Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost Efficiency Optimization of 5G Wireless Backhaul Networks

The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the ever-increasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper, we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.
Predictive control of thermal Power Plants

This work presents the results of a project aimed at verifying the applicability of industrial model predictive control (MPC) to thermal Power Plants. The research is motivated by the need to improve the efficiency of power plants so as to cope with the high levels of competition induced by the deregulation of the energy market. A detailed plant simulator, already used for operators training and controllers tuning, is coupled to an industrial software package implementing the dynamic matrix control algorithm. The achieved results witness the great potentialities of MPC, with respect to classical decentralized schemes, in terms of economical savings, reduction of pollutants, improved flexibility, easier tuning and better documentation. Copyright © 2004 John Wiley & Sons, Ltd.
Evolution of the Color-Magnitude Relation in High-Redshift Clusters: Early-Type Galaxies in the Lynx Supercluster at z ~ 1.26

Color-magnitude relations (CMRs) have been derived in two high-redshift clusters, RX J0849+4452 and RX J0848+4453 (with redshifts of z = 1.26 and 1.27, respectively), that lie in the highest redshift cluster superstructure known today, the Lynx Supercluster. The CMR was determined from ACS imaging in the WFC F775W (i775) and F850LP (z850) filters combined with ground-based spectroscopy. Early-type cluster candidates have been identified according to the Postman et al. morphological classification. In both clusters the bright red early-type population defines a tight CMR very similar in color, although the two clusters present different X-ray luminosities and shapes. The elliptical galaxy CMRs in RX J0849+4452 and RX J0848+4453 show an intrinsic (i775 - z850) color scatter of 0.026 ± 0.012 and 0.024 ± 0.015 mag, respectively, within 2' (~1 Mpc at z = 1.26) from the cluster X-ray emission centers. Simple modeling of the scatters using stellar population models from Bruzual and Charlot gives a mean luminosity-weighted age > 2.5 Gyr (zf > 2.75) and > 2.6 Gyr (zf > 2.8) for ellipticals in RX J0849+4452 and RX J0848+4453, respectively. S0 galaxies follow the elliptical CMR; they show larger scatters about the CMR. The intrinsic scatter decreases and the CMR slopes are steeper at smaller radii, within both clusters. Within 1' from the cluster X-ray emission centers, elliptical CMR scatters imply a mean luminosity-weighted age > 3.2 Gyr (zf > 3.7). We conclude that old stellar populations in cluster elliptical galaxies are already in place at z = 1.26, both in the more evolved cluster RX J0849+4452 and in its less evolved companion RX J0848+4453. Even at a look-back time of 9 Gyr, in the early merging and buildup of massive clusters, the bulk of the stellar content of the bright elliptical galaxy population was in place—apparently formed some 2.5 Gyr earlier at z ~ 3.
Short- and long-term prognosis for very old stroke patients. The Copenhagen Stroke Study.

BACKGROUND AND PURPOSE The very old are expected to become a growing part of the stroke population in the industrialised part of the world. The aims of this study were to evaluate clinical characteristics of patients aged 85 years or more at stroke onset and to investigate very old age as an independent predictor of short- and long-term outcome. METHODS In the community-based Copenhagen Stroke Study we recorded admission clinical characteristics in 1197 consecutive stroke patients. Patients were stratified according to age groups on admission. Follow-up was performed at a mean of 7 years after stroke onset. By way of multiple logistic regression and survival analyses very old age was independently related to short- and long-term mortality and nursing home placement independent of other clinical characteristics. RESULTS 16% of patients were 85 years or older at the time of stroke onset. More of the very old were women (75% versus 50%, P<0.0001), living alone (84% versus 54%, P<0.0001), had atrial fibrillation (37% versus 15%, P<0.0001), had pre-existing disability (29% versus 22%, P = 0.04), and had more severe strokes (Scandinavian Stroke Scale score 31 versus 37 points, P = 0.004). Fewer very old had hypertension (25% versus 34%, P = 0.02) and diabetes (14% versus 22%, P = 0.01). In adjusted multiple regression models, very old age predicted short-term mortality (OR 2.5; 95% CI 1.5-4.2), and discharge to nursing home or in-hospital mortality (OR 2.7; 95% CI 1.7-4.4). Five years after stroke very old age predicted mortality or nursing home placement (OR 3.9; 95% CI 2.1-7.3), and long-term mortality (HR 2.0; 95% CI 1.6-2.5). However, other factors such as onset stroke severity, pre-existing disability and atrial fibrillation were also significant independent predictors of prognosis after stroke. CONCLUSIONS In this study very old age per se was a strong predictor of outcome and mortality after stroke. Apart from very old age, factors such as prestroke medical and functional status, and onset stroke severity should be taken into consideration when planning treatment and rehabilitation after stroke.
The density of integral points on hypersurfaces of degree at least four

Let $f$ be a polynomial of degree at least four with integer-valued coefficients. We establish new bounds for the density of integer solutions to the equation $f=0$, using an iterated version of Heath-Browns $q$-analogue of van der Corput's method of exponential sums.
Metrics for characterizing complexity of network traffic

We propose the study of different entropy metrics as measures of complexity of network traffic. Based on examples with synthetic data as well as different measurement traces we show that these metrics provide a new way of characterizing and studying traffic behaviour. Especially multiscale entropy analysis appears to be a powerful tool, capable of uncovering rich structures on different time-scales from different traffic measurements. We also discuss potential uses of these tools in design and validation of traffic models. Examples of potential application areas for these metrics to be studied in the future include traffic characterization, classification and anomaly detection.
Portfolio Choices and Asset Prices: The Comparative Statics of Ambiguity Aversion

We investigate the comparative statics of "more ambiguity aversion" as defined by Klibanoff, Marinacci and Mukerji (2005) in the context of the static two-asset portfolio problem. It is not true in general that more ambiguity aversion reduces the demand for the uncertain asset. We exhibit some sufficient conditions to guarantee that, ceteris paribus, an increase in ambiguity aversion reduces the demand for the ambiguous asset, and raises the equity premium. For example, this is the case when the set of plausible distributions of returns can be ranked according to the monotone likelihood ratio order. We also show how ambiguity aversion distorts the price kernel in the alternative portfolio problem with complete markets for contingent claims.
A Smoothing Evolutionary Algorithm with Circle Search for Global Optimization

There are many global optimization problems arisen in various fields of applications. It is very important to design effective algorithms for these problems. However, one of the key drawbacks of the existing global optimization methods is that they are not easy to escape from the local optimal solutions and can not find the global optimal solution quickly. In order to escape from the local optimal solutions and find the global optimal solution fast, first, a smoothing function, which can flatten the landscape of the original function and eliminate all local optimal solutions which are no better than the best one found so far, is proposed. This can make the search of the global optimal solution much easier. Second, to cooperate the smoothing function, a tailor-made search scheme called circle search is presented, which can quickly jump out the flattened landscape and fall in a lower landscape quickly. Third, a better solution than the best one found so far can be found by local search. Fourth, a crossover operator is designed based on uniform design. Based on these, a smoothing evolutionary algorithm for global optimization is proposed. At last, the numerical simulations for eight high dimensional and very challenging standard benchmark problems are made. The performance of the proposed algorithm is compared with that of nine evolutionary algorithms published recently. The results indicate that the proposed algorithm is statistically sound and has better performance for these test functions.
The Gap between Individual Perception and Compliance: A Qualitative Follow-Up Study of the Surgical Safety Checklist Application

Background “The Surgical Safety Checklist (SSC) is important, but we don’t use it adequately” is a well-suited statement that reflects the SSC's application in hospitals. Our aim was to follow up on our initial study on compliance (2014) by analysing differences between individual perception and compliance with the SSC. Methods We conducted a follow-up online survey to assess healthcare professionals’ individual perception of, as well as satisfaction and compliance with the SSC three years following its thorough implementation. Results 171 (19.5%) of 875 operating team members completed the online survey. 99.4% confirmed using the SSC. Self-estimated subjective knowledge about the intention of the checklist was high, whereas objective knowledge was moderate, but improved as compared to 2014. According to an independent audit the SSC was used in 93.1% of all operations and among the SSCs used the completion rate was 57.2%. The use of the SSC was rated as rather easy [median (IQR): 7 (6–7)], familiar [7 (6–7)], generally important [7 (7–7)], and good for patients [7 (6–7)] as well as for employees [7 (7–7)]. Only comfort of use was rated lower [6 (5–7)]. Conclusion There is a gap between individual perception and actual application of the SSC. Despite healthcare professionals confirming the importance of the SSC, compliance was moderate. The introduction of SSCs in the health care sector remains a constant challenge and requires continuous re-evaluation as well as a sensible integration into existing workflows in hospitals.
Identification Of Deletion Mutants Of Inositol Kinases And Phosphatases Hypersensitive To Valproate

IDENTIFICATION OF DELETION MUTANTS OF INOSITOL KINASES AND PHOSPHATASES HYPERSENSITIVE TO VALPROATE by WELLEWATTA MUDIYANSELAGE MANOJ SENAKA BANDARA MAY 2010 Advisor: Dr. Miriam Greenberg Major: Biological Sciences Degree: Master of Science Bipolar disorder (BD) is a chronic psychiatric illness affecting at least 1% of the world population. BD is ranked as the sixth greatest cause of death or disability globally. The cause of BD is unknown. Although the anticonvulsant valproate (VPA) is widely used as a mood stabilizer to treat BD, VPA is not completely effective and causes numerous side effects. Hence, it is important to develop more effective drugs with fewer side effects to treat BD. However, drug development is hampered by the lack of knowledge of the therapeutic mechanisms of action of current drugs used to treat BD. VPA depletes inositol in yeast and mammals. The current study was undertaken to determine whether genes affecting inositol synthesis lead to VPA sensitivity.1D-myo-inositol-3 phosphate synthase (MIPS) encoded by INO1 converts glucose-6-phosphate to L-myo-inositol-3-phosphate (MIP) in the de novo synthesis of inositol. The activation of INO1 expression is positively
Unraveling the Pathogenesis of MDS: The NLRP3 Inflammasome and Pyroptosis Drive the MDS Phenotype

Myelodysplastic syndromes (MDS) are characterized by bone marrow cytological dysplasia and ineffective hematopoiesis in the setting of recurrent somatic gene mutations and chromosomal abnormalities. The underlying pathogenic mechanisms that drive a common clinical phenotype from a diverse array of genetic abnormalities have only recently begun to emerge. Accumulating evidence has highlighted the integral role of the innate immune system in upregulating inflammatory cytokines via NF-κB activation in the pathogenesis of MDS. Recent investigations implicate activation of the NLRP3 inflammasome in hematopoietic stem/progenitor cells as a critical convergence signal in MDS with consequent clonal expansion and pyroptotic cell death though caspase-1 maturation. Specifically, the alarmin S100A9 and/or founder gene mutations trigger pyroptosis through the generation of reactive oxygen species leading to assembly and activation of the redox-sensitive NLRP3 inflammasome and β–catenin, assuring propagation of the MDS clone. More importantly, targeted inhibition of varied steps in this pathway restore effective hematopoiesis. Together, delineation of the role of pyroptosis in the clinical phenotype of MDS patients has identified novel therapeutic strategies that offer significant promise in the treatment of MDS.
CDMA downlink code acquisition performance in frequency-selective fading channels

In this paper, the performance of some code acquisition schemes are studied in the downlink of DS-CDMA systems. The conventional matched filter, the minimum variance method, the MUSIC algorithms and the eigenvector method based delay acquisition schemes are studied in a frequency-selective fading channel. The three last algorithms are based on different estimation schemes for the inverse of the sample-covariance matrix. Based on the results, the minimum variance based method can be used to improve the acquisition performance in comparison to the conventional non-coherent matched filter method. The MUSIC algorithm becomes useless in highly loaded systems. The best performance, however, was obtained by using a longer despreading interval in the conventional delay estimator. Unfortunately, it would require either unmodulated pilot channel or a control channel with lower data rate, which may not be available in all applications.
Bayesian Semiparametric Dynamic Frailty Models for Multiple Event Time Data

Summary Many biomedical studies collect data on times of occurrence for a health event that can occur repeatedly, such as infection, hospitalization, recurrence of disease, or tumor onset. To analyze such data, it is necessary to account for within‐subject dependency in the multiple event times. Motivated by data from studies of palpable tumors, this article proposes a dynamic frailty model and Bayesian semiparametric approach to inference. The widely used shared frailty proportional hazards model is generalized to allow subject‐specific frailties to change dynamically with age while also accommodating nonproportional hazards. Parametric assumptions on the frailty distribution are avoided by using Dirichlet process priors for a shared frailty and for multiplicative innovations on this frailty. By centering the semiparametric model on a conditionally conjugate dynamic gamma model, we facilitate posterior computation and lack‐of‐fit assessments of the parametric model. Our proposed method is demonstrated using data from a cancer chemoprevention study.
Auditor choice, retained ownership, and earnings disclosure for IPO firms: Further evidence

Purpose – The purpose of this research is to provide further evidence on the association between the IPO signaling mechanisms (i.e. retained ownership, auditor choice, and earnings forecast) by using a less restrictive sample and by performing additional empirical tests. Design/methodology/approach – Single equations are used as the baseline approach to estimate the three models. In addition, Copley and Douthett's 2002 simultaneous equation systems are applied to examine whether the results remain the same. Moreover, ranked values of the risk proxies of IPOs are derived and general least squares are run on these ranked variables. Findings – Findings indicate that auditor reputation and retained ownership are not substitute signals. It is observed that as firm risk increases, entrepreneurs are more likely to retain higher ownership to signal firm value. In addition, contended that positive earnings disclosure before IPO is not associated with retained ownership in a significant manner. An analysis of the economic implication of the results suggests that findings are more representative. Research limitations/implications – In this study the risk measures used (as well as those used in other studies) may not adequately proxy for offering firm risk. Additionally, the sample is restricted by missing values of the retained ownership variable. Further study can expand the sample using retained ownership obtained from other data sources. A study employing alternative approaches to control for the supply-side effect of firm risk could be also productive. Practical implications – Findings are of particular interest to firms that are planning to go to the public. They need to evaluate the benefit and cost of selecting a particular information system in signaling firm value to the market. Originality/value – Using a larger sample, comprehensive testing periods, and ranked risk proxies contribute to the literature on evaluating singling mechanisms of IPOs.
Investigations into children's participation and agency : working towards change in classroom practices and cultures

My research broadly has two strands. The first has contributed to work on young children’s use of representational practices, showing how children’s drawing activities are grounded in intention and meaning-making in social contexts. In drawing attention to what children do and how they actively use visual representation it added new insights to theoretical work on learning in a socio-cultural framework where learning is understood as participation in social practices; as joint activity, mediated by other people and cultural activities and artefacts. The second strand was based in these understandings of learning but explored, and also developed communicative practices in classrooms and children’s role in decision-making. The research investigated the extent of the children’s democratic engagement and the development of a more democratically participatory space where they can exercise agency both as learners and as participants in democratic communities. This work also contributed in new ways to children’s agency as researchers. I argue that these two strands are inter-related, a position also represented in my sole-authored book (Cox 2011). In relation to development in pedagogical practices across important areas of teachers’ work, including classroom interaction and communication, curriculum and assessment, I argue in this book that principles can be based in socio-cultural ways of theorising learning, as well as in democratic values, suggesting more ‘participative’ (empowered) participation. Using research evidence and argument I show how changes in classroom practices can be appropriated by conventional theorisations of learning (person-to-person accounts) and existing cultures of ‘schooling’, limiting children’s learning. I explore the tensions that arise, especially in a wider context of performance-driven, market-led policies. An underlying theme is that a shift towards socio-cultural understandings of learning might help to resolve these tensions and achieve changes in practices in possibly transformative ways, creating a more educational culture and embracing the idea of children as agents of cultural change. I aim, in the commentary, to convey the thematic connections and contributions across my work in relation to my role as teacher educator as well as researcher.
The Coming Transformation of Continental European Banking?

This paper first provides a broad overview of current conditions in the banking industry in some of the countries that will participate in European economic and monetary union. It then goes on to identify a number of important forces for change. Some of these are global in nature, but likely to have a particularly strong impact in continental Europe, while others (such as the introduction of the euro) are distinctively European. The paper then notes just how little restructuring has actually taken place in continental European banking in the last decade and offers some explanations. The conclusion reached is that the banking sector in continental Europe may now be about to enter a period of sharply increased competition. This will pose challenges for national regulators and other policy-makers concerned with the maintenance of financial stability.
Stock market prediction using machine learning techniques

The main objective of this research is to predict the market performance of Karachi Stock Exchange (KSE) on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. The attributes used in the model includes Oil rates, Gold & Silver rates, Interest rate, Foreign Exchange (FEX) rate, NEWS and social media feed. The old statistical techniques including Simple Moving Average (SMA) and Autoregressive Integrated Moving Average (ARIMA) are also used as input. The machine learning techniques including Single Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are compared. All these attributes are studied separately also. The algorithm MLP performed best as compared to other techniques. The oil rate attribute was found to be most relevant to market performance. The results suggest that performance of KSE-100 index can be predicted with machine learning techniques.
Extracting conformational structure information of benzene molecules via laser-induced electron diffraction

We have measured the angular distributions of high energy photoelectrons of benzene molecules generated by intense infrared femtosecond laser pulses. These electrons arise from the elastic collisions between the benzene ions with the previously tunnel-ionized electrons that have been driven back by the laser field. Theory shows that laser-free elastic differential cross sections (DCSs) can be extracted from these photoelectrons, and the DCS can be used to retrieve the bond lengths of gas-phase molecules similar to the conventional electron diffraction method. From our experimental results, we have obtained the C-C and C-H bond lengths of benzene with a spatial resolution of about 10 pm. Our results demonstrate that laser induced electron diffraction (LIED) experiments can be carried out with the present-day ultrafast intense lasers already. Looking ahead, with aligned or oriented molecules, more complete spatial information of the molecule can be obtained from LIED, and applying LIED to probe photo-excited molecules, a “molecular movie” of the dynamic system may be created with sub-Ångström spatial and few-ten femtosecond temporal resolutions.