Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Study of Downlink Users in Non-Orthogonal Multiple Access (NOMA) for 5G Communications

An outline of NOMA principles is provided in this article. Furthermore, this page discusses cooperative NOMA and its variations, explains power allocation in detail as a technique of resource allocation for NOMA, and gives an overview of the research challenges related to NOMA. We then, use a two-case scenario, to suggest a dynamic power allocation (DPA) plan for the downlink NOMA users, albeit it can also be expanded to many use cases. The DPA relies on channel state information (CSI) to guarantee the quality of service (QOS) for cell center customers (user c). The Outage probability (OP) as a critical performance criterion simulation data is also provided, and they demonstrate a notable performance improvement when DPA is employed compared to fixed power allocation.
10 Arti ﬁ cial Intelligence in Neuroscience

Neurosurgeons have the privilege of peeking inside the most precious and the most mysterious device on earth: the human brain (Crick et al., 2004). The human brain is also the most expensive device on earth given that mental health problems constitute the largest health care cost. By deciphering the inner secrets of brain computations, scientists and engineers have taken inspiration to develop smart artificial intelligence (AI) algorithms. These AI algorithms in turn provide much help to understanding brain function and to multiple applications in brain disorders, including neurosurgery.
Biodiversity in cities. The effect of spatial scale and biodiversity data in heterogeneous landscapes.

The assessment and monitoring of biodiversity in urban areas has been performed until now through dedicated on site studies and a small number of studies using online digital biodiversity databases. The question remains as to what extent an e�cient and effective monitoring scheme could be implemented, one that not only facilitates comparisons across time and space, but also serves as an early change detection tool that complement local studies. Global Biodiversity Information Facility (GBIF) has been promoted for its central role, gathering and harmonizing biodiversity data worldwide, thereby facilitating the assessment and monitoring of biodiversity in multiple ecosystems. While GBIF data has been investigated for its potential at large scales (national, global) and in natural ecosystems, the question remains as to what extent, and in which context, is GBIF data applicable to urban biodiversity assessment and monitoring. In this study we have shown that the type of organisms surveyed -as well as its ecology and the spatial scale(s) of the assessments are critical to identify the effect of urban and green infrastructure in cities.
Calibrated Clustering and Analogy-Based Expectation Equilibrium *

Families of normal-form two-player games are categorized by players into K analogy classes applying the K-means clustering technique to the data generated by the distributions of opponent’s behavior. This results in Calibrated Analogy-Based Expectation Equilibria in which strategies are analogy-based expectation equilibria given the analogy partitions and analogy partitions are derived from the strategies by the K-means clustering algorithm. We discuss various concepts formalizing this, and observe that distributions over analogy partitions are sometimes required to guarantee existence. Applications to games with linear best-responses are discussed highlighting the differences between strategic complements and strategic
Stable Models and Temporal Difference Learning

In this thesis, we investigate two different aspects of stability: the stability of neural network dynamics models and the stability of reinforcement learning algorithms. In the first chapter, we propose a new method for learning Lyapunov-stable dynamics models that are stable by construction, even when randomly initialized. We demonstrate the effectiveness of this method on damped multi-link pendulums and show how it can be used to generate high-fidelity video textures. In the second and third chapters, we focus on the stability of Reinforcement Learning (RL). In the second chapter, we demonstrate that regularization, a common approach to addressing instability, behaves counterintuitively in RL settings. Not only is it sometimes ineffective, but it can also cause instability. We demonstrate this phenomenon in both linear and neural network settings. Further, standard importance sampling methods are also vulnerable to this. In the third chapter, we propose a mechanism to stabilize off-policy RL through resampling. Called Projected Off-Policy TD (POP-TD), it resamples TD updates to come from a convex subset of “safe” distributions instead of (as in other resampling methods) resampling to the on-policy distribution. We show how this approach can mitigate the distribution shift problem in offline RL on a task designed to maximize such shift. Overall, this thesis advances novel methods for dynamics model stability and training stability in reinforcement learning, questions existing assumptions in the field, and points to promising directions for stability in model and reinforcement learning.
BARGAINING THEORY AND THE COPYRIGHT ROYALTY BOARD’S RATE SETTING MANDATE FOR INTERACTIVE STREAMING OF MUSIC

In the USA, the remuneration for songwriters whose copyrighted material is broadcast using online interactive music services is subject to a compulsory license with a rate that is regulated by the Copyright Royalty Board (CRB). On the other hand, an essentially equally necessary input to the interactive music services, namely the sound recording copyright, is fully and freely negotiated between the parties. This situation sets up an interesting bargaining problem that should be of great interest to the CRB for their statutory mandate. The present paper sets out this problem formally, and resolves the equilibrium outcomes. The model is calibrated with the actual rates that have been set recently by the CRB.
Jin–Xin relaxation method for solving a traffic flow problem in one dimension

We test the performance of the Jin–Xin relaxation and Lax–Friedrichs finite volume numerical methods in solving a traffic flow problem. In particular, we focus on traffic flow at a traffic light turning from red to green. Numerical solutions are compared with the analytical solution to the mathematical model. We find that the Jin–Xin relaxation solution is more accurate than the Lax–Friedrichs finite volume solution.
Continuing excellence in critical care pharmacy practice, education, and advocacy

The critical care pharmacist serves as the medication specialist on the multidisciplinary team caring for critically ill and injured patients. Critical care pharmacists apply unique expert knowledge when managing complex pharmacotherapy regimens in a patient population at high risk for medication errors and adverse drug reactions. Inclusion of a critical care pharmacist consistently reduces adverse medication events, morbidity, mortality, and costs. Over several decades since the inception of the critical care pharmacist in the 1960s, roles have expanded to encompass research and scholarly activities, education, and involvement in leadership roles. As such, this themed issue of the Journal of the American College of Clinical Pharmacy includes manuscripts focused on the continuing excellence in critical care pharmacy practice, education, and advocacy.
Locality estimates for Fresnel-wave-propagation and stability of near-field X-ray propagation imaging with finite detectors

Coherent wave-propagation in the Fresnel- or paraxial regime is the underlying contrast-mechanism to near-field X-ray propagation imaging (XPI), an emerging lensless technique that enables 2D- and 3D-imaging of biological soft tissues and other light-element samples down to nanometer-resolutions. Mathematically, propagation is governed by the Fresnel-propagator, a convolution with an arbitrarily non-local kernel. As real-world detectors may only capture a finite field-of-view, this non-locality implies that the recorded diffraction-patterns are necessarily incomplete. This raises the question of stability of image-reconstruction from the truncated data - even if the complex-valued wave-field, and not just its modulus, could be measured. Contrary to the latter restriction of the acquisition, known as the phase-problem, the finite-detector-problem has not received much attention in literature. The present work therefore analyzes locality of Fresnel-propagation in order to establish stability of XPI with finite detectors. Image-reconstruction is shown to be exponentially ill-posed in this setting - even without a phase-problem. However, quantitative estimates of the leaked wave-field reveal that Lipschitz-stability holds down to a sharp resolution limit that depends on the detector-size and varies within the field-of-view. The smallest resolvable lengthscale is found to be 1/F times the detector's aspect length, where F is the Fresnel number corresponding to this length. The stability results are extended to phaseless imaging in the linear contrast-transfer-function regime.
On the Effects of Local Environment on Active Galactic Nucleus (AGN) in the Horizon Run 5 Simulation

We use the Horizon Run 5 cosmological simulation to study the effect of galaxy intrinsic properties and the local environment on active galactic nuclei (AGNs) characterized by their threshold of the accretion rate. We select galaxies in the stellar mass range 109.5≤M*/M⊙≤1010.5 in the snapshot at redshift z = 0.625. Among various intrinsic properties, we find that the star formation rate of the host galaxy is most correlated to the AGN activity. To quantify the environment, we use background galaxy number density (large-scale environment) and distance and morphological type of the nearest neighbors (small-scale environment), and study their relative effects on the AGN properties. We find that, compared to the background density, the nearest neighbor environment is the dominant quantity determining the bolometric luminosity, star formation rate, and kinematic properties of AGNs and better dictates the gas mass of the host galaxy. We show that the cold gas content in the host galaxies is crucial in triggering AGN activity. However, when the nearest neighbor environment effects start to act at the neighbor distance of less than about half the virial radius of the neighbor, the neighbor environmental effects are the most dominant factor for quasar activity.
Multi-hop Information-based Graph Convolutional Network for Clustering

Clustering is an essential and demanding undertaking in data analysis. The combination of traditional neural networks and graph convolutional networks (GCNs) has been extensively discussed in clustering tasks, in which the deep clustering methods learn useful content information and the graph convolutional networks mine the structured neighboring information in the graph data. However, the existing works equally consider the importance of different features to clustering and only focus on the nearest neighboring information in the structured features, ignoring the features of the long distant neighbors in the multi-hop information, resulting in inferior performance. We propose a novel multi-hop information-based graph convolutional network (MIGCN) for clustering to overcome these disadvantages. Specifically, we fuse content features and structured features with adaptive weights, which are dynamically adjusted according to the training results. Meanwhile, we utilize a multi-hop information module to extract structured features. Moreover, we design a multi-supervision mechanism and guide the training and updating of the whole model. Our method achieves better results than previous deep clustering approaches because it takes into account the diverse features embedded in the neural network and a dynamic fusion strategy for clustering. Our model has undergone rigorous testing on widely-accepted standard datasets, employing both objective and subjective evaluation measures. The results consistently demonstrate that our model consistently outperforms state-of-the-art techniques, validating its superior performance.
Comparison Between Linear and Non-Linear Regression Models in the Prediction of the Height of Gall Oak Trees (Quercus infectoria Olive.) in Duhok Governorate

This study was conducted for two main purposes, the first one was to study the mathematical relationship between naturally growing gall oak trees in three microsites (Chamankey, Swaratoka, and Baroshky) in Dohuk Governorate and the second one was to compare the ability of linear and nonlinear regression models in height estimation. For such purposes, a sample of 152 trees was purposely selected from these microsites in such a way that they cover the whole variation of breast height diameter in these microsites. The STATGRAPHIC Centurion along with other computer facilities including Excel were used in estimating the parameters of 32 regression models for all microsites together. Many criteria for testing the performance ability of the developed regression equations in the prediction of the height of trees were used in order to select the most appropriate regression model for all microsites together. Screening of the generated models was performed in three stages; the first one is the use of mathematical analysis to see if they have biological limitations. The second stage is to see the performance ability of homogeneous models,(models with the same form of the dependent variable) in the prediction of the dependent variable. The last stage dealt with testing the performance ability of heterogeneous models, (models with different forms of the dependent variable) in the prediction of the dependent variable.
Dynamic panel probit: finite-sample performance of alternative random-effects estimators

Estimation of random-effects dynamic probit models for panel data entails the so-called "initial conditions problem". We argue that the relative finitesample performance of the two main competing solutions is driven by the magnitude of the individual unobserved heterogeneity and/or of the state dependence in the data. We investigate our conjecture by means of a comprehensive Monte Carlo experiment and offer useful indications for the practitioner.
A Survey on Homebuyers Behaviour towards the Housing Market in Klang Valley

Study on home buyers’ behaviour is significant as current improvements can be made by evaluating the trend of past behaviour while the future improvement is based on current behaviour. Although extensive research has been carried out, most of the research are focused on preference-related behaviour while too little attention has been paid on behaviour towards market condition. Therefore, this paper aims to assess the behaviour of home buyers towards the housing market in Klang Valley. Conceptual framework has been developed by incorporating three (3) behavioural attributes of home buyers namely housing market perceptions, informational influence as well as housing market expectations. Questionnaire survey has been distributed to 224 respondents who bought the house in Klang Valley. The data were analysed using a descriptive statistic and thematic analysis. The empirical results revealed that majority of home buyers aware that the price is rising and optimistically forecast the trend to be continued for both short and long terms while the future purchase possibilities are expected at difficult level. Finally, the main sources of information frequently used by home buyers are human sources followed by networked sources, printed media, organisational sources as well as other sources. The study also signifies the existence of herd behaviour in most of home buyers’ decision. This paper proposed a new conceptual model for studying the home buyers’ behaviour.
Прогнозирование рядов динамики рыночных индикаторов на основе нелинейной авторегрессионной нейронной сети

The modern practice of economic research relies heavily on mathematical models that make it possible to reveal hidden regularities in statistical data and make forecasts on their basis. Linear models based on vector autoregression (VAR) are the most common. However, the relationship between time series in the economy is often difficult to identify, so non-linear autoregressive (NAR) models show more reliable results. Artificial neural networks (ANNs) are usually used for implementation of these models, but ANNs do not provide the possibility of estimating the forecast in the form of mathematical expectation and a standard deviation. Therefore, the model proposed in the article combines two blocks: VAR and NAR. NAR is used to construct a prediction for a given number of points, and VAR for estimating the forecast in the form of a mathematical expectation and a standard deviation. The evaluation of the model was carried out on the daily data: exchange rate USD / RUB and “Brent” oil from 1.01. 2016 to 1.03. 2017. The average accuracy of forecasting the trend for the dollar was 54.9%, for the oil prices it was 54.0%. In this case, the relative error in predicting the dollar rate was from 1.09% (for the first point) to 2.01% (for the tenth point); the relative error in forecasting oil prices was from 1.28% (for the first point) to 4.58 % (for the tenth point). Thus, the model showed accurate results when predicting dynamic series and can be used to solve other forecasting problems. In particular, it is expedient to use the model as one of the factors when making investment decisions. In addition, the evaluation of forecasts is done on the basis of testing the NAR block of historical data and on the basis of VAR block forecast in the form of mathematical expectation and standard deviation.
Relationship of GHRL Gene Polymorphism with Milk Production and Components in Holstein Cows

The current study was conducted in private station located in the middle of Iraq by using 100 samples of blood to determine the effect of GHRL gene polymorphism on milk traits in Holstein cows. Results showed two alleles with co-dominance and three genotypes was detected in the site -97, the allele frequency was 0.72 and 0.28 for G and A allele respectively. Results showed a significant effect of SNP on lactation period length (P<0.01). The cows with mutant genotype (AA) were the longest lactation period (241 day) while the cows with wild genotype were the shortest lactation period. Total milk yield affected significantly (P<0.01) by allele type, the highest quantity of milk yield was in cows with mutant genotype namely, 3366.77 Kg. High significant differences in daily milk yield were reported in cows according the alleles type, AA cows genotypes achieved 13.97 Kg /day compared with the cows with GG genotype which achieved 8.82 kg/day. Milk components such as lactose and protein were affected significantly (P<0.01) by GHRL gene SNP, the highest rate of lactose and protein were noticed in cows with wild genotype namely, 5.45 and 3.60% respectively. Milk fat differed significantly (P<0.05) in cows depending on its genotype, the highest rate was recorded in cows with wild genotype (GG) namely 3.90%.
Evaluation of the Efficiency of Nerium oleander in the Phytoremediation of Lead Pollution by using Humic Acid

A field experiment was conducted in the lath house of the Agricultural Research and Experiment Station of the College of Agriculture in the governorate of Kirkuk-Iraq for the period from 1/3/2022 to 1/9/2002, to evaluate the efficiency of N.Oleander L.Album Plenum in the phytoremediation of lead (Pb) contaminated soil by adding Humic acid with three levels (0,5,10) ml.pot−1 of soil directly, the design of the experiment used is R.C. B.D. The results of the mechanisms and indicators of the efficiency of the nerium oleander in the extraction of heavy metals showed that the oleander plant with the assistance of humic acid has a high ability to absorb and accumulate the lead element very effectively, and the concentration of 10 ml.pot−1 of humic increasing the concentration of the Pb in the shoot and root system (30.71, 16.83 PPM) respectively, however the concentration of lead in the control plants was (25.32 and 14.36 PPM) respectively. Translocation factor TF>1 for all humic concentrations (1.82, 1.88, 1.77), respectively, and this indicates the high ability of the oleander to absorb and accumulate heavy elements. The concentration of 10 ml.pot−1 of humic acid gave the highest value of the bioaccumulation factor (BAF) 0.85, however it was 0.64 in the control plant. The concentration of 10 ml.pot−1 of humic was also superior in giving the highest value of the concentration index (CI), which ranged 1.27, and 1.06 in the control plant.
Determining Factors for Measuring the Benefits of Building Information Modelling (BIM)

Studies related to measuring the benefits of BIM become a new sensation. However, the reported values are highly inconsistent as there are no standards to benchmark the benefits according to different returning values. This paper presents a critical review of various factors that may affect the benefits to be measured from BIM. The review of previous studies aims to propose a novel classification approach for BIM benefits as a theoretical basis for further validation. The objective of this study is to explore BIM benefit returning factors. A total of 17 sources from peer-reviewed journals and various grey literature are included in the review with no time limit. From the underlying theories of Benefit Realisation Management (BRM) and the Benefit Identification Model, it is found that there are several factors that can be used to determine and classify the benefits of BIM for measurement purposes. An ontology of Benefit Determining Factor is proposed consisting of four (4) main dimensions, namely project context, stakeholder, time, and type using thematic analysis. The result is an attempt to make a theoretical breakthrough that introduces the existence of various BIM benefit influencing factors into the body of knowledge. These factors will provide researchers and analysts greater insight into more accurate measurement of BIM outcomes.
Kinetic Monte Carlo Simulations of Helium Cluster Nucleation in Tungsten with Preexisting Vacancies

The object kinetic Monte Carlo code Kinetic Simulations Of Microstructure Evolution (KSOME) was used to study the subsurface helium clustering behavior in tungsten as a function of temperature, helium implantation rate, and vacancy concentration. The simulations evaluated helium implantation fluxes from 1022 to 1026 m−2 · s−1 at temperatures from 473 to 1473 K for 100-eV helium ions implanted below tungsten surfaces and for vacancy concentrations between 1 and 50 parts per million. Such vacancy concentrations far exceed thermodynamic equilibrium values but are consistent with supersaturated concentrations expected during concurrent, or preexisting, neutron irradiation. The thermodynamics and kinetic parameters to describe helium diffusion and clustering are input to KSOME based on values obtained from atomistic simulation results. These kinetic Monte Carlo results clearly delineate two different regimes of helium cluster nucleation, one dominated by helium self-trapping at high implantation rates and lower temperatures and one where helium–vacancy trapping dominates the helium cluster nucleation at lower implantation rates and higher temperatures. The transition between these regimes has been mapped as a function of implantation rate, temperature, and vacancy concentration and can provide guidance to understand the conditions under which neutron irradiation effects may contribute to subsurface gas nucleation in tungsten plasma-facing components.