Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of variety-seeking intention by mobile phone usage on university students’ academic performance

Abstract The purpose of this study is to explore the effects of variety-seeking (VS) intention arising from mobile phone usage on students’ academic performance (AP). The study identified how students are affected by mobile phone usage, revealing in particular an increased social networking-usage tendency among younger students and how students are continually enthralled by and attracted to VS, which affects their AP. Data were collected from five major universities in Bangladesh via a questionnaire (311 valid responses from 322 completed questionnaires). The methodology adopted was based on multiple theoretical frameworks including technology acceptance model, information systems (ISs) continuance theory, social cognitive theory, and ISs success model. Results from structural equation modelling revealed that VS tendency, arising from mobile phone usage, has a strong positive relationship with AP. The study also highlights theoretical and practical implications for university students, academicians, education managers, and educational policymakers.
Role of the C-Type Lectin Receptors MCL and DCIR in Experimental Colitis

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract. Though its exact etiology is still unclear, it is proposed that an imbalance in the intestinal homeostasis leads to a disturbed interaction between commensal microbiota and the mucosal immune system. Previous studies have shown that both innate and adaptive immunity are involved in an overwhelming colon inflammation, and thus contribute to the pathogenesis of IBD. In innate immunity, several pattern recognition receptors such as Toll-like receptors, NOD-like receptors or C-type lectin receptors (CLRs) are involved in IBD pathogenesis. Myeloid CLRs are mainly expressed by antigen-presenting cells and bind to glycan structures present on self or foreign antigens. The Macrophage-restricted C-type lectin (MCL) and the Dendritic cell immunoreceptor (DCIR) are two poorly characterized members of the CLR family. In this study, we investigated the role of MCL and DCIR in the pathogenesis of murine colitis. Both CLRs bound to intestinal microbiota to a different extent. They modulated the production of pro-inflammatory cytokines by antigen-presenting cells upon stimulation with heat-killed microbiota and impacted subsequent T cell responses. To analyze whether MCL and DCIR contribute to the pathogenesis of IBD, the dextran sulfate sodium (DSS) murine colitis model was employed. MCL−/− as well as DCIR−/− mice exhibited only a slightly increased severity of disease compared to wild-type mice indicating a limited role for MCL and DCIR in the regulation of intestinal immunity.
Comparisons of logistic regression and artificial neural network on power distribution systems fault cause identification

Power distribution systems play an important role in modern society. Proper outage root cause identification is often essential for effective restorations when outages occur. This paper reports on the investigation and results of two classification methods: logistic regression and neural network applied in power distribution fault cause classifier. Logistic regression is seldom used in power distribution fault diagnosis, while neural network, has been extensively used in power system reliability researches. Evaluation criteria of the goodness of the classifier includes: correct classification rate, true positive rate, true negative rate, and geometric mean. Two major distribution faults, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques.
Charge Scheduling of an Energy Storage System under Time-of-Use Pricing and a Demand Charge

A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power.
Towards a Composite XML Schema Matching Approach Using Reference Ontology

Schema matching, aiming at finding the semantic correspondence of concepts between different data sources, has become a hot research area in data integration, data exchange and other areas. Over the past few years, numerous effective schema matching methods have been proposed. In the latest years, researchers have proposed a number of matching methods, which make it possible to identify and discover the semantic correspondence between the data. But schema matching have some challenges in the form of definition, utilization, and combination of element similarity measures, especially in multiple schema matching. In this paper, we study multi-schema matching problem, and convert this issue to indirect matching problem using reference ontology. We find and locate the core word, present composite schema matching methods, and generate matching result. Our experimental results demonstrate the high performance with a well precision and recall.
Simulation Results for Algebraic Soft-Decision Decoding of Reed-Solomon Codes

The Koetter-Vardy algorithm is an algebraic softdecision decoder for Reed-Solomon codes. The algorithm is based on an extension to the Guruswami-Sudan list-decoding algorithm with variable multiplicities that are assigned proportional to the reliabilities of the received symbols. There are three steps: (1) multiplicity calculation, (2) interpolation of a bivariate polynomial, and (3) finding the y-roots of this polynomial. A low-complexity algorithm for calculating the multiplicities is proposed. Simulation results indicate that the coding gain is dependent on the code rate and ranges from 0.25 dB to 4.25 dB with a practical upper limit of 1− 1.5 dB, assuming binary phase shift keying and additive white Gaussian noise. Higher coding gains of between 2 dB and 6.8 dB can be achieved over a Rayleigh fading channel. The KV algorithm exhibits a performance-complexity tradeoff which is tunable by the choice of mmax, n and k. The code parameters should be chosen carefully to take advantage of the “sweet spots” in the performance-complexity profile.
Object combining: a new aggressive optimization for object intensive programs

Object combining tries to put objects together that have roughly the same life times in order to reduce strain on the memory manager and to reduce the number of pointer indirections during a program's execution. Object combining works by appending the fields of one object to another, allowing allocation and freeing of multiple objects with a single heap (de)allocation. Unlike object inlining, which will only optimize objects where one has a (unique) pointer to another, our optimization also works if there is no such relation. Object inlining also directly replaces the pointer by the inlined object's fields. Object combining leaves the pointer in place to allow more combining. Elimination of the pointer accesses is implemented in a separate compiler optimization pass. Unlike previous object inlining systems, reference field overwrites are allowed and handled, resulting in much more aggressive optimization. Our object combining heuristics also allow unrelated objects to be combined, for example, those allocated inside a loop; recursive data structures (linked lists, trees) can be allocated several at a time and objects that are always used together can be combined. As Java explicitly permits code to be loaded at runtime and allows the new code to contribute to a running computation, we do not require a closed‐world assumption to enable these optimizations (but it will increase performance). The main focus of object combining in this paper is on reducing object (de)allocation overhead, by reducing both garbage collection work and the number of object allocations. Reduction of memory management overhead causes execution time to be reduced by up to 35%. Indirection removal further reduces execution time by up to 6%. Copyright © 2005 John Wiley & Sons, Ltd.
New multilevel inverter topology with minimum number of switches

This paper presents two types of multilevel inverters, known as symmetrical and asymmetrical multilevel inverter. Both types are very effective and efficient for improving the quality of the inverter output voltage. Firstly, we describe briefly the structural parts of the inverter then switching strategy and operational principles of the proposed inverter are explained and operational topologies are given. The proposed topology reduces the number of switches, losses, installation area and converter cost. Finally, the simulation results are provided to validate the proposed theory.
Comparison Between Preoperative Administration of Methylprednisolone With its Administration Before and During Congenital Heart Surgery on Serum Levels of IL-6 And IL-10

Background Steroid administration during cardiopulmonary bypass is considered to improve cardiopulmonary function by modulating inflammations caused by bypass. Objectives This study was performed to compare effectiveness of preoperative and intraoperative methylprednisolone (MP) to preoperative methylprednisolone alone in post bypass inflammatory (IL-6) and anti-inflammatory (IL-10) factors. Patients and Methods Fifty pediatric patients undergoing cardiopulmonary bypass surgery from August 2011 to 2012 in the cardiac surgery department of Imam Reza Hospital, the major center for CPB, in Mashhad, Iran were randomly assigned to receive preoperative and intraoperative MP (30 mg/kg, 4 hours before bypass and in bypass prime, number 25) or preoperative MP only (30 mg/kg, number 25). Before and after bypass, four and 24 hours after bypass, serum IL-6 and IL-10 were measured by ELISA. Results In both groups, no significant difference with variation of expression for IL-6 (inflammatory factor) and IL-10 (anti-inflammatory factor) in different times after bypass was observed. Conclusions No significant difference in reducing post bypass inflammation between preoperative steroid treatment and combined preoperative and intraoperative steroid administration reported and they had the same effects.
Lymph node shape in computed tomography imaging as a predictor for axillary lymph node metastasis in patients with breast cancer

The aim of the present study was to evaluate whether preoperative computed tomography (CT) is a useful modality for the diagnosis of axillary lymph node metastasis. The axillary lymph node status was examined in patients with primary breast cancer who had undergone surgery. In total, 75 patients were analyzed with preoperative contrast CT images, following which the patients underwent an intraoperative sentinel lymph node biopsy to determine possible predictors of axillary lymph node metastasis. The lymph node shape was classified into three groups, which included fat-, clear-and obscure-types. Multivariate analysis revealed that clear-type lymph nodes in preoperative contrast CT imaging may be an independent predictor of lymph node metastasis (odds ratio, 15; P=0.003). Therefore, the results indicated that preoperative CT examination is useful to predict axillary lymph node metastasis.
FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models

Medical predictive modeling is a challenging problem due to the heterogeneous nature of the patients. In order to build effective medical predictive models we need to address such heterogeneous nature during modeling and allow patients to have their own personalized models instead of using a one-size-fits-all model. However, building a personalized model for each patient is computationally expensive and the over-parametrization of the model makes it susceptible to the model overfitting problem. To address these challenges, we propose a novel approach called FactORized MUlti-task LeArning model (Formula), which learns the personalized model of each patient via a sparse multi-task learning method. The personalized models are assumed to share a low-rank representation, known as the base models. Formula is designed to simultaneously learn the base models as well as the personalized model of each patient, where the latter is a linear combination of the base models. We have performed extensive experiments to evaluate the proposed approach on a real medical data set. The proposed approach delivered superior predictive performance while the personalized models offered many useful medical insights.
On the capacity of wireless networks: the relay case

Gupta and Kumar (see IEEE Transactions an Information Theory, vol.46, no.2, p.388-404, 2000) determined the capacity of wireless networks under certain assumptions, among them point-to-point coding, which excludes for example multi-access and broadcast codes. We consider essentially the same physical model of a wireless network under a different traffic pattern, namely the relay traffic pattern, but we allow for arbitrarily complex network coding. In our model, there is only one active source/destination pair, while all other nodes assist this transmission. We show code constructions leading to achievable rates and derive upper bounds from the max-flow min-cut theorem. It is shown that lower and upper bounds meet asymptotically as the number of nodes in the network goes to infinity, thus proving that the capacity of the wireless network with n nodes under the relay traffic pattern behaves like log n bits per second. This demonstrates also that network coding is essential: under the point-to-point coding assumption considered by Gupta et al., the achievable rate is constant, independent of the number of nodes. Moreover, the result of this paper has implications' and extensions to fading channels and to sensor networks.
Inclusion in Rural School Districts: Where is the Superintendent?

Ordinarily, one would assume that rural school superintendents provide leadership to all the district's programs. Results from a study investigating the implementation of inclusion in Texas rural schools raises some question regarding this assumption. This article provides an overview of the study, findings, conclusions, and recommended actions for facilitating the superintendents' leadership role in implementing inclusive practices.
Productivity Trends in India’s Energy Intensive Industries

This paper reports on an analysis of productivity growth and input trends in six energy intensive sectors of the Indian economy, using growth accounting and econometric methods. The econometric work estimates rates and factor price biases of technological change using a translog production model with an explicit relationship defined for technological change. Estimates of ownprice responses indicate that raising energy prices would be an effective carbon abatement policy for India. At the same time, our results suggest that, as with previous findings on the U.S. economy, such policies in India could have negative long run effects on productivity in these sectors. Inter-input substitution possibilities are relatively weak, so that such policies might have negative short and medium term effects on sectoral growth. Our study provides information relevant for the analysis of costs and benefits of carbon abatement policies applied to India and thus contributes to the emerging body of modeling and analysis of global climate policy.
Interface Design through the Eyes of Hofstede

This study examines selected design characteristics of government Web interfaces from ten Arab countries using Hofstede’s cultural dimensions. Organizational and graphical elements from 100 ministry Web interfaces from Jordan, Syria, Yemen, Sudan, Morocco, Tunisia, Algeria, Bahrain, Qatar, and Oman were examined using content analysis method. The results suggest that Hofstede’s model of culture does not fully reflect the design characteristics of these interfaces.
A Mixed-Technology Asymmetrically Biased Extended and Reconfigurable Doherty Amplifier With Improved Power Utilization Factor

In this paper, we present a mixed-technology extended Doherty amplifier using asymmetrical voltage biasing with an LDMOS main device and a GaN auxiliary device to improve the power utilization factor of the amplifier. Moreover, in contrast to previous work, the amplifier can achieve reconfigurable back-off power ranges and an extended bandwidth without using a mixed-signal setup. The analysis also highlights the relationship between power utilization and the range of the reconfigurable back-off power level. Finally, we demonstrate that the proposed amplifier can be optimally configured for a given modulated signal to obtain the highest average efficiency. A 180-W mixed-technology prototype Doherty amplifier measured peak and back-off efficiencies greater than 50% when configured for 6, 8, and 10 dB of back-off power level at 790, 870, and 960 MHz under continuous-wave stimulus. The amplifier is highly linearizable when driven with 20-MHz long-term evolution and WCDMA signals, achieving adjacent channel power ratio of better than ${-}{\hbox{50}}$ dBc after digital pre-distortion linearization.
A Positive Theory of Network Connectivity

In this paper we develop a positive theory of network connectivity, seeking to provide the microfoundations of alternative network topologies as the result of self-interested actors. By building roads, landowners hope to increase their parcels' accessibility and economic value. A simulation model is performed on a grid-like land-use layer with a downtown in the center. The degree to which the networks are tree-like is evaluated. This research posits that road networks experience an evolutionary process where a tree-like structure first emerges around the centered parcel before the network pushes outward to the periphery. Road network topology becomes increasingly connected as the accessibility value of reaching other parcels increases. The results demonstrate that, even without a centralized authority, road networks can display the property of self-organization and evolution, and that, in the absence of intervention, the degree to which a network structure is tree-like or web-like results from the underlying economies.
Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic Carm Data

Anatomical and functional information about the cardiac chambers is a key component of future developments in the field of interventional cardiology. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. Some approaches attempt to add the temporal dimension (4-D) by electrocardiogram (ECG)gating in order to distinguish physical states of the heart. However, for the left heart ventricle scanned during one single C-arm sweep, this approach leads to insufficient projection data and thus to a degraded image reconstruction quality. In this paper, we evaluate the influence of different interpolation methods for a motion compensated reconstruction approach for the left heart ventricle based on a recently presented 3-D dynamic surface model. The surface model results in a sparse motion vector field (MVF) defined at control points. However, to perform a motion compensated reconstruction a dense MVF is required. The dense MVF can be determined by different interpolation methods. In this paper, we evaluate thin-plate splines (TPS), the Shepard’s method, simple averaging, and a smoothed weighting function as interpolation functions. The 2-D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2-D ventricle blood pool is quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. Preliminary results on heart ventricle phantom data, as well as on clinical human data show the best results with the TPS interpolation.
Co-occurrence Bag of Words for Object Recognition

In the original 'bag of words' model image fea- tures are seen as independent and orderless. In the real world, relative co-occurrence of image features defines the spatial order in which they appear. This means the spatial relationship between image features has potential informa- tion to be exploited in a classification task. Our model attempts to discover the usefulness of the co-occurrence in- formation of image features with its extent ranging from local to global level targeting the image classification task. To establish the pair-wise relations between the features, we use the neighborhood of each image feature. To de- fine the extent of neighborhood of an image feature at a local level and to achieve scale invariance, the model uses a circular area based on the elliptical scale at which each image feature is detected. To range from local to global level, the model explores the extent of the neighborhood spatially at integral multiples of the circular area radius. In turn, each image is described by a histogram of pair-wise visual words and a concatenated histogram of independent visual words with pair-wise visual words. We develop a stop word removal technique to eliminate noisy redundant visual words from the collection. Experimental results show that the pair-wise co-occurrences of visual words alone and their augmentation with independent occurrences leads to a positive improvement in the classification accuracy.
Towards binocular active vision in a robot head system

This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the ‘searchlight metaphor’ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a “stepping stone” visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade.