Miracle-dz/UniFAR-FTUs huggingface dataset:
A Load Balancing Model Based On Cloud Partitioning For The Public Cloud

Cloud computing is one of the new style of computing.Network applications are growing very fast and the use of cloud computing has increased rapidly. Some of the advantages of cloud computing are low cost, on demand services pay per use basis,on-line and accessibility of data.Along with the advantages there are someissues to be resolved in order to improve reliability of cloud environment. Load Balancing is the main concern in cloud computing. In cloud computing load balancing has an important impact on the performance. User satisfaction and cloud computing efficiency can be improved by good load balancing.Some of the loadsaredelay or network load, CPU load, memory capacity. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work.Various load balancing techniques have been proposed by many researches. In this section, we present literatures of several highly related research areas to load balancing model based on cloud partitioning for the public cloud. Load balancing in cloud computing was describe by Adler who introduced the tools and techniques commonly used for load balancing in the cloud. Load balancing in the cloud is still a new problem that needs new architectures to adapt to many changes.Chaczkoetal described the role that load balancing plays an important role in improving the performance and maintaining stability. Some of the load balancing algorithms are Round Robin, Equally Spread Current Execution Algorithm and Ant Colony algorithm.These algorithms applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.Some of the classical loadbalancing methods are similar to the allocation methodin the operating system.Some of the examples are Round Robin algorithm and the First Come First Served (FCFS) rules. The Round Robin algorithm is used here because it is fairly simple. Many researchers have invented different techniques for load balancing and distributing the workload in a cloud environment. Many tools has been developed and some of them are c-meter, Amizone EC-2,GrenchMarketc.V. Mahadevan, S. Aslanzadeh andC. Mcdermid in their paper "Availability and load balancing in cloud computing 2011" described the role that
Synchrotron-based investigations of the nature and impact of iron contamination in multicrystalline silicon solar cell materials

Synchrotron-based microprobe techniques were used to obtain precise and systematic information about the size distribution, spatial distribution, shape, electrical activity, and chemical states of iron-rich impurity clusters in multicrystalline silicon materials used for cost-effective solar cells. These experimentally observed properties of iron-rich clusters allow one to derive conclusions about the origins of iron contamination, the mechanisms for incorporating large amounts of Fe into mc-Si, quantitative information about the distribution of Fe in mc-Si and the impacts of such contamination on solar cell performance. Two distinct groups of iron-rich clusters have been identified in both materials: (a) the occasional large (diameter greater than or equal to 1 mu-m) particles, either oxidized and/or present with multiple other metal species reminiscent of stainless steels or ceramics, which are believed to originate from a foreign source such as the growth surfaces, production equipment, or feedstock, and (b) the more numerous, homogeneously distributed, and smaller iron silicide precipitates (dia. less than or equal to 800 nm, often < 100 nm), originating from a variety of possible formation mechanisms involving atomically dissolved iron in the melt or in the crystal. It was found that iron silicide nanoprecipitates account for bulk Fe concentrations as high as 1014-15 cm-3 and can have a large negative impact on device performance because of their homogeneous distribution along structural defects. The large (dia. greater than or equal to 1 mu-m) particles, while containing elevated amounts of metals, are low in spatial density and thus deemed to have a low direct impact on device performance, although they may have a large indirect impact via the dissolution of Fe, thus assisting the formation of iron silicide nanoprecipitates. These results demonstrate that it is not necessarily the total Fe content that limits mc-Si device performance, but the distribution of Fe within the material.
Evaluating the Effects of DEM Properties on the Spatial and Statistical Distribution of Hydrological Surface Attributes

EVALUATING THE EFFECTS OF DEM PROPERTIES ON THE SPATIAL AND STATISTICAL DISTRIBUTION OF HYDROLOGICAL SURFACE ATTRIBUTES Kathryn Woodrow Advisor: University of Guelph, 2014 Professor J.L. Lindsay Professor A.A. Berg Incorporating topographic detail within advanced hydrological models has been achieved using digital elevation models (DEMs). Although DEMs prove useful for a number of hydrological applications, they are often the end result of numerous processing steps that contain some amount of uncertainty. These uncertainties greatly influence DEM quality and can further propagate to DEM-derived attributes. This research examines the impacts of DEM grid resolution, elevation source data, and conditioning techniques on the spatial and statistical distribution of field-scale hydrological attributes. Variation in DEM grid resolution and elevation source data resulted in significant differences in the spatial and statistical distributions of variables. The resulting effects of applied conditioning techniques were closely linked to DEM grid resolution; differences in derived attributes among conditioning techniques significantly increased with grid resolution. Greater consideration of DEM conditioning is therefore required at finer-resolutions.
Using Tags to Select Stimuli in the Study of Music and Emotion

A wealth of literature on musical emotion exists, including investigation of the use of tags to classify musical emotions. However, the relationship between musical emotions and human annotated information is still unclear. Likewise, the understanding of the differences between induced emotion (also known as felt emotion) and perceived emotion (also known as expressed emotion) is at an early stage. In previous work, lists of songs labelled with one of the four basic emotion tags “happy”, “sad”, “angry” and “relaxed” were retrieved from Last.FM, and audio excerpts were fetched from 7Digital.com. In this study, we asked listeners to rate musical excerpts with the perceived or induced emotion fitting the excerpt. 80 excerpts (20 for each of the four emotions considered) were rated by 40 participants from various backgrounds and levels of musical expertise. The results show that in majority of the selected songs the tags agreed more closely with the ratings of perceived emotion than induced emotion. In addition, each induced emotion was highly correlated with its corresponding perceived emotion and induced anger can also be very distinct from its perceived ratings. However, the participants' emotional judgements were not related to measured cultural or musical factors.
Developments in Business Simulation & Experiential Exercises , Volume 15 , 1988 138 FORMAL PLANNING , SIMULATION TEAM PERFORMANCE , AND SATISFACTION : A REPLICATION

This study is a replication of an earlier study that found no difference in performance between simulation teams that conducted formal planning and those that did not. In contrast, the present study found that planning teams produced higher profitability on four measures. As in the previous study, little difference was found between planners and nonplanners in their satisfaction with the simulation and their teammates. The study concludes that the different findings between the two studies can be best explained by the low market potential parameters used for the simulation in the first study. Planning teams simply did not have an opportunity to excel. In the second study, expanded market potential provided conditions where the effects of formal planning were reflected in organizational performance.
A Burning Question: Does Arson Increase When Local House Prices Decline?

We construct panel data on house prices and the determined cause of 4.8 million individual fires in the United States between 1986 and 2010 to test whether decreases in local housing market prices coincided with increases in arson. Since some insured homeowners may attempt to disguise the actual cause of fire as accidental, we also examine how decreases in local house prices are associated with changes in the total number of fires and the probability of determined causes of accidental fires. For the sample period, our results suggest that declines in local house prices coincided with increases in arson, the total number of fires, and the probability that fires were determined to occur due to arson and misuse. We provide further support for the existence of such an effect with empirical evidence that the relation between declines in house prices and arson is stronger in states that allow mortgage lender recourse.
USING THE LITERATURE BASED DISCOVERY RESEARCH METHOD IN A CONTEXT OF BUILT ENVIRONMENT RESEARCH

For two disparate research groups, unaware of each other's work, one group can inadvertently solve a problem prevalent in the other. Without considering work from both groups together, such breakthroughs may remain undiscovered. The solution is literature based discovery (LBD), a method which involves investigation or search for novel hypotheses connecting work from two or more disparate contexts. However, LBD has predominantly been used to address medical problems, and its uptake outside medical research remains scanty. In the context of built environment research, there are countable studies that have claimed using LBD and moreover, they presented sparse details. On one hand, studies that have claimed using LBD as a research method seem to confuse it with traditional literature reviews, and on the other hand, even those that could have used LBD seem unaware that they used some kind of LBD-style analysis. Following the original principles of LBD, this paper presents an LBD-inspired research method and a demonstration of its applicability within a built environment research context. The findings indicate promising leads to encouraging LBD and elucidating several misconceptions surrounding its use in built environment research. It is hoped that this paper will encourage future research in built environment, like construction management research, to confidently use LBD appropriately and consciously.
Impact of potassium on the yield of lily (Lilium longifolium × Asiatic cv. Termoli) and antioxidant enzymes activity under drought stress

This study was conducted in two phases: 1) investigation of the effect of drought stress on relative water content and proline of leaf and 2) evaluate of the effect of drought stress and potassium concentrations in nutrient solution on the lily (Lilium longifolium × Asiatic cv. Termoli). Based on a completely randomized design in mixture of sand and perlite medium and in three levels of potassium (0, 6 and 12 mM), the growth indices, vase life of flowers, potassium concentration and antioxidant enzymes activity were measured. The results showed that the proline concentration increased about 7 times in the medium containing the moisture content of one third of field capacity (FC) compared to the FC treatment after 3 months in plant leaves. Lily vase life at 6 mM potassium increased 5.7 days relative to potassium-free conditions. Peroxidase activity in the nutrient solution with 12 mM potassium was significantly lower than the potassium-free conditions.
Why business angels reject investment opportunities: Is it personal?

A major focus of research on business angels has examined their decision-making processes and investment criteria. As business angels reject most opportunities they receive, this article explores the reasons informing such decisions. In view of angel heterogeneity, investment opportunities might be expected to be rejected for differing reasons. Two sources of data are used to examine this issue. Face-to-face interviews with 30 business angels in Scotland and Northern Ireland provided information on typical ‘deal killers’. This was complemented by an Internet survey that attracted responses from 238 business angels from across the UK. The findings confirm that the main reason for rejection relates to the entrepreneur/management team. However, angel characteristics do not explain the number of reasons given for opportunity rejection nor do they predict the reasons for rejecting investment opportunities. This could be related to the increasing trend for business angels to join organised groups which, in turn, leads to the development of a shared repertoire of investment approaches. We therefore suggest the concept of ‘communities-of-practice’ as an explanation for this finding.
Prediction of Rock Mass Failure-time of Geo-hazards

Can we predict failure-time of geo-hazards? Prediction of geo-hazards, for instance, rock mass failure, landslides, etc., is still a challenge to date in the rock mechanics field. Geo-hazards still pose a major threat to life and major loss in terms of economics. The focal point of our research is to predict failure-time of geo-hazards. Firstly, the authors evaluated the validity of the Inverse-velocity (INV) method to predict failure-time of rock mass and landslides. Secondly, two methods for computing predictions were also evaluated: (1) based on non-linear approximation (NLA), and (2) on the slope (gradient) to compute Tf, termed the SLO method, which will be described in detail in the paper. The concept of “safe” and “unsafe” predictions was developed to classify predictions. With this in mind, prediction of failure case histories and rock specimens in the laboratory is presented. It was realised that SLO is better than the INV method. 2 PREDICTION METHODS Using displacement u instead of strain e and differentiating both sides of Equation 1 with respect to time t:
Effects of Increases in Value Added Tax: A Dynamic CGE Approach

This paper analyses the effects of increases in value added tax (VAT) through a dynamic computable general equilibrium model. The database of the model encompasses a social accounting matrix (SAM) for the year 2010. All the important South African taxes are included in the SAM and the household sectors are disaggregated according to income deciles, with the top decile being further split into five groups. Five different simulations are performed, ranging from 1% increase in the VAT to 5% over the period 2012 to 2018. Our findings show that the percentage increase in VAT would not affect lower income households negatively if the higher government revenue flowed to the lower income households. For example, the 1% increase in the VAT rate impacts on the investment through the price of capital. The change in investment means that any adjustment in capital stock will affect the production and demand for labour that might impact on the standard of living of all income groups. The GDP increases slightly by 0.02173% in 2013 and reports a positive change for the period between 2013 and 2018. This shows that in the short run the GDP depends on other variables such as investment and consumption, which likewise are positively affected by this shock.
Modelling a water purification process for quality monitoring

This paper deals with a quality engineering problem introduced by ‘Waterlaboratorium Noord’ (WLN) situated at the Netherlands. In-terest lies in determining an optimal sampling frequency that provides suycient information on the water quality in a drinking water purifica-tion plant. The water purification plant that is studied consists of two aeration and filtration processes and a clear water reservoir where wa-ter is saved until distribution to households. One of the main processes during these filtration processes is iron removal. A stochastic model is proposed that describes the decreasing eects on iron concentration after the filtration processes by multiplicative eects. This model is combined with an ordinary dierential equation to model the amount of iron in the clear water reservoir that fluctuates due to the quality of the incoming filtrated water and the varying water demand. In this way the iron concentration levels in the dierent compart-ments of a water purification plant can be simulated. Range and fluc-tuations approximate those of the observed data. Hence a realistic benchmark for detecting anomalies is obtained. Keywords: quality engineering, sampling frequency, stochastic model, dierential equation, water quality
OPTIMIZATION OF DESIRABILITY FUNCTIONS AS A DNLP MODEL BY GAMS/BARON

Desirability functions of Derringer and Suich, one of the widely used approaches in multiresponse optimization, have nondifferentiable points in their formulations as a drawback. To solve the optimization problem of the overall desirability function, one way is to modify the individual desirability functions by approximation approaches and then to use the gradient based methods. Another way is to use the optimization techniques that do not employ the derivative information. In this study, we propose a new approach which is easy to implement and does not need assumptions like convexity and smoothness. Our approach is based on writing the optimization problem of the overall desirability function as a mixed‐integer nonlinear problem, and then putting a constraint on the integer variable to obtain a continuous formulation. The resulting problem is solved as a nonlinear model with discontinuous first order derivatives (DNLP) with Branch And Reduce Optimization Navigator (BARON), a new solver of the General Algebraic Modeling System (GAMS) for nonconvex optimization problems. The solutions obtained for two example problems are better than those of the others.
A Differential Evolution with Two Mutation Strategies and a Selection Based on an Improved Constraint-Handling Technique for Bilevel Programming Problems

Two mutation operators are used in the differential evolution algorithm to improve the diversity of population. An improved constraint-handling technique based on a comparison mechanism is presented, and then it is combined with the selection operator in the differential evolution algorithm to fulfill constraint handling and selection simultaneously. A differential evolution with two mutation strategies and a selection based on this improved constraint-handling technique is developed to solve bilevel programming problems. The simulation results on some linear and nonlinear bilevel programming problems show the effectiveness and efficiency of the proposed algorithm.
Linear interference and the initiation of extratropical stratosphere-troposphere interactions

variability is also negatively correlated with the amplitude of the wave pattern coherent with the large-scale climatological stationary wavefield; when the climatological stationary wavefield is amplified or attenuated, the stratospheric jet correspondingly weakens or strengthens. Here we quantify the importance of this linear interference effect in initiating stratosphere-troposphere interactions by performing a decomposition of the vertical wave activity flux using reanalysis data. The interannual variability in vertical wave activity flux in both the Northern and Southern Hemisphere extratropics is dominated by linear interference of quasi-stationary waves during the season of strongest stratosphere-troposphere coupling. Composite analysis of anomalous vertical wave activity flux events reveals the significant role of linear interference and shows that “linear” and “nonlinear” events are essentially independent. Linear interference is the dominant contribution to the vertical wave activity flux anomalies preceding displacement stratospheric sudden warmings (SSWs) while split SSWs are preceded by nonlinear wave activity flux anomalies. Wave activity variability controls the timing of stratospheric final warmings, and this variability is shown to be dominated by linear interference, particularly in the Southern Hemisphere. The persistence of the linear interference component of the vertical wave activity flux, corresponding to persistent constructive or destructive interference between the wave-1 component of climatological stationary wave and the wave anomaly, may help improve wintertime extratropical predictability.
R-RAP: A retrotransposon-based DNA fingerprinting technique in plants

The present research is aimed at testing a new retrotransposon-based marker, RAPD-retrotransposon amplified polymorphism (RRAP), which used the combination of RAPD and LTR retrotransposon primers. Banding patterns which are obtained by R-RAP primer combinations were different from amplicons by IRAP and RAPD, demonstrating detection of different genomic regions that are not covered by the other molecular marker systems. The used LTR rertrotransposons had been previously isolated from barley and wheat, hence, twelve accessions of three species of Triticum aestivum , Hordeum vulgare, and Aegilops tauschii were used to evaluate the efficiency of this method. Ten polymorphic R-RAP primer combinations generated 141 loci, of which 114 were polymorphic. The studied species were divided with 66 % similarity into 3 clusters according to their species and even some subclusters within species. High polymorphism was observed in both between and within species. Polymorphic information content (PIC) ranged from 0.28 to 0.40. Reproducibility of each primer combination, tested 4 times in different conditions, was approximately 100%. Agarose gel was used to separate PCR products and showed good resolving ability. We showed that R-RAP can be used as an efficient marker as with other retrotransposon-based markers and can be applied in a similar way for DNA fingerprinting, genetic diversity, genome mapping, and gene tagging in plants. This method also represents a departure for IRAP and RAPD limitations.
A multilayer approach and its application in modeling QGNSea V1.0: a local gravmetric quasi-geoid model over the North Sea

Abstract. A multilayer approach is set up for local gravity field modeling based on the idea of multi-resolution representation merging heterogeneous gravity data. Different layers of Poisson wavelets' grids are formed to recover the signals at various levels, where the shallow and deep layers mainly capture the short- and long-wavelength signals, respectively. The depths of these layers beneath the topography are linked to the locations that the anomaly sources locate, estimated by the wavelet decomposition and power spectrum analysis. For testing the performance of this approach, a gravimetric quasi-geoid over the North Sea in Europe called QGNSea V1.0 is computed and compared with other existing models. The results show that the multilayer approach outperforms the traditionally used single-layer one in high-frequency bands, and the former fit the gravity data better, especially in regions with a tendency toward topographical variation. The evaluation with GPS/leveling data show the accuracies of QGNSea V1.0 modeled from the multilayer approach are improved by 0.3 cm, 0.6 cm and 0.8 cm in the Netherlands, Belgium and parts of Germany, respectively, compared to the original solution computed from the single-layer approach. Further validation with existing models show QGNSea V1.0 has the best quality, which may be beneficial for studying the ocean circulation between the North Sea and neighbouring waters.