scholarly journals Thermodynamics of Oils of Nutritional/Cosmetic Use: Bertholletia excelsa, Cocos nucifera, and Pterodon emarginatus Vogel

2020 ◽  
Vol 3 (4) ◽  
pp. 347-353
Author(s):  
Marta A. Pires ◽  
Rebecca S. Andrade ◽  
Miguel Iglesias
Keyword(s):  
Fatty Acids ◽  
Experimental Study ◽  
Temperature Effect ◽  
Ultrasonic Velocity ◽  
Cocos Nucifera ◽  
Complex Nature ◽  
Brazil Nut ◽  
Bertholletia Excelsa ◽  
Molecular Group ◽  
Wide Range

This paper contains the results of a new experimental study about the temperature effect on density and ultrasonic velocity for Brazil nut (Bertholletia excelsa), coconut (Cocos nucifera), and sucupira (Pterodon emarginatus Vogel) oils. Isentropic compressibilities and isobaric expansibilities were computed from the experimental magnitudes as a function of temperature. Halvorsen’s model (HM), and Collision Factor Theory (CFT) were selected for the prediction of these properties due to their wide range of application and easy computation. An accurate response was observed, despite the use of several simplifications as molecular group contribution procedures for estimation of theoretic criticals points of the fatty acids and the complex nature of the studied fluids.  

Modeling of Sessile Droplet Evaporation on Engineered Surfaces

2019 ◽  
Vol 11 (6) ◽  
Author(s):  
Jyoti Prakash ◽  
Basant Singh Sikarwar
Keyword(s):  
Experimental Study ◽  
Relative Humidity ◽  
Evaporation Rate ◽  
Sessile Drop ◽  
Droplet Evaporation ◽  
Contact Angles ◽  
Surface Wettability ◽  
Aluminum Surface ◽  
Complex Nature ◽  
Wide Range

The evaporation of sessile drop has a wide range of application that includes printing, washing, cooling, and coating. Due to the complex nature of drop evaporation process, this phenomenon is reliant on several parameters such as ambiance and physiochemical properties of liquid and surface. In the present study, a mathematical model of water droplet evaporation on an engineered aluminum surface is developed. Experimental study is carried out for the validation of code. The data obtained from the simulation is validated against the data obtained from an experimental study as well as the data available in the literature and good agreement was found among them. Post-validation, the effect of surface wettability and environment conditions on a droplet evaporation rate is estimated. It is inferred from the outcomes that the temperature at the apex of the drop varies linearly with the increasing relative humidity. Droplet volume has a significant impact on the evaporation rate and comparatively higher evaporative flux for a smaller volume of the drop with large contact angles. This unveils the possibility of achieving the required evaporation rate by controlling surface wettability and relative humidity conditions near the drop.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Consumer Neuroscience Techniques in Advertising Research: A Bibliometric Citation Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1589
Author(s):  
Juan Sánchez-Fernández ◽  
Luis-Alberto Casado-Aranda ◽  
Ana-Belén Bastidas-Manzano
Keyword(s):  
Self Report ◽  
Future Research ◽  
Complex Nature ◽  
Clear Understanding ◽  
Continuous Growth ◽  
Main Research ◽  
Consumer Neuroscience ◽  
Starting Point ◽  
Wide Range ◽  
Performance Analysis Tools

The limitations of self-report techniques (i.e., questionnaires or surveys) in measuring consumer response to advertising stimuli have necessitated more objective and accurate tools from the fields of neuroscience and psychology for the study of consumer behavior, resulting in the creation of consumer neuroscience. This recent marketing sub-field stems from a wide range of disciplines and applies multiple types of techniques to diverse advertising subdomains (e.g., advertising constructs, media elements, or prediction strategies). Due to its complex nature and continuous growth, this area of research calls for a clear understanding of its evolution, current scope, and potential domains in the field of advertising. Thus, this current research is among the first to apply a bibliometric approach to clarify the main research streams analyzing advertising persuasion using neuroimaging. Particularly, this paper combines a comprehensive review with performance analysis tools of 203 papers published between 1986 and 2019 in outlets indexed by the ISI Web of Science database. Our findings describe the research tools, journals, and themes that are worth considering in future research. The current study also provides an agenda for future research and therefore constitutes a starting point for advertising academics and professionals intending to use neuroimaging techniques.

scholarly journals The Supportive Care Needs of Cancer Patients: a Systematic Review

2021 ◽  
Author(s):  
Madeleine Evans Webb ◽  
Elizabeth Murray ◽  
Zane William Younger ◽  
Henry Goodfellow ◽  
Jamie Ross
Keyword(s):  
Cancer Patients ◽  
Healthcare Professionals ◽  
Treatment Options ◽  
Complex Nature ◽  
Narrative Synthesis ◽  
Care Needs ◽  
Wide Range ◽  
Need Support ◽  
Lack Of Control ◽  
Everyday Work

AbstractCancer, and the complex nature of treatment, has a profound impact on lives of patients and their families. Subsequently, cancer patients have a wide range of needs. This study aims to identify and synthesise cancer patients’ views about areas where they need support throughout their care. A systematic  search of the literature from PsycInfo, Embase and Medline databases was conducted, and a narrative. Synthesis of results was carried out using the Corbin & Strauss “3 lines of work” framework. For each line of work, a group of key common needs were identified. For illness-work, the key needs idenitified were; understanding their illness and treatment options, knowing what to expect, communication with healthcare professionals, and staying well. In regards to everyday work, patients wanted to maintain a sense of normalcy and look after their loved ones. For biographical work, patients commonly struggled with the emotion impact of illness and a lack of control over their lives. Spiritual, sexual and financial problems were less universal. For some types of support, demographic factors influenced the level of need reported. While all patients are unique, there are a clear set of issues that are common to a majority of cancer journeys. To improve care, these needs should be prioritised by healthcare practitioners.

scholarly journals Optimization Model of Phenolics Encapsulation Conditions for Biofortification in Fatty Acids of Animal Food Products

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 881
Author(s):  
Roberta Tolve ◽  
Fernanda Galgano ◽  
Nicola Condelli ◽  
Nazarena Cela ◽  
Luigi Lucini ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Condensed Tannins ◽  
Encapsulation Efficiency ◽  
Biological Effects ◽  
Fatty Acid Content ◽  
Food Products ◽  
Animal Origin ◽  
Animal Nutrition ◽  
Loading Capacity ◽  
Wide Range

The nutritional quality of animal products is strongly related to their fatty acid content and composition. Nowadays, attention is paid to the possibility of producing healthier foods of animal origin by intervening in animal feed. In this field, the use of condensed tannins as dietary supplements in animal nutrition is becoming popular due to their wide range of biological effects related, among others, to their ability to modulate the rumen biohydrogenation and biofortify, through the improvement of the fatty acids profile, the derivate food products. Unfortunately, tannins are characterized by strong astringency and low bioavailability. These disadvantages could be overcome through the microencapsulation in protective matrices. With this in mind, the optimal conditions for microencapsulation of a polyphenolic extract rich in condensed tannins by spray drying using a blend of maltodextrin (MD) and gum Arabic (GA) as shell material were investigated. For this purpose, after the extract characterization, through spectrophotometer assays and ultra-high-performance liquid chromatography-quadrupole time-of-flight (UHPLC-QTOF) mass spectrometry, a central composite design (CCD) was employed to investigate the combined effects of core:shell and MD:GA ratio on the microencapsulation process. The results obtained were used to develop second-order polynomial regression models on different responses, namely encapsulation yield, encapsulation efficiency, loading capacity, and tannin content. The formulation characterized by a core:shell ratio of 1.5:5 and MD:GA ratio of 4:6 was selected as the optimized one with a loading capacity of 17.67%, encapsulation efficiency of 76.58%, encapsulation yield of 35.69%, and tannin concentration of 14.46 g/100 g. Moreover, in vitro release under varying pH of the optimized formulation was carried out with results that could improve the use of microencapsulated condensed tannins in animal nutrition for the biofortification of derivates.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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