Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft Computing Techniques for Path Loss Estimation at 1800 MHz in a Tropical Metropolitan Environment : Case Study of Abuja-Nigeria

This study proposes path loss prediction models based on Soft Computing Techniques, for a tropical metropolitan environment, using Abuja, the federal capital territory of Nigeria, as case study. The two Soft Computing networks considered are the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Generalized Regression Neural network (GRNN). Prediction models based on these networks were created, trained and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations (BTSs) distributed across the city. Results indicate that the ANFIS and GRNN based models gave predictions with Root Mean Squared Error (RMSE) values of 5.76dB and 5.17dB respectively. A comparison of prediction results indicate that on the average, the two Soft Computing Techniques offer an improvement of about 4.71dB over the linear regression based methods comprising of the COST 231 Hata and COST 231 Walfisch-Ikegami models.
Rate Balancing for Multiuser Multicell Downlink MIMO Systems

In this paper, we consider rate balancing problem for the Multiple-Input Multiple-Output (MIMO) Interfering Broadcast Channel (IBC), i.e. the multiuser multicell downlink (DL). We address the MIMO DL beamformer design and power allocation for maximizing the minimum weighted user rate with sum-power constraint with the weighting reflecting user priorities. The proposed solution is based on reformulating the max-min user rate optimization problem into a weighted Mean Squared Error (MSE) balancing problem. Employing MSE duality between DL channel and its equivalent Uplink (UL) channel, we propose an iterative algorithm to jointly design the transceiver filters and the power allocation. Simulation results verify the computational efficiency of the proposed algorithm and provide appreciable performance improvements as compared to optimizing the conventional unweighted per user MSE.
Implementation of Dynamic Graphical Display Using GDI+ in C#

GDI+ is graphics device interface+. It is a class libraries.It transforms data to a form which is compatible with graphics device.Microsoft Visual C# is object oriented language which is simple,modern and secure.It provides us with environment which is able to exploit program that run at Windows and other flat.Therefore,with the use of GDI+,it will achieve the alternation conveniently and quickly between application and graphics device in C#. This paper will introduce approaches to implementation of dynamic graphical in C# so as to meet with the requirements of complex engineering and more flexible display.
E ± cient Estimation of General Linear Growth Curve Models

A general linear growth curve model is proposed where only part, or more generally, a linear combination of growth curve parameters, growth curve chracteristic is modelled in the second stage model. The formula for generalized least squares estimator under constraints is derived when variance parameters are known. When the variance parameters are unknown, three methods of estimation for general linear growth curve model are proposed: full maximum likelihood, second stage maximum likelihood and the two-stage estimator. A consistent and unbiased estimator for the covariance matrix of the random e®ects is derived based on the method of moments. The two-stage estimator has the form of a generalized least squares estimator under constraints with variance parameters estimated by the method of moments. It is shown that all three estimators are consistent, asymptotically normally distributed, e±cient and equivalent when the number of individuals goes to in ̄nity. The properties of estimators are investigated under model misspeci ̄cation. It is shown that one might come to a severe bias using standard growth curve model. The suggested approach is illustrated on pancreatic tumor growth data example.
A HYSTERETIC MITC 9 SHELL FINITE ELEMENT

In this work, a hysteretic shell finite element for the inelastic static and dynamic analysis of structures is presented. The Bouc-Wen model ([6], [7]) is utilized as a smooth hysteretic, rate independent model, capable of expressing the hysteretic behavior that can be easily extended to account for stiffness degradation, strength deterioration and pinching phenomena. On the basis of the classical theory of plasticity [2], the generalized 3D Bouc-Wen model is expressed in tensorial form incorporating the yield criterion and linear or nonlinear, isotropic or kinematic hardening law. Based on this approach, a hysteretic shell finite element is developed following the steps of FEM in which the shell is considered as a number of fully bonded layers [5]. The classical elastic formulation of the shell element [1] is employed following the Mixed Interpolation of Tensorial Components (MITC) approach in MITC9 shell element ([3], [4]). This is extended by considering as additional hysteretic degrees of freedom the plastic strains at the Gauss points of each interface between layers, the evolution of which is determined by a BoucWen evolution equation. The solution provides the nodal displacements, the elastic and plastic strains and the stresses at every Gauss point of each interface. Numerical results are presented that validate the proposed formulation, which are further compared against those obtained using Abaqus code. A good agreement is achieved between the standard FEM and the proposed formulation which computationally results as more efficient for the same accuracy.
Quantification of freshly-excised human lymph node tissue using high-frequency ultrasound

Quantitative ultrasound (QUS) methods using high-frequency transducers (i.e., > 20 MHz) permit characterization of tissue microstructure and organization using system-independent estimates. In this study, lymph nodes excised during lymphadenectomy from cancer patients were evaluated using three-dimensional (3D) high-frequency QUS methods. The long-term objective of these studies is to develop 3D QUS methods for detecting lymph-node metastases. Detection of metastases is critically important for cancer staging, treatment, and optimal patient management. A custom laboratory scanning system was used to acquire radio-frequency (RF) data in 3D from excised lymph nodes using a 26-MHz center-frequency transducer. Overlapping 1-mm cylindrical regions-of-interest (ROIs) of the RF data were processed to yield 13 QUS estimates associated with tissue microstructure. QUS estimates were obtained from 255 nodes from 162 colorectal, gastric, and breast-cancer patients. Cancer-detection performance was assessed for individual estimates and linear combinations of estimates. ROC results demonstrated excellent classiﬁcation. For colorectal-and gastric-cancer nodes, the areas under the ROC curves (AUCs) were above 0.94. Slightly poorer results (AUC = 0.87) were obtained for breast-cancer nodes. Images based on QUS parameters also permitted localization of cancer foci in some micrometastatic cases. Therefore, these advanced 3D QUS methods potentially can be valuable for detecting small clinically-signiﬁcant metastatic foci in dissected lymph nodes which can be missed during routine histology procedures.
Komparasi Metode Decision Tree dan K-Means Clustering Dalam

Penelitian domain sistem rekomendasi (RS) sedang popular saat ini. RS adalah teknologi yang memungkinkan penggunanya untuk mendapatkan rekomendasi pribadi untuk item tertentu. Salah satu pendekatan yang sering digunakan dalam RS adalah metode Collaborative Filtering (CF). CF adalah algoritme yang mendasarkan prediksi dan rekomendasi dari penilaian atau perilaku pengguna lain dalam sistem. Salah satu tantangan yang dihadapi dalam CF adalah masalah Coldstart. Cold-start adalah keadaan dimana RS tidak dapat mengambil kesimpulan secara optimal disebabkan oleh informasi dalam dataset yang tidak lengkap. Sudah banyak penelitian yang dilakukan untuk menangani masalah Coldstart. Namun kebanyakan penelitian fokus pada pendekatan CF dan tidak fokus pada awal proses rekomendasi yaitu pada saat proses pengelompokan pengguna. Setiap metode pengelompokan memiliki kelemahan dan kelebihan masingmasing. Pada penelitian ini mengusulkan untuk meneliti dan membandingkan metode pengelompokan, khususnya algoritme decision tree dan K-means clustering dalam menangani masalah cold-start pengguna baru dengan pendekatan CF berorientasi ranking khususnya Copelandscore. Dari hasil evaluasi menggunakan NDCG metric disimpulkan bahwa hasil dari pendekatan K-means clustering memiliki nilai akurasi yang lebih besar dibandingkan dengan pendekatan decision tree. Namun perbedaan akurasinya tidak signifikan.
Diffusion Directions Imaging (DDI)

Diffusion magnetic resonance imaging (dMRI) is the reference \emph{in vivo} modality to study the connectivity of the brain white matter. Images obtained through dMRI are indeed related to the probability density function (pdf) of displacement of water molecules subject to restricted diffusion in the brain white matter. The knowledge of this diffusion pdf is therefore of primary importance. Several methods have been devised to provide an estimate of it from noisy dMRI signal intensities. They include popular diffusion tensor imaging (DTI) as well as higher-order methods. These approaches suffer from important drawbacks. Standard DTI cannot directly cope with multiple fiber orientations. Higher-order approaches can alleviate these limitations but at the cost of increased acquisition time. In this research report, we propose, in the same vein as DTI, a new parametric model of the diffusion pdf with a reasonably low number of parameters, the estimation of which does not require acquisitions longer than those used in clinics for DTI. This model also accounts for multiple fiber orientations. It is based on the assumption that, in a voxel, diffusing water molecules are divided into compartments. Each compartment is representative of a specific fiber orientation (which defines two opposite directions). In a given compartment, we further assume that water molecules that diffuse along each direction are in equal proportions. We then focus on modeling the pdf of the displacements of water molecules that diffuse only along one of the two directions. Under this model, we derive an analytical relation between the dMRI signal intensities and the parameters of the diffusion pdf. We exploit it to estimate these parameters from noisy signal intensities. We carry out a cone-of-uncertainty analysis to evaluate the accuracy of the estimation of the fiber orientations and we evaluate the angular resolution of our method. Finally, we show promising results on real data and propose a visualization of the diffusion parameters which is very informative to the neurologist.
Classification of Computer Generated and Natural Images based on Efficient Deep Convolutional Recurrent Attention Model

Most state-of-the-art techniques of distinguishing natural images and computer generated images based on handcrafted feature and Convolutional Neural Network require processing of the entire input image pixels uniformly. As a result, such techniques usually require extensive computation time and memory, that scale linearly with the size of the input image in terms of number of pixels. In this paper, we deploy an efficient Deep Convolutional Recurrent Attention model with relatively less number of parameters, to distinguish between natural and computer generated images. The proposed model uses a glimpse network to locally process a sequence of selected image regions; hence, the number of parameters and computation time can be controlled effectively. We also adopt a local-to-global strategy by training image patches and classifying full-sized images using the simple majority voting rule. The proposed approach achieves superior classification accuracy compared to recently proposed approaches based on deep learning.
Enhanced Video Retrieval and Classification of Video Database Using Multiple Frames Based on Texture Information

Content Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless there exist CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos. Results have shown performance improvement when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. Many researchers have an opinion that result is poor when images are used as a query for video retrieval. Here, instead of using a single image or key frames, multiple frames of the video clip being searched are used. Technique used for CBVR system shown in this paper yields significantly acceptable and higher retrieval results. The system is implemented using MATLAB. Performance of the system is assessed using a database containing 1000 video clips of 20 different categories with each category having 50 clips. The performance is shown using two different types of texture featuresfeatures extracted using Gabor filter and Kekre’s Fast Codebook Generation Algorithm (KFCG). KeywordsCBVR, Multiple Frames, Gabor, KFCG,
From Self-Esteem to Mental Health : Empathy as Mediator

Problem Statement: Although self-esteem has been considered an endstate of mental health or a defense mechanism, recent theoretical and empirical findings questions its status in the field of psychology. As a result of such a framework, research mainly concentrated on mean differences in self-esteem and the consequences of these differences for mental health. However, some theoretical considerations imply that selfesteem should be considered a dynamic human striving, which contributes to mental health through its effects on other variables related to the construction and maintainance of social relationships such as empathic tendency. Thus, it is a requisite to understand the mediatory factors connecting self-esteem and mental health. Purpose of the Study: The purpose of this study was to understand whether the relation between self-esteem and mental health is mediated by empathic tendency. In other words, a structural model with direct paths from self-esteem to empathic tendency and paths from empathic tendency to life-satisfaction, happiness, and psychopathology were specified. Method: A structural model was tested using structural equation modeling. Before the structural model, a measurement model was tested. A measurement model is structured using the item-parceling method. Different goodness of fit statistics were used to assess models * Corresponding Author: Asst. Prof. Dr., Izmir University of Economics, Department of Psychology, Izmir, TURKEY, faruk.simsek@ieu.edu. tr **Asst. Prof. Dr., Abant Izzet Baysal University, Faculty of Education, Bolu, TURKEY, ihsanbozanoglu@gmail.com 226 Ömer Faruk Şimşek & İhsan Bozanoğlu such as the Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), and Root Mean Square Error of Approximation (RMSEA). Findings and Results: The results showed that both the measurement model and structural model fitted the data reasonably. Mediation tests indicated that the relation between self-esteem and well-being (life satisfaction and happiness) is partially mediated by empathic tendency, while the relation between self-esteem and ill-being is fully mediated. Conclusions and Recommendations: The idea that self-esteem is good in itself and an end state that should be satisfied for mental health should be re-evaluated. Empathic tendency is one important mediatory variable in understanding the effect of self-esteem on mental health. Its effects on mental health through other social or individual dynamics should be evaluated in future research.
Pyruvate kinase M 2 is requisite for Th 1 and Th 17 differentiation

Th1 and Th17 are important in the pathogenesis of autoimmune diseases and they depend on glycolysis as a source of energy. T cell antigen receptor signaling phosphorylates a serine/threonine kinase, calcium/calmodulin–dependent protein kinase IV (CaMK4), and promotes glycolysis. Based on these findings we hypothesized that CaMK4 promotes glycolysis. Camk4-deficient CD4+ T cells and cells treated with a CaMK4 inhibitor had less glycolysis compared with their counterparts. Pull-down of CaMK4 and mass spectrometry identified pyruvate kinase muscle isozyme (PKM), the final rate-limiting enzyme in glycolysis, as a binding partner. Coimmunoprecipitation and Western blotting showed that CaMK4 interacts directly with PKM2. Camk4-deficient CD4+ T cells displayed decreased pyruvate kinase activity. Silencing or pharmacological inhibition of PKM2 reduced glycolysis and in vitro differentiation to Th1 and Th17 cells, while PKM2 overexpression restored Th17 cell differentiation. Treatment with a PKM2 inhibitor ameliorated experimental autoimmune encephalomyelitis and CD4+ T cells treated with PKM2 inhibitor or Pkm2shRNA caused limited disease activity in an adoptive cell transfer model of experimental autoimmune encephalomyelitis. Our data demonstrate that CaMK4 binds to PKM2 and promotes its activity, which is requisite for Th1 and Th17 differentiation in vitro and in vivo. PKM2 represents a therapeutic target for T cell–dependent autoimmune diseases. Research Article Immunology
Interpretable Robust Recommender Systems with Side Information

In this paper, we propose two methods, namely Trace-norm regression (TNR) and Stable Tracenorm Analysis (StaTNA), to improve performances of recommender systems with side information. Our trace-norm regression approach extracts low-rank latent factors underlying the side information that drives user preference under different context. Furthermore, our novel recommender framework StaTNA not only captures latent low-rank common drivers for user preferences, but also considers idiosyncratic taste for individual users. We compare performances of TNR and StaTNA on the MovieLens datasets against state-of-the-art models, and demonstrate that StaTNA and TNR in general outperforms these methods.
VISMELLS: An Interactive Visualization for Identifying and Evaluating the Effects of Code Smells on Software Projects

Context: Code Smells indicate potential problems in the making of software projects. If considered harmful, they must be removed from the source code. Thus, it is important to enable developers to spot the source code artifacts that have been affected by Smells. Problem: however, there is a lack of adequate approaches to make the identification of Code Smells easier while providing details about their occurrences. Goals: in order to help developers to identify Code Smells, we have developed VISMELLS, an interactive visualization. Method: we elicited requirements with the purpose of defining the functionalities of VISMELLS. After implementing the visualization we went through a qualitative analysis of its application with the help of professional software developers and computer science students. Lastly, we used the data obtained from the evaluation to determine if VISMELLS met the targeted requirements. Results: as a result, we were able to confirm that VISMELLS fulfilled its intended purpose as it received good reviews from testers.
Performance Evaluation of Adaptive Beamforming in 5 GV 2 X Networks

Vehicles are the third fastest growing connected device type after smart phones and tablets. Also, automotive industry is interested to get more vehicles connected to the internet to improve traffic safety and efficiency. This creates a need for Vehicle-to-Everything (V2X) communications. In this work, the possibility of exploiting beamforming in LTE-V2X is considered. Singular value decomposition (SVD) receiver and precoder is implemented in an LTE-A system level simulator and the performance on multi-lane highway scenario is simulated and analyzed in downlink Vehicle-to-Infrastructure (V2I) scenario. The performance is compared to the conventional maximumratio combining (MRC) and LTE codebook precoded minimum mean square error (MMSE) receivers. In addition, the switchedbeam beamforming is imitated by modified antenna patterns with 7 and 15 narrow beams. The results show that the SVD receiver provides gain compared to the conventional MRC and MMSE receivers in ideal scenario. Furthermore, with modified antenna patterns, the performance was enhanced when compared to the default antenna pattern.
GIS-BASED VISUALIZATION FOR ESTIMATING LEVEL OF SERVICE

Traffic congestion is the biggest problem in Turkey, especially in Istanbul. People have a hard time because of the traffic problems, which causes accident and deaths in Istanbul. In this regard, in order to minimise those kind of problems, the concept of safety level of service (LOS) at a section existing on the Trans European Motorway (TEM) and D-100 state roadway having huge traffic congestion especially in peak hours is determined with the help of using Geographic Information Systems (GIS), which is the objective of this study. According to Highway Capacity Manual (HCM), volume-to-capacity (V/C) ratios and average travel speeds are used for the estimation of LOS for selected motorway segments requires. In this paper, types of LOS will be assigned for the concerned road sections and some suggestion will be given in order to solve some traffic congestion related problems about the road. GIS-Based Visualization for Estimating Level of Service (9339) Gozde Bakioglu and Asli Dogru (Turkey) FIG Congress 2018 Embracing our smart world where the continents connect: enhancing the geospatial maturity of societies Istanbul, Turkey, May 6–11, 2018 GIS-BASED VISUALIZATION FOR ESTIMATING LEVEL OF SERVICE Gozde BAKIOGLU and Asli DOGRU, Turkey
Comparing Ocean Surface Boundary Vertical Mixing Schemes Including Langmuir Turbulence

Six recent Langmuir turbulence parameterization schemes and five traditional schemes are implemented in a common single‐column modeling framework and consistently compared. These schemes are tested in scenarios versus matched large eddy simulations, across the globe with realistic forcing (JRA55‐do, WAVEWATCH‐III simulated waves) and initial conditions (Argo), and under realistic conditions as observed at ocean moorings. Traditional non‐Langmuir schemes systematically underpredict large eddy simulation vertical mixing under weak convective forcing, while Langmuir schemes vary in accuracy. Under global, realistic forcing Langmuir schemes produce 6% (−1% to 14% for 90% confidence) or 5.2 m (−0.2 m to 17.4 m for 90% confidence) deeper monthly mean mixed layer depths than their non‐Langmuir counterparts, with the greatest differences in extratropical regions, especially the Southern Ocean in austral summer. Discrepancies among Langmuir schemes are large (15% in mixed layer depth standard deviation over the mean): largest under wave‐driven turbulence with stabilizing buoyancy forcing, next largest under strongly wave‐driven conditions with weak buoyancy forcing, and agreeing during strong convective forcing. Non‐Langmuir schemes disagree with each other to a lesser extent, with a similar ordering. Langmuir discrepancies obscure a cross‐scheme estimate of the Langmuir effect magnitude under realistic forcing, highlighting limited understanding and numerical deficiencies. Maps of the regions and seasons where the greatest discrepancies occur are provided to guide further studies and observations.