How Does The Interior Design of Learning Spaces Impact The Students` Health, Behavior, and Performance?

: The importance of research: human beings are greatly affected by their surroundings, especially in learning spaces. Lighting, colors, seating arrangement, and other factors all have a physical and psychological impact on students, which is reflected in their behavior, and performance. Research problem: Although there have been previous studies that have linked a student\u27s behavior to the interior design elements of the learning space, there is no extensive, combined study that can guide the design of university educational spaces to achieve the highest possible efficiency for the students. Research Objective: Set criteria to assess the learning space; this standard states whether or not a learning space can provide an appropriate learning environment that can help students physically and psychologically. Research methodology: (theoretical approach) relied on a review of previous theoretical and practical studies to gather sufficient information to determine the criteria for evaluating each element of the educational space and how it impacts students, this is shown by presenting definitions of human behavior and how it is influenced by the built environment, and then used to illustrate how the interior architectural design of the educational space impact students, reaching the ideal status for each element in the final table. (analytical approach) in which a questionnaire was conducted for students as a field study and then analyzed for results. Results showed that many existing educational spaces have problems with interior design elements. Some of these problems, such as lighting, ventilation, and temperature, can be avoided from the beginning by orienting educational spaces correctly and thus obtaining adequate natural lighting, ventilation, and temperature without having to rely on industrial means. Noise can also be reduced if a suitable location away from the noise is chosen, as well as careful selection of finishing materials of sound insulation material. Table 8 shows additional means of isolation that can be used in existing spaces. On the other hand, the color of the white wall, which many studies have confirmed harms the space users of students and teachers but is still used, is a problem that is easily avoidable but still recurring. It is preferable to use cold or warm colors instead, as shown in Table 8. The significance of this research is in assisting in the creation of a suitable educational space that meets the needs of students while avoiding all of the problems mentioned above in many educational settings

How Does The Interior Design of Learning Spaces Impact The Students` Health, Behavior, and Performance?

: The importance of research: human beings are greatly affected by their surroundings, especially in learning spaces. Lighting, colors, seating arrangement, and other factors all have a physical and psychological impact on students, which is reflected in their behavior, and performance. Research problem: Although there have been previous studies that have linked a student\u27s behavior to the interior design elements of the learning space, there is no extensive, combined study that can guide the design of university educational spaces to achieve the highest possible efficiency for the students. Research Objective: Set criteria to assess the learning space; this standard states whether or not a learning space can provide an appropriate learning environment that can help students physically and psychologically. Research methodology: (theoretical approach) relied on a review of previous theoretical and practical studies to gather sufficient information to determine the criteria for evaluating each element of the educational space and how it impacts students, this is shown by presenting definitions of human behavior and how it is influenced by the built environment, and then used to illustrate how the interior architectural design of the educational space impact students, reaching the ideal status for each element in the final table. (analytical approach) in which a questionnaire was conducted for students as a field study and then analyzed for results. Results showed that many existing educational spaces have problems with interior design elements. Some of these problems, such as lighting, ventilation, and temperature, can be avoided from the beginning by orienting educational spaces correctly and thus obtaining adequate natural lighting, ventilation, and temperature without having to rely on industrial means. Noise can also be reduced if a suitable location away from the noise is chosen, as well as careful selection of finishing materials of sound insulation material. Table 8 shows additional means of isolation that can be used in existing spaces. On the other hand, the color of the white wall, which many studies have confirmed harms the space users of students and teachers but is still used, is a problem that is easily avoidable but still recurring. It is preferable to use cold or warm colors instead, as shown in Table 8. The significance of this research is in assisting in the creation of a suitable educational space that meets the needs of students while avoiding all of the problems mentioned above in many educational settings

The impact of semantics on aspect level opinion mining

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure

Ontological Engineering For Source Code Generation

Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledge base, ontology, Specification Configuration Template (SCT) model and deep learnin

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

A hybrid model to predict best answers in question answering communities

Question answering communities (QAC) are nowadays becoming widely used due to the huge facilities and flow of information that it provides. These communities target is to share and exchange knowledge between users. Through asking and answering questions under large number of categories. Unfortunately there are a lot of issues existing that made knowledge process difficult. One of those issues is that not every asker has the knowledge and ability to select the best answer for his question, or even selecting the best answer based on subjective matters. Our analysis in this paper is conducted on stack overflow community. We proposed a hybrid model for predicting the best answer. The proposed model is consisting of two modules. The first module is the content feature which consists of three types of features; question-answer features, answer content features, and answer-answer features. In the second module we examine the use of non-content feature in predicting best answers by using novel reputation score function. Then we merge both of content and non-content features and use them in prediction. We conducted experiments to train three different classifiers using our new added features. The prediction accuracy is very promising

A hybrid Stacking-SMOTE model for optimizing the prediction of autistic genes

Abstract Purpose Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis. Methods A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes. Results The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy. Conclusion The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE)

Inter-plant hydrogen integration with regeneration placement and multi-period consideration

Inter-plant hydrogen integration (IPHI) is getting more attention in recent years, as a result of the increasing demand for hydrogen in refinery processes, such as hydrotreating and hydrocracking. In this work, IPHI with regeneration scheme is analyzed. Indirect integration scheme is adopted, where hydrogen sources from different hydrogen networks are integrated via a centralized purifier, such as pressure swing adsorption (PSA) or membrane separation. The introduced model is able to select the optimum interception unit, which minimizes the total annualized cost. Besides, multi-period consideration is included in the analysis to address the effect of changes in operating conditions of the IPHI network on total hydrogen consumption. Two case studies are presented to demonstrate the applicability and usefulness of the proposed model. Keywords: Optimization, Optimal design, Hydrogen, Regeneration, Process integration, Multi-perio

Monitoring and modelling of variables affecting isomerate octane number produced from an industrial isomerization process

Petroleum refineries are now facing much tighter and stricter transportation and fuel specification standards, as well as environmental regulations, than in previous years. Therefore, tough rules have been applied on gasoline specifications. Octane number is a key variable of gasoline quality. Isomerization is one of many processes that generate profit by increasing low gasoline octane numbers, with better environmental impacts compared to other processes. Here we analyzed and optimized the various variables affecting the isomerate octane numbers produced by isomerization. Feed composition (naphthenes and benzene content in the feed) and operating conditions (temperature, hydrogen consumption and liquid hourly space velocity) data were collected from the Midor isomerization plant (Egypt) over a 4-year period based on the catalyst lifetime. These data were then used to predict the influence of feed composition and operating conditions on isomerate research octane number using a Response Surface Methodology (RSM) approach (Design Expert software). Thus, we were able to predict isomerate research octane number under various operating conditions. All of the studied variables were found to influence octane number. Keywords: Isomerization process, Operating variables, Octane number, Gasolin

GNNGLY: Graph Neural Networks for Glycan Classification

Glycans are important biological molecules that can be found on their own or attached to other molecules. They have complex, branching structures that do not follow the linear structure. Glycans are crucial for many biological processes and they are involved in the development of several important diseases. Due to the complexity and the branched structure of glycans, most of the current studies have mainly focused on the other attached molecules instead of glycans themselves. This paper proposes, GNNGLY, a graph neural networks model for glycans classification. Firstly, Glycans are represented as molecular graphs, where atoms are represented as nodes and bonds are represented as edges. Graph convolutional networks (GCNs) are then used to make predictions on eight taxonomic classification levels and for the level of immunogenicity property. The performance results indicate that GNNGLY outperforms traditional machine learning methods and when compared to other existing tools for glycan classification, GNNGLY showed considerable performance results. GNNGLY could have a significant impact on the field of glycoinformatics and related research areas