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Dias, J. Cabral, L. Golestan, A. Jundi, L. Nassar, F. Sattar, F. Karray, M. Kamel et al. Nassar, A. Jundi, K. Golestan, F. Bio-inspired Navigation of Mobile Robots. Lei Wang, Simon X. Yang, Mohammad Biglarbegian. Ahmed M. Elmogy, Alaa M. Khamis, Fakhri Karray. Mohammad Hossein Mirabdollah, Baerbel Mertsching. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions.
Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions.
Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. To test this hypothesis, studies 1—3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. Accurate predictions for the LHC made easy. The data recorded by the LHC experiments is of a very high quality.
To get the most out of the data, precise theory predictions , including uncertainty estimates, are needed to reduce as much as possible theoretical bias in the experimental analyses. Recently, significant progress has been made in computing Next-to-Leading Order NLO computations, including matching to the parton shower, that allow for these accurate , hadron-level predictions.
Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping Gaussian distributions, in order to train a CART classifier to provide the prediction.
The threshold that separates the short jobs from the long jobs is determined during the ev Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine SVM are now receiving more and more attentions in this research field.
Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed.
In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.
Hounsfield unit density accurately predicts ESWL success. Extracorporeal shockwave lithotripsy ESWL is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones.
In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. Of these met study inclusion with accessible CT scans and stones ranging from mm. Follow-up imaging demonstrated stone freedom in The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different A new, accurate predictive model for incident hypertension.
Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures Bayesian calibration of power plant models for accurate performance prediction. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance.
In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction.
The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load.
The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions. After asserting that public institutions should not provide training for nonexistent jobs, this paper reviews problems associated with the accurate prediction of future manpower needs.
The paper reviews the processes currently used to project labor force needs and notes the difficulty of accurately forecasting labor market "surprises,"…. Towards more accurate and reliable predictions for nuclear applications. The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models.
Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications.
In the present contribution, the reliability and accuracy of recent nuclear theories are discussed for most of the relevant quantities needed to estimate reaction cross sections and beta-decay rates, namely nuclear masses, nuclear level densities, gamma-ray strength, fission properties and beta-strength functions.
It is shown that nowadays, mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenogical inputs in the prediction of nuclear data. While fundamental nuclear physicists keep on improving state-of-the-art models, e. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.
The primary study population consisted of normotensive individuals aged years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania SHIP. The initial set was randomly split into a training and a testing set.
We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs and urinary albumin concentrations [area under the receiver operating characteristic AUC 0.
Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice. Accurate prediction of the enthalpies of formation for xanthophylls.
Within the training set, MM4 predictions are more accurate than those obtained using AE and GE; however a systematic underestimation was observed in the extended systems. In facilities that process special nuclear material SNM it is important to account accurately for the fissile material that enters and leaves the plant.
Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i. Generalized Geometry Holdup GGH , primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations.
Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information.
To use. Improving medical decisions for incapacitated persons: does focusing on " accurate predictions " lead to an inaccurate picture? The Patient Preference Predictor PPP proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate , individualized statistical model has significant obstacles.
Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate , and authoritative than is commonly assumed.
Full Text Available Upon infection of a new host, human immunodeficiency virus HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1—2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics.
We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus SIV. First, we found that the mode of virus production by infected cells budding vs. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production.
Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral. A highly accurate predictive -adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination.
However, the commonly-used direct calculation DC method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge SOC. To enhance the E RDE accuracy, this paper presents a battery energy prediction EP method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized.
Three EP approaches with different model parameter updating routes are introduced, and the predictive -adaptive energy prediction PAEP method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles.
Predicting future forestland area: a comparison of econometric approaches. Predictions of future forestland area are an important component of forest policy analyses. In this article, we test the ability of econometric land use models to accurately forecast forest area. We construct a panel data set for Alabama consisting of county and time-series observation for the period to We estimate models using restricted data sets-namely, Protein disordered regions are segments of a protein chain that do not adopt a stable structure.
Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction , protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction.
Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions.
To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure.
Feedforward signal prediction for accurate motion systems using digital filters. A positioning system that needs to accurately track a reference can benefit greatly from using feedforward. When using a force actuator, the feedforward needs to generate a force proportional to the reference acceleration, which can be measured by means of an accelerometer or can be created by.
Full Text Available Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Subjects were also assessed with the Activities-specific Balance Confidence, Falls Efficacy Scale-International, and Multiple Sclerosis Walking Scale questionnaires, the Expanded Disability Status Scale, Timed Foot Walk, and computerized dynamic posturography and recorded their falls daily for the following 6 months with calendars.
The ability of baseline assessments to predict future falls was compared using receiver operator curves and logistic regression. All tests individually provided similar fall prediction area under the curve AUC 0. A fall in the past year was the best predictor of falls AUC 0. Simply asking people with MS if they have fallen in the past year predicts future falls and injurious falls as well as more complex, expensive, or time-consuming approaches.
Can phenological models predict tree phenology accurately under climate change conditions? The onset of the growing season of trees has been globally earlier by 2. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas.
Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: 1 one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and 2 two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year.
So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees.
An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay. Prediction of future asset prices. This paper attempts to incorporate trading volumes as an additional predictor for predicting asset prices. By examining the average lengths of the prediction intervals found by using the composite indices of the Malaysia stock market for the period to , we found that the value 2 appears to be a good choice for l.
With the omission of the trading volume in the vector r t , the corresponding prediction interval exhibits a slightly longer average length, showing that it might be desirable to keep trading volume as a predictor. When the probability differs from 0 or 1 by less than 0.
Thus the above probability has a good potential of being used as a market indicator in technical analysis. Designers predict a bright future. As power plant designers and builders, there is a bright future for the industry. The demand for electricity will continue to grow, and the need for new plants will increase accordingly. But companies that develop and supply these plants must adapt to new ways of doing business if they expect to see the dawn of this new age.
Several factors will have a profound effect on the generation and use of electricity in future years. Instant communications now reach all corners of the globe, making people everywhere aspire to a higher standard of living. The economic surge needed to satisfy these appetites will, in turn, be fed by a network of suppliers who are themselves restructuring to serve global markets, unimpeded by past nationalistic barriers to trade.
The strong correlation between economic progress and the growing demand for electricity is well recognized. A ready supply of affordable electricity is a necessary underpinning for any economic expansion. As economies advance and jobs increase, electric demand grows geometrically, fueled by an ever-improving quality of life. Coupled with increasing demand is the worldwide trend toward privatization of the generation industry. The reasons may vary in different parts of the world, but the effect is the same--companies are battling intensely for the right to build or purchase generating facilities.
Those companies, like the industry they serve, are themselves in a period of transition. Once a closed, monopolistic group of owners in a predominantly services-based market, they are, thanks to competitive forces, being driven steadily toward a product-based structure. Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by The aim of this study was to predict coronary artery disease using data mining algorithms.
Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc. The data used in this study was taken from several valid databases including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced.
Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Predicting the future trend of popularity by network diffusion. Conventional approaches to predict the future popularity of products are mainly based on extrapolation of their current popularity, which overlooks the hidden microscopic information under the macroscopic trend.
Here, we study diffusion processes on consumer-product and citation networks to exploit the hidden microscopic information and connect consumers to their potential purchase, publications to their potential citers to obtain a prediction for future item popularity. By using the data obtained from the largest online retailers including Netflix and Amazon as well as the American Physical Society citation networks, we found that our method outperforms the accurate short-term extrapolation and identifies the potentially popular items long before they become prominent.
Predicting the future of sports organizations. Full Text Available The current crisis of sport in Serbia justifies its prediction of real potential future of sport organizations. The results of structural analysis showed that experimental sample based its vision on the staff as a determinant of the system, which is providing creativity as a characteristic of the organizational culture of the club.
Control subsample of respondents could indicate some characteristic variables to predict the future of clubs, but can't say a clear prediction system based on a long sequence of reasoning. We can conclude that the mentioned two sub-samples are differerent in terms of the ability to orient to predict the future of their clubs on the basis of assessment of the key variables that shape the future scenarios.
Predict SNP: robust and accurate consensus classifier for prediction of disease-related mutations. Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization.
Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools.
The six best performing tools were combined into a consensus classifier Predict SNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools.
A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier Predict SNP and annotations from the Protein Mutant Database and the UniProt database. Can numerical simulations accurately predict hydrodynamic instabilities in liquid films?
Understanding the dynamics of hydrodynamic instabilities in liquid film flows is an active field of research in fluid dynamics and non-linear science in general. Numerical simulations offer a powerful tool to study hydrodynamic instabilities in film flows and can provide deep insights into the underlying physical phenomena. However, the direct comparison of numerical results and experimental results is often hampered by several reasons. For instance, in numerical simulations the interface representation is problematic and the governing equations and boundary conditions may be oversimplified, whereas in experiments it is often difficult to extract accurate information on the fluid and its behavior, e.
In this contribution we present the latest results of our on-going, extensive study on hydrodynamic instabilities in liquid film flows, which includes direct numerical simulations, low-dimensional modelling as well as experiments.
The major focus is on wave regimes, wave height and wave celerity as a function of Reynolds number and forcing frequency of a falling liquid film. Specific attention is paid to the differences in numerical and experimental results and the reasons for these differences. Predicting accurate absolute binding energies in aqueous solution. Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic.
In this paper I summarize some of the many factors that could easily contribute kcal While I focus on binding free energies in aqueous solution the approach also applies with minor adjustments to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent Thermal fatigue is a safety related issue in primary pipework systems of nuclear power plants. Life extension of current reactors and the design of a next generation of new reactors lead to growing importance of research in this direction.
The thermal fatigue degradation mechanism is induced by temperature fluctuations in a fluid, which arise from mixing of hot and cold flows. Accompanied physical phenomena include thermal stratification, thermal striping, and turbulence . Current plant instrumentation systems allow monitoring of possible causes as stratification and temperature gradients at fatigue susceptible locations .
However, high-cycle temperature fluctuations associated with turbulent mixing cannot be adequately detected by common thermocouple instrumentations. For a proper evaluation of thermal fatigue, therefore, numerical simulations are necessary that couple instantaneous fluid and solid interactions. In this work, a strategy for the numerical prediction of thermal fatigue is presented. For the development of the computational approach, a classical test case for the investigation of thermal fatigue problems is studied, i.
Due to turbulent mixing of hot and cold fluids in two perpendicularly connected pipes, temperature fluctuations arise in the mixing zone downstream in the flow. Subsequently, these temperature fluctuations are also induced in the pipes. The stresses that arise due to the fluctuations may eventually lead to thermal fatigue.
In the first step of the applied procedure, the temperature fluctuations in both fluid and structure are calculated using the CFD method. Subsequently, the temperature fluctuations in the structure are imposed as thermal loads in a FEM model of the pipes. A mechanical analysis is then performed to determine the thermal stresses, which are used to predict the fatigue lifetime of the structure.
Change in BMI accurately predicted by social exposure to acquaintances. Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives.
In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information.
The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information.
Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.
Full Text Available Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. I argue that in the first case we have a form of analogue reasoning, which predicts the past in order to envision the future Towards accurate performance prediction of a vertical axis wind turbine operating at different tip speed ratios.
Furthermore, the domain size needs. Three Paradoxes of the Future Prediction. Full Text Available The paper is devoted to the issue of predicting the future. While creating the future image of the mankind as a whole, and Russia in particular, extrapolated some 50 or years ahead, such cultural forms as religion, philosophy, education and art make their significant impact.
However, philosophy plays a special role of critical methodology in coordinating the futurological efforts. It works as a tuning fork that tunes up the orchestra of various sciences and other forms of social consciousness. Being dialectical, philosophers find out and analyze the contradictions — paradoxes, antinomies, and aporias - involved in such activities as prophesizing, prognosticating, predicting and foreseeing.
On the basis of the retrospective analysis, the author considers the most significant paradoxes facing the futurologists engaged in predicting the general course of historic events; the paradoxes being denoted as follows: the antinomy of academic ignorance, paradox of newness and paradox of an emergent effect. Nevertheless, global prognoses are highly valued, widely discussed and always in demand in society due to the purposeful human intellect.
Prediction of future subsurface temperatures in Korea. The importance of climate change has been increasingly recognized because it has had the huge amount of impact on social, economic, and environmental aspect. For the reason, paleoclimate change has been studied intensively using different geological tools including borehole temperatures and future surface air temperatures SATs have been predicted for the local areas and the globe.
Future subsurface temperatures can have also enormous impact on various areas and be predicted by an analytical method or a numerical simulation using measured and predicted SATs, and thermal diffusivity data of rocks. SATs have been measured at 73 meteorological observatories since in Korea and predicted at same locations up to the year of Measured SATs at the Seoul meteorological observatory increased by about 3.
Predicted SATs have 4 different scenarios depending on mainly CO2 concentration and national action plan on climate change in the future. The hottest scenario shows that SATs in Korea will increase by about 5. In addition, thermal diffusivity values have been measured on 2, rock samples collected from entire Korea.
Data pretreatment based on autocorrelation analysis was conducted to control high frequency noise in thermal diffusivity data. At Seoul, the results of predictions show that subsurface temperatures will increase by about 5. We are now proceeding numerical simulations for subsurface temperature predictions for 73 locations in Korea.
Accurate information regarding crack growth times and structural strength as a function of the crack size is mandatory in damage tolerance analysis. Various equivalent stress intensity factor SIF models are available for prediction of mixed mode fatigue life using the Paris' law.
Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempts to outline models that would provide accurate and conservative life predictions. Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related.
Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same.
In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies. Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change.
However, previous work on load prediction lacks to consider a wider set of possible data sources. By presenting these results we hope to improve the modeling of building loads and algorithms for Demand Response planning Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure. There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate HR during intermittent sport.
The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost VO2 and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males HR and VO2 were directly measured during basketball games 6 male, Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations.
These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport. Accurate and dynamic predictive model for better prediction in medicine and healthcare. Information and communication technologies ICTs have changed the trend into new integrated operations and methods in all fields of life.
The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance.
In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury TBI datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
Towards cycle- accurate performance predictions for real-time embedded systems. Triantafyllidis, K. In this paper we present a model-based performance analysis method for component-based real-time systems, featuring cycle- accurate predictions of latencies and enhanced system robustness. The method incorporates the following phases: a instruction-level profiling of SW components, b modeling the. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated.
Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. In all, participants from 32 different countries responded assessing real scenarios and outcomes. Accurate wavelength prediction of photonic crystal resonant reflection and applications in refractive index measurement.
The importance of accounting for material dispersion in order to obtain accurate simulation results is highlighted, and a method for doing so using an iterative approach is demonstrated. Furthermore, an application for the model is demonstrated, in which the material dispersion In the past decade, photonic crystal resonant reflectors have been increasingly used as the basis for label-free biochemical assays in lab-on-a-chip applications.
In both designing and interpreting experimental results, an accurate model describing the optical behavior of such structures Here, an analytical method for precisely predicting the absolute positions of resonantly reflected wavelengths is presented. The model is experimentally verified to be highly accurate using nanoreplicated, polymer-based photonic crystal grating reflectors with varying grating periods The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions.
To overcome this limitation, we compute columnwise and global reliabilities of alignments based In particular, we improve the boundary prediction of the widely used nc Multi-fidelity machine learning models for accurate bandgap predictions of solids. Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level.
Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps.
The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way. Rapid and accurate prediction and scoring of water molecules in protein binding sites.
Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable.
The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules.
In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity. Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates.
The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time.
However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics CFD simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time.
Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster. Helicopter noise prediction is increasingly important.
The purpose of this viewgraph presentation is to: 1 Put into perspective the recent progress; 2 Outline current prediction capabilities; 3 Forecast direction of future prediction research; 4 Identify rotorcraft noise prediction needs. The presentation includes an historical perspective, a description of governing equations, and the current status of source noise prediction. Predicting the future development of depression or PTSD after injury. The objective was to develop a predictive screener that when given soon after injury will accurately differentiate those who will later develop depression or posttraumatic stress disorder PTSD from those who will not.
This study used a prospective, longitudinal cohort design. Subjects were randomly selected from all injured patients in the emergency department; the majority was assessed within 1 week postinjury with a short predictive screener, followed with in-person interviews after 3 and 6 months to determine the emergence of depression or PTSD within 6 months after injury.
A total of completed a risk factor survey at baseline; were assessed over 6 months. Twenty-six subjects [ The final screener demonstrated excellent sensitivity and moderate specificity both for clinically significant symptoms and for the diagnoses of depression and PTSD. A simple screener that can help identify those patients at highest risk for future development of PTSD and depression postinjury allows the judicious allocation of costly mental health resources.
All rights reserved. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation. Recently, a quasi-closed phase QCP analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction WLP methods uses the conventional forward type of sample prediction.
This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame.
To counter this problem, a modified quasi-closed phase forward-backward QCP-FB analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.
An accurate model for numerical prediction of piezoelectric energy harvesting from fluid structure interaction problems. Piezoelectric energy harvesting PEH from ambient energy sources, particularly vibrations, has attracted considerable interest throughout the last decade. Since fluid flow has a high energy density, it is one of the best candidates for PEH. Indeed, a piezoelectric energy harvesting process from the fluid flow takes the form of natural three-way coupling of the turbulent fluid flow, the electromechanical effect of the piezoelectric material and the electrical circuit.
There are some experimental and numerical studies about piezoelectric energy harvesting from fluid flow in literatures. Nevertheless, accurate modeling for predicting characteristics of this three-way coupling has not yet been developed. In the present study, accurate modeling for this triple coupling is developed and validated by experimental results.
A new code based on this modeling in an openFOAM platform is developed. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries.
Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds.
This remarkable accuracy is achieved by a vectorized representation of molecules so-called Bag of Bonds model that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex MHC :peptide binding.
Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate A physics-based, systematically coupled, multidisciplinary prediction tool MUTE for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions.
MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow shock wave effects, which generate the high-speed impulsive noise. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis.
Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database  to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized.
Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al . Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network.
I know I will lose all of what I got one day, but it's a story of future as well as all of us will die in the future. However, we still fight and live for now, right? I use the PI system The good news is this system is literally infinite. It also is percent guaranteed to do as well in the long run as any system currently being sold.
Start with 3 units. Then go to 1 unit. Then 4 units. Then as follows: Axle da Wolf has a different definition of pride it seems. I believe people who use negative betting systems have great pride. PRIDE : pride refers to an inflated sense of one's personal status or accomplishments. Does or bettting system really work??? Recommended online casinos. Joined: Nov 9, Threads: 1 Posts: 4. November 12th, at AM permalink.
Could anybody using this system tell me whether it works or not??? I am trying to use it but I am having some troubles with it I don't know why I almost always lose at the level of 3 units level 2. I really feel upset So, I decide to change it to system.
And then, you know what? I almost always lose at the level of 3 units level 3. Is the house kidding me? After that, I have checked my records and I see that I usually win at the first level of the progression Therefore, I make system become system.
However, it really sucks, I lose continuously at the level 1 level of 2 units Finally, I decided to quit and post this topic Thank you for visiting Try your best and the rest let God decide. You can easily find it in the in-game Minecraft menu by going into Options, then Resource Packs, then Open texture pack folder.
Now, make sure you either have MCPatcher or Optifine installed. These mods makes your Minecraft HD-ready for higher resolution texture packs. Without one of these, there might be bugs in the texture and your game might crash. Remember to close Minecraft before installing one of these mods.
Do not install both! When you have everything ready, open Minecraft and go to Options, then Texture Packs. It is usually named something like: "[1. Your game might freeze for a couple of seconds. It really depends on how much quality you wanted and how fast your computer is. Do not click anywhere and let the game load the texture. Before you do anything, check out the system requirements for Minecraft and make sure your computer meets the requirements. RAM is quick memory that all programs and games uses to some extent.
By default, Minecraft is only using whats needed for playing the game as it is. That's why you need to increase your RAM. I highly recommend installing Optifine. It's maybe the simplest way to heavily increase performance to Minecraft while giving you the ability to adjust graphical settings in-game. You can address that by following the steps below. There's nothing stopping people from expanding upon this texture pack.