Miracle-dz/UniFAR-FTUs huggingface dataset:
Verifying the moderating role of satisfaction on service quality and students’ accomplishment in ODL perspective

Purpose Service quality and satisfaction in the ODL setting related to students’ accomplishments (performance, loyalty and career) were reconsidered. It was aimed at exposing the moderating role of satisfaction on service quality and accomplishment. It was also of interest to scrutinize how, in what routines determinants engaged interdepended. The paper aims to discuss these issues. Design/methodology/approach This study utilized an exploratory design. It was qualitatively identified first that service quality included tangible, empathy, assurance, reliability, responsiveness and referral factors. It preceded to satisfaction (perceived from academic, operational and managerial attitudes). Satisfaction led to accomplishment. Quantitatively, service quality, satisfaction and accomplishment were identified as independent, moderating, and dependent variables, respectively. Respondents, 500 Universitas Terbuka graduates, were randomly pursued to accumulate data by a survey. Methodically, importance-performance analysis (IPA) and customer-satisfaction index (CSI) were used to figure out satisfaction and their importance degree. Nine hypotheses were developed and examined using structural-equation modeling to visualize the loading factors. Findings Replies from 163 respondents were completed. Seven of nine hypotheses were validated. It was distinguished that reliability influencing satisfaction, they were empathy, assurance and responsiveness; excluding tangible and referral. Satisfaction influenced performance, career, and loyalty. IPA-CSI analysis recognized 15 (of 21) attributes as the pillars of service quality. Originality/value Despite the qualitative framework was improperly approved by quantitative procedure, they were methodically reliable. It was supported by the fact that nine cut-off values of goodness-of-fit requirements harmonized. Additional inquiry is therefore required to tail off variances by integrating a more appropriate approach, amplifying theoretical coverage, and/or extending population/sample size.
A VOLUME-SURFACE COMPOSITE SCATTERING MODEL FOR NONLINEAR OCEAN SURFACE WITH BREAKING WAVES AND FOAM LAYERS UNDER HIGH WIND CONDITIONS

Electromagnetic scattering from the sea surface is of great significance in ocean remote sensing especially under high wind conditions. A novel volume-surface composite scattering model of nonlinear rough sea surfaces with breaking waves and foam layers under high wind conditions is presented in this study. Based on the semi-deterministic facet scattering model (SDFSM), using a ray tracing method combined with impedance equivalent edge currents (RT-IEEC) and vector radiative transfer theory (VRT), the backscattering characteristics of the sea surface with breaking waves and foam layers are investigated. The crestand static-foam coverage was introduced to determine the breaking point and foam coverage distribution. The dependence of the backscattering coefficient of the sea surface with and without breaking waves and foam layers on the incident angle, wind speed, and the polarization are discussed in detail. The results of numerical simulations are analyzed and compared with the measured data from the relevant references which verifies the validity of our volume-surface composite scattering model. The synthetic aperture radar (SAR) image simulations of the surface with and without the breaking waves and foam layers are compared, and the combined effects of the breaking waves and whitecaps are analyzed.
Postsurgical Enhancement of Fracture Repair : Biophysical Alternatives to Bone Grafting *

Surgical intervention of fracture repair is usually achieved without complication. Although bone grafting is the most common method of enhancing fracture repair, several biophysical alternatives may accelerate the natural healing process and can be used to treat complications, such as delayed or nonunion fractures. This article describes low-intensity pulsed ultrasonography, electrical stimulation, and axial dynamization. Veterinary orthopedic surgery is continually developing. There is growing interest in alternative therapies for improving and accelerating fracture healing. This article discusses the availability and possible clinical use of biophysical alternatives to bone grafting, including low-intensity pulsed ultrasonography (LIPU), electrical stimulation, and axial dynamization. THERAPEUTIC ULTRASONOGRAPHY Ultrasonography has various applications in surgery, diagnostics, and therapy. The frequency range commonly used in ultrasonography varies from 0.8 to 15.0 MHz. Surgical and physical therapy intensities range from 1 to 50 W/cm, with the aim of inducing significant tissue heating. To avoid tissue heating in diagnostic ultrasonography, much lower intensity (1 to 50 mW/cm) is employed. Recent investigations have suggested that LIPU in the diagnostic range can stimCE Vol. 23, No. 5 May 2001 423 V n Low-intensity pulsed ultrasonography can be used as a home or outpatient
A Second Thought on Estimating Expansionary Fiscal Policy Effects in the U.S

This paper revisits mixed findings of the expansionary fiscal spending effect in the U.S. An array of standard Vector-Autoregressive (VAR) models have been implemented to capture inconsistent effects of the fiscal expansion across studies. Findings in this paper consistently reveal that, first, government expenditures often generates less positive influence than government purchases; second, leaving aside the state and local government spending, federal government purchases alone have very limited influence on economy; third, 2007 recession significantly weakened the effectiveness of fiscal expansionary policy thereafter. Following these findings, this paper questions the validity of using government purchases alone to conclude the comprehensive effect of fiscal expansion.
CARTOGRAPHIC METHODS FOR VISUALIZING SEA LEVEL RISE

Increased attention to global climate change in recent years has resulted in a wide array of maps and geovisualizations that forecast the anticipated consequences of climate change, including sea level rise in coastal areas. Since inundation from sea level rise is inherently geographic in nature, there is a need for effective cartographic representations at global, regional, and local scales that depict where the inundation is expected to occur as well as the magnitude of the projected inundation. The recent increase in production of maps and geovisualizations displaying the anticipated impacts of sea level rise calls attention to numerous cartographic issues that warrant consideration for such displays, including representation of various categories of uncertainty. Using examples from analyses of sea level rise at global, regional, and local scales, we provide a general framework of cartographic issues related to sea level rise as well as a collection of specific examples that demonstrate the challenges and limitations of sea level rise maps and geovisualizations. We draw our observations on the effectiveness and limitations of these displays through informal, qualitative feedback from scientists, educators, and other map users. INTRODUCTION AND OBJECTIVES Global climate change is perhaps one of the more significant hazards confronting the global society in the foreseeable future. The International Polar Year (IPY) has elevated climate change as a major issue that has received significant exposure in the recent scientific literature as well as the popular press. One potential consequence of global climate change is a gradual rise in sea level due to the increased melting of snow and ice pack from the world’s temperate glaciers and the major ice sheets, Greenland and Antarctica. According to one estimate, global sea level would rise approximately 80 meters if the entirety of the Greenland and Antarctic ice sheets were to melt (USGS 2000). The Intergovernmental Panel on Climate Change (IPCC) (IPCC 2007) report released in 2007 provides an upper-level estimate of 26-59 cm of sea level rise across the globe over the next century. Cartographers and GIScientists have an important opportunity to contribute to the issue of global climate change through the production of maps and geovisualizations that depict the predicted consequences of sea level rise and may be used by scientists, policy makers, educators, and the general public. Given the great influence that such maps and geovisualizations may have for planning, public policy, education, and a range of other purposes, an important challenge to cartographers is the development of displays that communicate the critical issues associated with a forecasted event such as sea level rise in an effective, yet responsible manner. Despite the
Studies on the Survey Reports about Aids Risk among Drug Users

Social development is a process of consistently emerging and resolving social problems.Since the 1980s,the problems of drug abuse and aids transmission have been widely accented by Chinese,as being the crucial issues in regard to protecting public health and for maintaining social order.To cope with these problems,many Chinese investigators conducted a number of surveys with an intent to find the way against these challenges.This paper provides readers a general review on the literatures and survey reports about these issues.Established on this ground,the study also gives its comments on the current stage of research development.The purpose of this work is to provide some theoretical outline for those researchers and social workers,who carried on their fieldwork on aids prevention and the behavioral correction of drug users,rendering them a grand viewabout the general conditions of these affairs.
Informational Asymmetry Index

This research aims to propose an index of informational asymmetry and create a ranking with publicly traded firms in a market analysis that can be used in several studies in corporate finance. The proposed method consists in the construction of an algorithm based on the Elo rating and capture the perception of the analyst that choose, between two firms, the one they consider to have better information. After having two different indexes built on two distinct dates and by different analysts, it was confirmed the consistence of the method once the Spearman rank-order correlation between different combinations of indexes was higher than 70%. This index is a good proxy for use as a measure of disclosure of Brazilian firms and it will help the development of the theory and greater understanding of the impacts of disclosure on many aspects related to finance as value and cost of capital.
MEASURING INEQUALITY OF EDUCATIONAL ATTAINMENT: A SUGGESTED METHODOLOGY

This paper has shown that it is possible to have a useful measure of educational attainment that is not based on an absolute level of attainment, such as the mean years of formal education. The measure proposed here is to calculate the Gini ratio for the distribution of educational attainment, so that we will have a measure of the inequality of education within a population. The methodology involves deriving cumulative distribution functions for the degree of educational attainment in a population, and then calculating the Gini ratios based on those distributions. A sample of five countries was chosen. Two alternative sets of weights were used for the differing degrees of educational attainment. Our results showed the changing the weighting did not affect the ranking of the inequality of educational attainment of the countries included in the sample.
Adsorption of the Nafion Polymers on Mica Substrate in Water

The Nafion films from ethanol-diluted Nafion dispersions of concentration 0.5 wt% were deposited on mica substrate by spin coating. The thickness of the film was 1 ± 0.2 nm measured with surface force apparatus techniques in dry nitrogen. Due to the swelling of Nafion in water, the thickness of the film increased to 4.8 ± 1 nm. The thickness had a sudden jump-in with high load (40 mN/m) in water, possibly because a few layers of water molecules were extruded from the Nafion film. The surface topography of the Nafion film pretreated with the Nafion solution the same as that used in the SFA experiments was characterized by atomic force microscopy (AFM). The Nafion polymers formed a relatively uniform region in two-dimension on the mica substrate via spin coating. The completely repulsive force at all distances between Nafion and mica surface indicates that the surface of Nafion is negative in water.
A Survey on Internet of Things Security Issues and Applications

Internet of Things (IoT) is an emerging technology closely related to other research areas like Peer to Peer Networking, Mobile computing, Pervasive or Ubiquitous computing, Wireless Sensor Networks, Cyber Physical Systems, Real Time Analytics, etc. Technologies like ZigBee and Wi-Fi Direct can be widely deployed to achieve the notion of smart cities, eventually achieving a globally integrated smart world. However, there are ongoing issues like architecture design, hardware design, cost accountability, identity, privacy, and security issues for building new services and solutions in IoT. To resolve these issues, we can categorize them either as business-driven or research driven. Some of the research driven issues are architecture design, identity, privacy, and security. In this survey, we primarily focus on the architecture design, identity, privacy, and security issues associated with IoT. After a brief introduction on the adaptation of IoT in recent times, we discuss various architectures currently available for IoT along with the issues in identity, security and privacy. Then, various current and future applications of IoT are briefly outlined. Finally, the survey paper concludes with a brief discussion on the ongoing challenges for IoT.
RL-Based Radio Resource Slicing Strategy for Software-Defined Satellite Networks

As an attractive infrastructure, satellite networks have obvious limitations such as high complexity and lack of flexibility. The introduction of software-defined networking (SDN) and network slicing into satellite networks is a promising solution. In satellite slicing networks, dynamic allocation of radio resource is a critical and challenging issue that should maximize system utility and take into account users’ dynamic channel conditions and quality of service (QoS). In this paper, we propose a dynamic radio re-source slicing strategy called Sat-RRSlice which is based on reinforcement learning(RL) to serve the low-orbit satellite slicing networks. The strategy learns useful information from the wireless environment dynamically, then updates the number of radio resource units allocated to each slice based on the useful information dynamically. Secondly, we use the utility function as RL’s reward function which considers users’ dynamic channel conditions and QoS. Finally, this strategy avoids invalid allocation and resource waste by implementing access control. Simulation results demonstrate the effectiveness of Sat-RRSlice strategy.
Automatization in Blacklist Management

With the developing technology, there is a need for safe, fast and convenient shopping in intelligent transportation systems as in every sector. In response to this need, studies to protect mutual security and minimize violations in mobile ticketing applications have accelerated. In this paper, it is aimed to prevent frauds in the mobile ticketing by blacklisting the violated situations in line with the benefits of both the end user and the system. And this will be automated by artificial intelligence technologies, not visual inspection of the data. Thus, if the data and variables differ in the future, it will be expected to achieve the best adaptation to the new situation quickly and with minimum deviation.
Chronic Kidney Disease Prediction and Recommendation of Suitable Diet Plan by using Machine Learning

Chronic kidney disease (CKD) is a type of kidney disease in which there is gradual loss of kidney function over a period of months or years. Prediction of this disease is one of the most important problems in medical fields. So automated tool which will use machine learning techniques to determine the patient's kidney condition that will be helpful to the doctors in prediction of chronic kidney disease and hence better treatment. The proposed system extracts the features which are responsible for CKD, then machine learning process can automate the classification of the chronic kidney disease in different stages according to its severity. The objective is to use machine learning algorithm and suggest suitable diet plan for CKD patient using classification algorithm on medical test records. Diet recommendation for patient will be given according to potassium zone which is calculated using blood potassium level to slow down the progression of CKD.
Analysis of Outage Probability and Effective Security Throughput over a Noiseless Wiretap Channel

This paper analyzes the security performance based on outage probability and effective security throughput over a noiseless wiretap channel. Firstly, the outage probability is derived as a double integral function, which is simplified into a first order exponential integral function based on the nature of the noiseless eavesdropping system. And the effective security throughput is the product of transmission rate and non-outage probability. Then by considering both outage probability and effective security throughput, the system is modeled as a dual objective optimization problem, which is further transformed into a single objective optimization problem. Finally, an optimal transmission rate algorithm is proposed. Simulation results show the effects of transmission power and transmission rate on the security performance, and verify the theoretical analysis.
Forecasting Museum Visitor Behaviors Using Deep Learning

We show an application of big data in smart tourism by conducting a case study on museum visitor behaviors. Based on obtained data of entries to museums in Florence, Italy, we apply a Gated Recurrent Unit (GRU) model and a one-dimensional Convolutional Neural Network (CNN) model to analyze and forecast museum visit volume over time. Our results indicate that tourism industry can benefit from accurate forecasts with big data.
Deriving Centroids for K-means Algorithm

Clustering is a technique that groups objects together, where the set of objects in each group shares a similar characteristic. K-means is one of the well-known clustering techniques due to its simplicity, robustness, easy implementation and computational efficiency. However, one of the k-means drawbacks is that it is mainly based on the selection of initial centroids that might either lead to a perfect or poor clustering. Many methods were introduced to address this drawback, such as the k-means++. Another drawback is that both give different clusters in each run. In this thesis, we introduce a new technique called Deriving the Centroids for k- means (Dk-means) that selects specific centroids to improve clustering. The new technique is based on a linear clustering algorithm called Linear Clustering for Transactional Operations (LCTO). Our technique is tested on many datasets with different sizes and is compared to k-means and k- means++ using the Silhouette Cluster Validity Measure (SCVM).
An Improved SVM-Based Spatial Spectrum Sensing Scheme via Beamspace at Low SNRs

Most spectrum sensing algorithms mainly use the characteristics of frequency, time, and geographical dimensions to detect spectrum holes. In this paper, we propose a novel spectrum sensing scheme from the space domain by using beamspace transformation and the support vector machine technology. First, a model of beamspace transformation is proposed for the case of complex calculations in a sizeable multi-antenna system. This beamspace transformation has the ability of spatial filtering, which can not only decrease the dimension of the receive matrix but also enhance the signal to noise ratio of the received signal. Then, we employ the support vector machine classification to overcome the problems caused by the inherent threshold of traditional sensing algorithms. We only need to train the historical samples to distinguish between noise and primary user signals effectively. This classification algorithm has self-learning ability, which can adaptively adjust the classification hyperplane according to environmental changes without complex threshold calculation. Finally, simulation results show that the proposed scheme outperforms other related multi-antenna sensing algorithms, especially under low signal to noise ratio and low snapshot.
Study of the Sticking Effects during Mechanical Nano-manipulation Using Shape Memory Nanotools

Nowadays top down planar nanolithography technologies are widely used in the area of production of Nano devices for electronics and telecommunications. The new alternative paradigm of mechanical bottom-up nano-assembling of prospective nanodevices from individual nano-objects pretends to compete with it. One of the main fundamental problems in mechanical nano-manipulation is that connected with large influence of sticking force between nanogripper and object under nanomanipulation. In this report sticking forces between CNTs and Ti2NiCu nanotweezers are studied experimentally and approaches to theoretical estimation of the nature of those forces are discussed.
Security of Vehicle Platooning: A Game-Theoretic Approach

In this paper, we study the security of a vehicle platoon exposed to cyber attacks using a game-theoretic approach. The platoon topologies under investigation are directed (called predecessor following) or undirected (bidirectional) weighted graphs. The edge weights specify the quality of the communication links between the vehicles in both the unidirectional/bidirectional data transfer environments. The attacker-detector game is defined as follows. The attacker targets some vehicles in the platoon to attack and the detector deploys monitoring sensors on the vehicles. The attacker’s objective is to be as stealthy to the sensors as possible while the detector tries to place the monitoring sensors to detect the attack impact as much as it can. The existence of Nash Equilibrium (NE) strategies for this game is investigated based on which the detector can choose specific vehicles to put his sensors on and increase the security level of the system. Moreover, we study the effect of adding (or removing) communication weights between vehicles on the game value. The simulation and experimental results conducted on a vehicle platoon setup using Robotic Operating System (ROS) demonstrate the effectiveness of our analyses.
Joint Resource Allocation Algorithm in Relay-Enhanced D2D Communication Networks

High expectation is given to the device-to-device (D2D) communication to improve the system capacity in cellular networks. But, the interference among specturm-sharing links is a major obstacle to fulfill the potential of D2D communication. To the end, we focus on joint optimization of mode selection, channel allocation, and power control in a direct and relay D2D communication coexisted scenario. The paper proposed a novel resource allocation scheme aiming at minimizing overall energy consumption, subject to the quality-of-service (QoS) requirements of cellular users (CUs) and D2D links. In order to solve the formulated non-convex combinatorial optimization problem, a three-stage scheme is proposed. Specifically, the joint mode selection and relay selection (JMSRS) algorithm is proposed to select communication modes for D2D links firstly. Then, the relay enhanced channel assignment (RCA) algorithm is used to determine the set of reused channels of each D2D link. Finally, the relay-enhanced power control (RPC) is devised to coordinate the transmit power of the BS, relays, and D2D transmitters to alleviate the interference. The simulation results demonstrate that the proposed scheme can significantly improve the system performance in terms of data rate and energy consumption, while guaranteeing diverse QoS requirements of the CUs and D2D links.