian Inputs and Nonstationary System

This brief proposes a novel diffusion dual sign algorithm (DDSA) over networks to improve the convergence performance of the existing diffusion signed error algorithm (DSEA). The stochastic analysis of DDSA with the cyclostationary white Gaussian inputs is derived for the nonstationary system with random walk varying parameters in the presence of non-Gaussian impulsive noises. Simulation results illustrate the excellent agreement between the simulated learning curves and the predictions of derived analytical models, as well as the faster convergence speed compared with DSEA.