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The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students
No full textBackground. The aim of this research was to analyze whether the personality factors included in the Big Five model differentially predict the self-regulation and affective states of university students and health.
Methods. A total of 637 students completed validated self-report questionnaires. Using an ex post facto design, we conducted linear regression and structural prediction analyses.
Results The findings showed that model factors were differential predictors of both self-regulation and affective states. Self-regulation and affective states, in turn, jointly predict emotional performance while learning and even student health. These results allow us to understand, through a holistic predictive model, the differential predictive relationships of all the factors: conscientiousness and extraversion were predictors regulating positive emotionality and health; the openness to experience factor was non-regulating; nonregulating; and agreeableness and
neuroticism were dysregulating, hence precursors of negative emotionality and poorer student health.
Conclusions. These results are important because they allow us to infer implications for guidance and psychological health at university
Ultrasound for assessing tumor spread in ovarian cancer. A systematic review of the literature and meta-analysis
No full textIn this review, we aimed to assess the diagnostic performance of ultrasound for assessing the tumor spread in the abdomen in women with ovarian cancer. A search for studies evaluating the role of ultrasound for assessing intrabdominal tumor spread in women with ovarian cancer compared to surgery from January 2011 to March 2023 was performed in PubMed/MEDLINE, Web of Science, and Scopus databases. The Quality Assessment of Diagnostic Accuracy Studies 2 evaluated the quality of the studies (QUADAS-2). All analyses were performed using MIDAS and METANDI commands in STATA 12.0 software. We identified 1552 citations. After exclusions, five studies comprising 822 women were included. Quality of studies were considered as good, except for patient selection as all studies were considered as having high risk of bias. The pooled sensitivity and specificity could be calculated for three anatomical areas (recto-sigma, major omentum and root of mesentery) and the presence of ascites. The pooled sensitivity and specificity for detecting disease in the recto-sigma, major omentum and root of mesentery were 0.83 and 0.95, 0.87 and 0.87, and 0.29 and 0.99, respectively. The pooled sensitivity and specificity for detecting ascites was 0.95 and 0.91, respectively. There is evidence that ultrasound offers good diagnostic performance for evaluating the intra-abdominal extent of disease in women with suspected ovarian cancer
A microstructure-based constitutive model for eutectoid steels
No full textThis work presents a constitutive model for eutectoid steels based on their two-phase lamellar microstructure. The model accounts for the individual behaviour of both ferrite and cementite, with perfect interphase adhesion assumed. It considers anisotropic hardening mechanisms in ferrite derived from the lamellar structure of pearlite while ignoring the crystal structure of either phase. The model also accounts for the evolution of orientation and spacing of lamellae under directional deformation, along with the evolution of internal stress distribution in both phases. Due to its simplicity, the model has very few calibration parameters but is still able to reproduce complex strain paths and loading conditions with excellent accuracy. The model was compared with tensile, compression and torsion tests from a 13-pass wire drawing series (up to drawing strains of 2.7) and reproduced accurately the mechanical response under any loading condition. The robustness of the model lies in the fact that it is able to recreate the evolution of internal stresses built in cementite and ferrite. Such internal stress evolution was confirmed to reproduce accurately the stress partitioning observed in neutron and X-ray diffraction tests reported in literature. Moreover, the model contributes to the understanding of the rapid broadening of cementite diffraction peaks observed during in-situ tensile tests of patented wires
A numerical model for predicting powder characteristics in LMD considering particle interaction
No full textIn this work, a numerical model is proposed to analyze the influence of particle-particle interaction in laser directed energy deposition or LMD (laser metal deposition) of CM247 Ni-based superalloy. The model is based on the analysis of contact between particles and the potential agglomeration of powder to predict powder conditions at the nozzle exit. Simulation results were experimentally validated and a good agreement was observed. At the nozzle exit mainly large particles (>100 mu m) are found and small ones (<10 m) tend to flow away from this region. This was also observed in the experimental PSD. Additionally, based on the relative velocity of particles, simulations are able to predict the formation of dents. In comparing virgin powder PSD and the one at the nozzle exit, it was observed that largest particles are collected at the exit. In order to explain this phenomena, particle agglomeration was analysed numerically. It was seen that small particles tend to adhere to the big ones due to their higher adhesive forces, which would explain the change in PSD. (c) 2024 The Society of Powder Technology Japan. Published by Elsevier BV and The Society of Powder Technology Japan. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Classification of Mexican audiences by their interest in digital news content and socioeconomic characteristics
No full textImpact of applying the global lung initiative criteria for airway obstruction in GOLD defined COPD cohorts: the BODE and CHAIN experience
Full text linkIntroduction: The Global Lung Function Initiative (GLI) has proposed new criteria for airflow limitation (AL) and recommends using these to interpret spirometry. The objective of this study was to explore the impact of the application of the AL GLI criteria in two well characterized GOLD-defined COPD cohorts.
Methods: COPD patients from the BODE (n=360) and the COPD History Assessment In SpaiN (CHAIN) cohorts (n=722) were enrolled and followed. Age, gender, pack-years history, BMI, dyspnea, lung function measurements, exercise capacity, BODE index, history of exacerbations and survival were recorded. CT-detected comorbidities were registered in the BODE cohort. The proportion of subjects without AL by GLI criteria was determined in each cohort. The clinical, CT-detected comorbidity, and overall survival of these patients were evaluated.
Results: In total, 18% of the BODE and 15% of the CHAIN cohort did not meet GLI AL criteria. In the BODE and CHAIN cohorts respectively, these patients had a high clinical burden (BODE≥3: 9% and 20%; mMRC≥2: 16% and 45%; exacerbations in the previous year: 31% and 9%; 6MWD<350m: 15% and 19%, respectively), and a similar prevalence of CT-diagnosed comorbidities compared with those with GLI AL. They also had a higher rate of long-term mortality - 33% and 22% respectively.
Conclusions: An important proportion of patients from 2 GOLD-defined COPD cohorts did not meet GLI AL criteria at enrolment, although they had a significant burden of disease. Caution must be taken when applying the GLI AL criteria in clinical practice
Building energy performance metamodels for district energy management optimisation platforms
Full text linkReactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy; detailed physics-based models provide satisfactory generalization but at the expense of high computational costs; and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble boosting methods (e.g. Adaboost, Gradient Boosting and Extreme Gradient Boosting) with comparatively low use in the building simulation community. Finally, a virtual test bed consisting in metamodels coupled to an optimization engine based on genetic algorithms, was implemented, and compared to a traditional Physics-based model MPC solution (EnergyPlus-GENOPT), to evaluate the potential of the developed building level optimization functionalities. The metamodels and optimization engine were able to reproduce the optimized settings identified by the EnergyPlus-GENOPT MPC solution with cost savings potentials of 5-10%
NUM-score: A clinical-analytical model for personalised imaging after urinary tract infections
No full textAim: To identify predictive variables and construct a predictive model along with a decision algorithm to identify nephrourological malformations (NUM) in children with febrile urinary tract infections (fUTI), enhancing the efficiency of imaging diagnostics.
Methods: We performed a retrospective study of patients aged <16 years with fUTI at the Emergency Department with subsequent microbiological confirmation between 2014 and 2020. The follow-up period was at least 2 years. Patients were categorised into two groups: 'NUM' with previously known nephrourological anomalies or those diagnosed during the follow-up and 'Non-NUM' group.
Results: Out of 836 eligible patients, 26.8% had underlying NUMs. The study identified six key risk factors: recurrent UTIs, non-Escherichia coli infection, moderate acute kidney injury, procalcitonin levels >2 μg/L, age <3 months at the first UTI and fUTIs beyond 24 months. These risk factors were used to develop a predictive model with an 80.7% accuracy rate and elaborate a NUM-score classifying patients into low, moderate and high-risk groups, with a 10%, 35% and 93% prevalence of NUM. We propose an algorithm for approaching imaging tests following a fUTI.
Conclusion: Our predictive score may help physicians decide about imaging tests. However, prospective validation of the model will be necessary before its application in daily clinical practice
GMCSpy: efficient and accurate computation of genetic minimal cut sets in Python
No full textMotivation: The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal
Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have
been developed to calculate MCSs and gMCSs using both commercial and open-source software.
Results: Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial
optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on
commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant
improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of
Systems Biology for different applications in health and biotechnology