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. Accurate remaining useful life RUL prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost.
Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management PHM techniques. In this paper, the proposed method is based on the data-driven prognostic approach.
WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series.
As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction , a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings REBs based on vibration signals.
The proposed prediction approach can be applied to prognostic other various mechanical assets. Accurate prediction of severe allergic reactions by a small set of environmental parameters NDVI, temperature. Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies.
The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies and especially those needing hospital treatment in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures.
In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods validation and test sets. We have used the Normalized Difference Vegetation Index NDVI , a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency.
Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.
Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature.
Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct. Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset.
We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January to December Patients who met the Third International Consensus Definitions for Sepsis Sepsis-3 prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27, and 42, patients within our development and validation cohorts, respectively. High-resolution vital signs time series and electronic medical record data were extracted.
Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the , 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.
Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient hours prior to clinical recognition. A prospective study is necessary to determine the. Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry.
Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk.
Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method.
Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. Full Text Available Death investigations often include an effort to establish the postmortem interval PMI in cases in which the time of death is uncertain.
The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment.
Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types gravesoil, skin of the torso, skin of the head, gene markers 16S ribosomal RNA rRNA, 18S rRNA, internal transcribed spacer regions ITS, and taxonomic levels sequence variants, species, genus, etc.. We also tested whether particular suites of indicator microbes were informative across different datasets.
Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer. The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming.
Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available.
High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect.
Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer.
Our automated defect simulation tool predicts. Predicting the Future at Yucca Mountain. This paper summarizes a climate- prediction model funded by the DOE for the Yucca Mountain nuclear waste repository. Several articles in the open literature attest to the effects of the Global Ocean Conveyor upon paleoclimate, specifically entrance and exit from the ice age.
The data shows that these millennial-scale effects are duplicated on the microscale of years to decades. This work also identifies how man may have influenced the Conveyor, affecting global cooling and warming for 2, years. Predictive toxicology: the paths of the future. Detilleux, Ph. Prevention of possible noxious effects in relation with the exposure to one or several chemical, physical or biological agents present in our domestic or professional environment is one of today's big public health stakes.
Another stake is the better assessment of the risks linked with the use of health-care products. The efficacy and predictiveness of toxicology studies are directly related to the combination of alternate complementary methods and animal experiments obtaining data from different species and with different models: in vitro, ex vivo and in vivo.
Despite important efforts, the toxicological evaluation remains perfectible. The proceedings of this congress of the French Society of cell pharmaco-toxicology deal with recent advances, both scientific and technological, in ' predictive toxicology'. Four main topics are approached: cell and organ models, 'omics', in silico modeling, and new technologies imaging, cell ships, high-speed processing. Among the different presentations, 3 abstracts present some recent advances in imaging techniques applied to toxicology studies.
Laprevote, Paris-Descartes Univ. FR ; 2 - Small animal imaging, a tool for predictive toxicology A. Rouas et al. Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus. Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus.
The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information.
The goal was to anticipate the direction of the service left or right and the rotational motion of service topspin, sidespin, or cut. We recorded their responses and quantified the following outcomes: i directional accuracy and ii rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials.
The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.
Improvement of a land surface model for accurate prediction of surface energy and water balances. In order to predict energy and water balances between the biosphere and atmosphere accurately , sophisticated schemes to calculate evaporation and adsorption processes in the soil and cloud fog water deposition on vegetation were implemented in the one-dimensional atmosphere-soil-vegetation model including CO 2 exchange process SOLVEG2.
Performance tests in arid areas showed that the above schemes have a significant effect on surface energy and water balances. With further modifications of the model to implement the carbon exchanges between the vegetation and soil, deposition processes of materials on the land surface, vegetation stress-growth-dynamics etc.
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points.
Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design. Watershed area ratio accurately predicts daily streamflow in nested catchments in the Catskills, New York.
New hydrological insights for the region: Watershed area ratio was the most important basin parameter for estimating flow at upstream sites based on downstream flow. Regression analysis indicated that flow at any upstream point can be estimated by multiplying the flow at a downstream reference gage by the watershed area ratio. This method accurately predicted upstream flows at area ratios as low as 0. Our results indicate that a simple flow estimation method based on watershed area ratios is justifiable, and indeed preferred, for the estimation of daily streamflow in ungaged watersheds in the Catskills region.
In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli. Full Text Available A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression.
An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor—operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data.
We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system. Measuring solar reflectance - Part I: Defining a metric that accurately predicts solar heat gain.
Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective ''cool colored'' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary unconcentrated global sunlight.
Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.
Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor.
Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use.
This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain. A widely used solar reflectance metric based on the ASTM Standard E beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary unconcentrated global sunlight.
At mainland U. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor.
Highly accurate prediction of food challenge outcome using routinely available clinical data. Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service phase 1.
We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center phase 2. Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center phase 3. Allergen-specific models were developed for peanut, egg, and milk. Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed.
Published by Mosby, Inc. The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction of the fut Depression, realism, and the overconfidence effect: are the sadder wiser when predicting future actions and events?
Do depressed individuals make more realistic judgments than their nondepressed peers in real world settings? Depressed and nondepressed Ss in 2 studies were asked to make predictions about future actions and outcomes that might occur in their personal academic and social worlds. Both groups of Ss displayed overconfidence, that is, they overestimated the likelihood that their predictions would prove to be accurate. Of key importance, depressed Ss were less accurate in their predictions , and thus more overconfident, than their nondepressed counterparts.
These differences arose because depressed Ss a were more likely to predict the occurrence of low base-rate events and b were less likely to be correct when they made optimistic predictions i. Discussion focuses on implications of these findings for the depressive realism hypothesis. Visel, Axel; Blow, Matthew J. A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes.
Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p in mouse embryonic forebrain, midbrain, and limb tissue.
We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p binding. Our results indicate that in vivo mapping of p binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.
Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design.
In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space.
The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error RMSE of 0. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.
In this paper we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3.
For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions temperature, optical path length, pressure.
On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions.
There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range.
The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are i accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and ii efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical.
Does the emergency surgery score accurately predict outcomes in emergent laparotomies? The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using 1 the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and 2 all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture.
Multivariable logistic regression analyses were performed to measure the correlation c-statistic between the emergency surgery score and 1 day mortality, and 2 day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes.
A total of 26, emergent laparotomy patients were included. Thirty-day mortality and morbidity were The morbidity rates plateaued for scores higher than Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent 1 splenic, 2 gastroduodenal, 3 intestinal, 4 hepatobiliary, or 5 incarcerated ventral hernia operation.
The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision. Scenario research: Can the future be predicted? Jensen sits at the computer in his office wondering what to cook for dinner. He browses through the web site of his usual supermarket. Today's menu is fish. He skims the recipe that also informs him when, where and how the fish was caught. The fish is quite expensive but it has a story to tel Experts from industry, trade organisations, retailing, authorities and other interested parties participated The scenarios are put together with the purpose of making the food industry and food research reflect on which demands their competencies will be met with in various In the process of creating the scenarios, researchers at MAPP and the Danish Technical University formulated mini scenarios based on their research.
The scenarios were used as inspiration in discussions with experts from industry, trade organisations, authorities etc. Predicting future discoveries from current scientific literature. Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries.
We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses.
In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature. While we found that the application of the framework has been useful in the Nepalese context, there are some limitations in its scope and precision.
Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients? Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy IMRT and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies.
The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters TLDs in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment.
Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Dose accuracy of up to 2. The deviations from the calculated absorbed doses were overall larger 3. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses by TPS and measured doses by TLD dosimetry can be accurately predicted for tangential treatment of the chest wall after mastectomy.
The interactions between non-coding RNAs ncRNAs and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming.
Deep learning provides a powerful solution to computationally predict RNA-protein interactions. Stacked assembling is further used to improve the accuracy of the proposed method. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.
Published by Elsevier Inc. How accurate is anatomic limb alignment in predicting mechanical limb alignment after total knee arthroplasty? Anatomic limb alignment often differs from mechanical limb alignment after total knee arthroplasty TKA.
We also assessed whether the accuracy of anatomic limb alignment was affected by anatomic variation. This retrospective study included primary TKAs. The alignment of the limb was measured with both anatomic and mechanical methods of measurement. We also measured anatomic variation, including the femoral bowing angle, tibial bowing angle, and neck-shaft angle of the femur.
All angles were measured on the same full-length standing anteroposterior radiographs. The accuracy, specificity, and sensitivity for each range of anatomic limb alignment were calculated and compared using mechanical limb alignment as the reference standard. The associations between the accuracy of anatomic limb alignment and anatomic variation were also determined. Anatomic limb alignment did not accurately predict mechanical limb alignment after TKA, and its accuracy was affected by anatomic variation.
Thus, alignment after TKA should be assessed by measuring mechanical alignment rather than anatomic. Human capital planning is an important tool in predicting future talent needs and sustaining organizational excellence over the long term. This article examines the concept of human capital planning and outlines how institutions can use HCP to identify the type and number of talent needed both now and in the future , recognize and prioritize talent…. We evaluated variables with potential predictive Cluster abundance in chameleon f R gravity I: toward an accurate halo mass function prediction.
We refine the mass and environment dependent spherical collapse model of chameleon f R gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N -body simulations.
We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.
Colorectal cancer remains the second leading cause of death in the United States despite improvements in incidence rates and advancements in screening. The present study evaluated the prognostic value of two tumor markers, MET and ROCK I, which have been noted in other cancers to provide more accurate prognoses of patient outcomes than tumor staging alone. We constructed a tissue microarray from surgical specimens of adenocarcinomas from colorectal cancer patients.
Kaplan-Meier survival analysis revealed that patients' 5-year survival was inversely correlated with levels of expression of ROCK I. In contrast, MET was less strongly correlated with five-year survival. As a result, ROCK I may provide a less invasive method of assessing patient prognoses and directing therapeutic interventions. John G. Delinassios , All rights reserved. Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time.
Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity NR waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code.
The accuracy of new wheelchair users' predictions about their future wheelchair use. This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair.
Predictive accuracy diminished over time, with overall accuracy declining from 0. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use.
The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term. Service life prediction of exterior plastics vision for the future. Coverage includes progress in developing new science and test methods to determine how materials respond to weather exposure.
This book is ideal for researchers and professionals working in the field of service life prediction. This book also: Examines numerous consensus standards that affect commercial products allowing readers to see the future of standards related to service life prediction Provides the scientific foundation for the latest commercially viable instruments Presents groundbreaking research, including the blueprint of a new test method that will significantly shorten the service life prediction process time Covers two of the latest verified predictive models, which demonstrate realized-potential to transform the field.
Absolute Hounsfield unit measurement on noncontrast computed tomography cannot accurately predict struvite stone composition. We retrospectively searched for all patients with struvite components on stone composition analysis between January and March Stone analysis was performed via Fourier transform infrared spectrometry.
Overall, 3 6. Again, significant overlaps were seen. Pure struvite stones have significantly lower HUD than mixed struvite stones, but overlap exists. A low HUD may increase the suspicion for a pure struvite calculus. Purpose: Hemiablative therapy HAT is increasing in popularity for treatment of patients with low-risk prostate cancer PCa.
The validity of this therapeutic modality, which exclusively treats PCa within a single prostate lobe, rests on accurate staging. We tested the accuracy of unilaterally unremarkable biopsy findings in cases of low-risk PCa patients who are potential candidates for HAT.
Conclusions: Two-thirds of patients with unilateral low-risk PCa, confirmed by clinical stage and biopsy findings, have bilateral or non-organ-confined PCa at radical prostatectomy. This alarming finding questions the safety and validity of HAT. Large arterial occlusive strokes as a medical emergency: need to accurately predict clot location.
Endovascular treatment for acute ischemic stroke with a large intracranial occlusion was recently shown to be effective. Timely knowledge of the presence, site, and extent of arterial occlusions in the ischemic territory has the potential to influence patient selection for endovascular treatment.
We aimed to find predictors of large vessel occlusive strokes, on the basis of available demographic, clinical, radiological, and laboratory data in the emergency setting. Easily accessible variables were used in a multivariate analysis. Of patients enrolled, a significant proportion The main clinical predictors of any arterial occlusion were in-hospital stroke [odd ratios OR 2. Further, the following radiological predictors were identified: time-to-imaging OR 0.
The area under curve for this analysis was 0. Looking at different occlusion sites, National Institute of Health Stroke Scale and early ischemic changes on CT were independent predictors in all subgroups. Neurological deficits, stroke risk factors, and CT findings accurately identify acute ischemic stroke patients at risk of symptomatic vessel occlusion. Predicting the presence of these occlusions may impact emergency stroke care in regions with limited access to noninvasive vascular imaging.
Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases.
However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy.
We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0. 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. Automatic Planning in a Robotized Cell. Federico Guedea-Elizalde, Yazmin S. Mohammed J. Back Matter. The 31 revised full papers were carefully reviewed and selected from 48 submissions. The papers are organized in topical sections on autonomous sensors and sensor systems, autonomous systems and intelligent control with applications, intelligent fuzzy systems, intelligent robotics, intelligent knowledge management, swarm and evolutionary methods, and applications.
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