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• Liquid desiccant-based deep dehumidifier (LDDD) working with a novel ionic liquid is introduced in this study. • The moisture transfer characteristics of LDDD using the novel IL are numerically simulated under different operating parameters and verified by experiments. • The LDDD using the novel IL can achieve deep dehumidification and outlet humidity ratio could be lower than 6.2 g/kg da under most working conditions. • A prediction model for moisture transfer is established and provides guidance for application of LDDD using ILs. Deep dehumidification system with energy conservation and stable operation is expected to be used in industrial occasions with low-humidity environment. Compared with solid desiccant dehumidification, liquid desiccant-based dehumidification is a promising alternative because of its advantages of continuous operation and low regeneration temperature. However, corrosion and crystallization problems exist with liquid desiccant-based deep dehumidifier (LDDD) using salt solutions. In this work, LDDD using a novel ionic liquid (IL) was introduced, and the heat and moisture transfer model was established based on the finite difference method. Meanwhile, the moisture transfer characteristics were numerically simulated under different operating parameters (i.e., inlet humidity ratio w a,in , air velocity v a , solution mass concentration X , solution temperature t s and solution volume flow V s). Besides, the significance of various factors and their interactions were analyzed based on response surface methodology. The numerical model is validated against the experiment of the LDDD test bench with a relative deviation of 3%-8%. Numerical results show that the LDDD using IL can achieve deep air dehumidification, and outlet humidity ratio of less than 4 g/kg da is possible. In particular, the order of significance for influencing factors of outlet humidity ratio is: w a,in > X > t s > v a > V s. Moreover, it is found that dehumidification rate of 60% LiBr solution (saturated state) is less than that of 90% IL. This study establishes a prediction model of moisture transfer between IL and air under the coupling of multiple physical fields, and provides guidance for the application of LDDD using ILs. [ABSTRACT FROM AUTHOR]

Pour des raisons techniques et économiques, toutes les masses d’eau européennes ne bénéficient pas d’un suivi direct de leur qualité établi à partir d’une collecte régulière de données biologiques. En France, cela représente environ dix mille sites. Pour combler ces lacunes, des chercheurs d’INRAE ont mis au point un ensemble de modèles capables de prédire avec un bon taux de réussite l’état écologique de ces masses d’eau à partir d’un jeu de données de pressions. Si ce type d’outil prédictif est d’un intérêt majeur pour les gestionnaires, il ne doit cependant pas être considéré comme une approche antagoniste à l'amélioration du réseau de surveillance mais plutôt comme un moyen de fournir un cadre général pour aider les politiques de l'eau.

Divers scores tentent d'estimer la probabilité de survenue de PR. En particulier, l'EULAR propose une règle simple pour déterminer si des arthralgies nouvelles sont suspectes de progression vers une PR. Il semble toutefois nécessaire d'ajouter un test sérologique à ce score, pour en augmenter la spécificité. Chez les patients présentant des arthrites cliniques, des critères prédictifs de l'évolution vers une PR ont aussi été identifiés. Globalement, la validité des scores disponibles est encore débattue. Ils peinent aussi à prendre en compte les interactions possibles entre les divers facteurs de risques, dans des sous-populations précises. Les nouvelles technologies pourraient aider à dépasser ces limitations, mais nous avons besoin de bases de données contenant suffisamment de patients PR et pré-PR, et incluant un suivi pré-diagnostique. Pour le moment, les règles prédictives existantes ne nous semblent pas concurrencer significativement les opinions d'experts. Various scores attempt to predict the development of RA. In particular, EULAR proposes a simple rule to identify new onset arthralgias suspicious of progression to RA. However, serological tests are necessary to ensure acceptable specificity. In patients with clinical arthritis, reliable predictive criteria for progression to RA have also been identified. Overall, the validity of the available scores is still debated. Such scores limitedly take into account interactions between risk factors in specific subpopulations. New technologies could help to overcome these limitations, but we need databases containing sufficient numbers of RA and pre-RA patients, including pre-diagnostic follow-up. Today, existing predictive rules do not seem to compete significantly with expert opinions. [ABSTRACT FROM AUTHOR]

The aim of this work was to propose a latent equation model that structures the relationship between academic engagement, schadenfreude and prosocial behavior on the school climate. We used a cross-sectional design and 437 secondary students from Chile were recruited. School Climate Scale, Engagement UWES-S 9, School Prosocial Behavior Inventory and Schadenfreude scale were administered. Factor analysis, reliability test, correlation analysis and structural equation modeling were performed to analysis data. After fitting two models for our data and contrasting them with theoretical literature, we found that school climate was affected directly by school engagement and prosocial behavior. Academic engagement positive affects, direct and indirectly, the school climate. An increase in academic engagement also lowers the levels of schadenfreude and causes an increase in the levels of prosocial behaviour. Results are discussed and possible implications are highlighted, considering limitations and suggestions that might guide practical interventions on school climate. [ABSTRACT FROM AUTHOR]

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Pour faire face aux limites des réseaux de surveillance pour l’évaluation de l’état écologique, il s’est avéré nécessaire, au moment de mettre en place les règles d’évaluation DCE, de pouvoir s’appuyer sur des méthodes fiables et reproductibles afin d’évaluer de manière homogène l’état écologique des masses d’eau ne bénéficiant pas de données biologiques. Dans ce but, nous avons construit un corpus de modèles capables, d’une part, de hiérarchiser l’impact des différents types de pressions anthropiques sur les indices biologiques utilisés dans les réseaux et, d’autre part, de prédire de manière satisfaisante les situations de bon état déterminées à partir d’indicateurs biologiques. S’il est très important d'encourager la construction de modèles pressions-impacts basés sur des ensembles de données de pression robustes et complets afin de soutenir la classification des masses d'eau conformément à la demande de la DCE, ce développement ne doit pas être considéré comme une approche antagoniste à l'amélioration du réseau de surveillance mais plutôt comme un moyen de fournir un cadre général pour aider les politiques de l'eau.

Statistical analysis for the big data of marathon score. Taking the Boston Marathon as an example and using statistical analysis, this paper studies the distribution of all players' performances in 2001–2018 from the multiple perspectives of age, gender and digital statistics in the context of big data. A series of valuable information and research conclusions that are instructive for the organizers and participants of the event are obtained. Then an autoregressive moving average (ARMA) model for the statistics of male and female entrants' average finishing time is established and the ARMA model has excellent explanation and prediction ability for the marathon average result. The statistical analysis and modeling method of big data for marathon results proposed in this paper based on Boston can also be extended to other cities and events. In addition, the ARMA method can also be used to model other marathon statistics according to the needs of researchers. [ABSTRACT FROM AUTHOR]

Copyright of European Review of Applied Psychology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Boreal felt lichen [ Erioderma pedicellatum (Hue) P.M. Jorg. (1972)] occurs on mainland Nova Scotia as well as Cape Breton, growing almost entirely on balsam fir [ Abies balsamea (L.) Mill.] in wet coastal forests. A Geographical Information System (GIS) based predictive model for E. pedicellatum habitat in Nova Scotia has facilitated surveys and guided conservation. We used this model to examine the relationship between presence of E. pedicellatum and forest structure (tree DBH, height, age, and crown closure, inter-tree distance, basal area of live and dead trees, and percent cover of shrubs, herbs, Sphagnum spp., and other mosses), and site conditions (topographic position, slope, aspect, and drainage) as well as the presence of lichen indicator species. Erioderma pedicellatum sites had significantly older trees, higher density of live trees, lower crown closure, lower basal area of live Picea mariana (Mill.) Britton, Sterns & Poggenb., lower basal area of live trees, higher basal area of dead trees, higher Sphagnum spp. cover, and lower shrub cover than unoccupied habitat. Erioderma pedicellatum sites were significantly less well drained and occurred on steeper slopes with a north or east aspect. Four macrolichens ( Coccocarpia palmicola, Platismatia norvegica, Lobaria scrobiculata, and Sphaerophorus globosus) occurred at a significantly higher frequency at E. pedicellatum sites. [ABSTRACT FROM AUTHOR]

Résumé Les nombreuses avancées dans les systèmes informatiques (collection de données, bases de données, stockage), les possibilités diagnostiques et thérapeutiques sont responsables d’une augmentation et d’une diversification des données disponibles. Les big data offrent les capacités, dans le domaine de la santé, d’accélérer les découvertes et d’optimiser la prise en charge des patients en combinant un nombre de données presque infini par la création de modèles thérapeutiques. En radiothérapie, le développement des big data est attrayant car les données à traiter sont très nombreuses et hétérogènes (démographiques, radiomiques, génomiques, radiogénomiques, etc.). La prédiction de l’efficacité et de la tolérance du traitement par irradiation serait alors optimisée. Avec ces nouveaux concepts, encore au stade préliminaire, il apparaît possible de développer une médecine de haute précision et personnalisée, toujours plus sécurisée et fiable. The many advances in data collection computing systems (data collection, database, storage), diagnostic and therapeutic possibilities are responsible for an increase and a diversification of available data. Big data offers the capacities, in the field of health, to accelerate the discoveries and to optimize the management of patients by combining a large volume of data and the creation of therapeutic models. In radiotherapy, the development of big data is attractive because data are very numerous et heterogeneous (demographics, radiomics, genomics, radiogenomics, etc.). The expectation would be to predict the effectiveness and tolerance of radiation therapy. With these new concepts, still at the preliminary stage, it is possible to create a personalized medicine which is always more secure and reliable. [ABSTRACT FROM AUTHOR]

Résumé La réalisation d’essais comparatifs randomisés en radiothérapie peut être difficile lorsque de nouvelles modalités de traitement émergent. Ainsi, peu ou pas d’études de phase 3 ont prouvé l’intérêt de la radiothérapie conformationnelle avec modulation d’intensité dans la majorité de ses indications actuelles. Un nouveau paradigme se développe, consistant à exploiter de larges bases de données afin de répondre à certaines questions cliniques ou translationnelles. Au-delà des bases nationales (la SEER [Surveillance, Epidemiology, and End Results] ou la NCDB [National Cancer Database]), pour lesquelles le niveau de détails est souvent jugé insuffisant au regard l’hétérogénéité des populations et des traitements étudiés, la généralisation des dossiers médicaux électroniques permet maintenant au contraire de dresser des profils phénotypiques très détaillés. En parallèle, les systèmes de radiothérapie enregistrent avec précision les traitements planifiés et réalisés. Les techniques d’intelligence artificielle et d’apprentissage automatisé permettent d’exploiter de façon incrémentale ces bases de données massives afin de dégager des hypothèses permettant de mieux personnaliser nos traitements. Cette revue détaille comment ces méthodes ont déjà été exploitées dans la littérature. Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies. [ABSTRACT FROM AUTHOR]

In order to reduce the overall emissions of greenhouse gases and to be in compliance with the current environmental regulations, a new class of refrigerants has appeared. Such refrigerants are generally multi-component systems that contain a fluorocompound mixed with CO 2 and/or an alkane. In order to design processes involving such blends or to implement a product-design approach aimed at identifying new refrigerant mixtures, an equation of state (EoS) able to predict the properties of fluorocompound-containing systems is required. In order to reach this goal, it was decided in this study to add six fluorinated groups to the E nhanced-PPR78 model which combines the Peng–Robinson EoS and a group contribution method aimed at estimating the binary interaction parameters, k ij ( T ), involved in Van der Waals one-fluid mixing rules. [ABSTRACT FROM AUTHOR]

La croissance de populations de truite commune (Salmo trutta L.) a été étudiée sur 84 cours d'eau répartis sur l'ensemble de la chaîne des Pyrénées. Pour cela, des échantillons d'écailles ont été récoltés dans 215 sites situés à des altitudes comprises entre 185 et 2000 m, pour des largeurs variant de 1,2 à 60 m. La croissance des individus a été déterminée par scalimétrie. La longueur totale moyenne atteinte par les individus à l'âge de 3 ans a été retenue comme variable biologique. La taille moyenne à 3 ans des individus est fortement structurée par l'altitude, ce gradient altitudinal représentant en grande partie un gradient thermique. Elle est corrélée négativement avec l'altitude et la densité totale en truites communes et positivement avec la largeur du cours d'eau et la conductivité estivale. La nature du débit joue également un rôle important sur la croissance puisqu'à altitude et à température moyenne estivale équivalentes, la taille moyenne à 3 ans est significativement plus faible en site soumis à un débit réservé qu'en site à débit naturel. Selon la nature du débit, deux modèles prédictifs de la taille moyenne à 3 ans (T3) en fonction de l'altitude (ALT) et de la largeur du cours d'eau (L) ont été établis par régression multiple pas à pas : - sites à débit naturel : Log (T3) = 2.457 - 0.007 x sgrt ALT + 0.066 x Log (L) 65.5 % (p < 0.01) de variance expliquée dont 61.6 % (p < 0.01) par l'altitude et 3.9 % (p < 0.01) par la largeur. -sites à débit réservé : Log (T3) = 2.432 - 0.009 x sqrt ALT + 0.087 x Log (L) 53.5 % (p < 0.01) de variance expliquée dont 47 % (p < 0.01) par l'altitude et 6.5 % (p < 0.05) par la largeur. Ces deux modèles, validés sur un jeu de données test, doivent permettre aux gestionnaires d'estimer une taille légale de capture adaptée aux conditions locales rencontrées dans les rivières des Pyrénées. La valeur des deux modèles est discutée et des propositions sont faites en vue de les améliorer. The growth of brown trout (Salmo trutta L.) populations has been studied on 84 streams distributed over the whole range of the Pyrenees. Samples of scales were collected in 215 sites located at altitudes ranging between 185 and 2000 m, for width varying from 1,2 to 60 m. The growth of individuals was determined by scale reading. The mean total length of age-3 trout was selected as the biological variable. The mean total length of age-3 trout was strongly structured by altitude, this altitudinal gradient mainly representing a thermal gradient. It is negatively correlated with altitude and total density of brown trout and positively correlated with stream width and summer conductivity. The nature of the flow regime also plays a major role on the growth since at equivalent altitude and equivalent mean summer temperature, the mean total length of age-3 trout is significantly lower in sites below dams with constant reduced flow than in site with natural flow. According to the nature of the flow, two predictive models of the mean total length of age-3 trout (T3), which take into account altitude (ALT) and width of the river (L), were established by stepwise linear multiple regression analysis : - sites with natural flow : Log (T3) = 2.457 - 0.007 x sqrt ALT + 0.066 x Log (L) The model explains 65.5 % (p < 0.01) of the variance of the total length of age-3 trout (T3 in mm): respectively 61.6 % (p < 0.01) by altitude (ALT in m) and 3.9 % (p < 0.01) by stream width (L in m). - sites with constant reduced flow: Log (T3) = 2.432 - 0.009 x sqrt ALT + 0.087 x Log (L). The model explains 53.5 % (p < 0.01) of the variance of the total length of age-3 trout (T3 in mm): respectively 47 % (p < 0.01) by altitude (ALT in m) and 6.5 % (p < 0.05) by stream width (L in m). These two models, tested and validated on a new data set, may help managers to estimate a legal size of capture adapted to the local conditions met in Pyrenean streams. The value of the two models is discussed and improvements are suggested.

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geotechnical Journal is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The prediction model M6 classifies pregnancy of unknown location (PUL) into a low-risk or a high-risk group in developing ectopic pregnancy (EP). The aim of this study was to validate the two-step M6 model's ability to classify PUL in French women., Material and Methods: All women with a diagnosis of PUL over a year were included in this single center retrospective study. Patients with a diagnosis of EP at the first consultation of with incomplete data were excluded. For each patient, the M6 model calculator was used to classified them into "high risk of EP" and "low risk of EP" group. The reference standard was the final diagnostic: failed PUL (FPUL), intrauterine pregnancy (IUP) of EP. The statistical measures of the test's performance were calculated., Results: Over the period, 255 women's consulted for a PUL, 197 has been included in the study. Final diagnosis were: 94 FPUL (94/197; 47.7%), 74 IUP (74/197; 37.6%) et 29 EP (29/197; 14.7%). The first step of the M6 model classified 16 women in the FPUL group of which 15 (15/16; 93.7%) correctly. The second step of the M6 model classified 181 women: 90 (90/181; 49.7%) in the "high risk of EP" group of which 63 (63/90; 70%) were FPUL/IUP and 27 (27/90; 30%) were EP. 91 (91/181; 50.3%) was classified in the "low risk of EP" group of which 90 (90/91; 98.9%) were FPUL/IUP and 1 (1/91; 1.1%) were EP. EP were correctly classified with sensitivity of 96.4%, negative predictive value of 98.9%, specificity of 58.8% and positive predictive value of 30.0%., Conclusions: The prediction model of PUL M6 classified EP in "high risk of EP group" with a sensitivity of 96.4%. It classified 50.3% of PUL in a "low risk of EP" group with a negative predictive value of 98.9%., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

This paper presents the results of an assessment performed on the empirical model used in level 3 input of the proposed mechanistic-empirical pavement design guide (MEPDG) developed under the National Cooperative Highway Research Program (NCHRP) project 1-37A. The empirical formula estimates the stiffness (dynamic modulus) of asphalt concrete mixes used in paving roads. The assessment revealed that analytically estimated dynamic modulus differs substantially from the actual value determined in the laboratory. Errors are high at elevated service temperatures and for mixes prepared with engineered binders. An alternative approach is presented in this paper based on the use of generic modulus values. Generic asphalt concrete dynamic modulus values were generated from tests performed on typical Marshall and SuperPave mixes. Results were compiled in a database (material library) to provide users of the MEPDG with estimates of the dynamic modulus that will yield more accurate predictions of performance until dynamic modulus laboratory testing capabilities are established locally. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Engineering Geology & the Environment is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: In type 2 diabetes (T2D), the most common causes of death are cardiovascular (CV) related, accounting for >50% of deaths in some reports. As novel diabetes therapies reduce CV death risk, identifying patients with T2D at highest CV death risk allows for cost-effective prioritization of these therapies. Accordingly, the primary goal of this study was to quantify the risk continuum for CV death in a real-world T2D population as a means to identify patients with the greatest expected benefit from cardioprotective antidiabetes therapies., Methods: This retrospective study included patients with T2D receiving services through an integrated health-care system and used data generated through electronic medical records (EMRs). Quantifying the risk continuum entailed developing a prediction model for CV death, creating an integer risk score based on the final prediction model and estimating future CV death risk according to risk score ranking., Results: Among 59,180 patients with T2D followed for an average of 7.5 years, 15,691 deaths occurred, 6,033 (38%) of which were CV related. The EMR-based prediction model included age, established CV disease and risk factors and glycemic indices (c statistic = 0.819). The 10% highest-risk patients according to prediction model elements had an annual CV death risk of ∼5%; the 25% highest-risk patients had an annual risk of ∼2%., Conclusions: This study incorporated a prediction modelling approach to quantify the risk continuum for CV death in T2D. Prospective application allows us to rank individuals with T2D according to their CV death risk, and may guide prioritization of novel diabetes therapies with cardioprotective properties., (Copyright © 2021 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

The many advances in data collection computing systems (data collection, database, storage), diagnostic and therapeutic possibilities are responsible for an increase and a diversification of available data. Big data offers the capacities, in the field of health, to accelerate the discoveries and to optimize the management of patients by combining a large volume of data and the creation of therapeutic models. In radiotherapy, the development of big data is attractive because data are very numerous et heterogeneous (demographics, radiomics, genomics, radiogenomics, etc.). The expectation would be to predict the effectiveness and tolerance of radiation therapy. With these new concepts, still at the preliminary stage, it is possible to create a personalized medicine which is always more secure and reliable., (Copyright © 2017. Published by Elsevier SAS.)

Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies., (Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.)

Aim: To describe the main prognostic factors with an impact on survival of patients diagnosed with upper tract urothelial carcinomas (UTUC)., Material and Methods: A systematic review of the literature has been performed using Pubmed without timeline restriction with the following keywords (MeSH): urothelial carcinoma; ureter; renal pelvis; prognosis; recurrence; survival; predictive models; nomogram., Results: The level of evidence was low (3) in every available studies. There were 4 categories of prognostic factors in UTUCs: clinical (patient and tumor characteristics); surgical; pathological and molecular. The most important pre-operative prognostic factors were: size>3cm, grade (biopsy and cytology); multifocality; important hydronephrosis; co-morbidity (ASA), ECOG status, and a surgical delay of no more than 3months. After surgery, the most important prognostic factors are: stage, grade, carcinoma in situ, lymphovascular invasion and lymph node involvement. Serum markers from inflammation (CRP) could be useful for the prediction of advanced stages. Molecular markers are still under evaluation., Conclusion: The identification of prognostic factors in UTUC has improved over the past years. These prognostic factors can be considered alone but also as a panel or inside predictive tools to predict accurately patient's survival., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)