Transylvania University of Brașov

Allergic rhinitis is a symptomatic disorder of the nose\ninduced after allergen exposure by an IgE-mediated\ninflammation of the membranes lining the nose. It is a\nglobal health problem that causes major illness and disability\nworldwide. Over 600 million patients from all\ncountries, all ethnic groups and of all ages suffer from\nallergic rhinitis. It affects social life, sleep, school and\nwork and its economic impact is substantial.\nRisk factors for allergic rhinitis are well identified.\nIndoor and outdoor allergens as well as occupational\nagents cause rhinitis and other allergic diseases.\nThe role of indoor and outdoor pollution is probably\nvery important, but has yet to be fully understood\nboth for the occurrence of the disease and its manifestations.\nIn 1999, during the Allergic Rhinitis and its Impact on\nAsthma (ARIA) WHO workshop, the expert panel\nproposed a new classification for allergic rhinitis which\nwas subdivided into _intermittent_ or _persistent_ disease.\nThis classification is now validated.\nThe diagnosis of allergic rhinitis is often quite easy, but\nin some cases it may cause problems and many patients\nare still under-diagnosed, often because they do not\nperceive the symptoms of rhinitis as a disease impairing\ntheir social life, school and work.\nThe management of allergic rhinitis is well established\nand the ARIA expert panel based its recommendations\non evidence using an extensive review of the literature\navailable up to December 1999. The statements of\nevidence for the development of these guidelines followed\nWHO rules and were based on those of Shekelle et al.\nA large number of papers have been published since 2000\nand are extensively reviewed in the 2008 Update using\nthe same evidence-based system. Recommendations for\nthe management of allergic rhinitis are similar in both the\nARIA workshop report and the 2008 Update. In the\nfuture, the GRADE approach will be used, but is not yet\navailable.\nAnother important aspect of the ARIA guidelines was\nto consider co-morbidities. Both allergic rhinitis and\nasthma are systemic inflammatory conditions and often\nco-exist in the same patients. In the 2008 Update, these\nlinks have been confirmed.\nTheARIAdocument is not intended to be a standard-ofcare\ndocument for individual countries. It is provided as a\nbasis for physicians, health care professionals and\norganizations involved in the treatment of allergic rhinitis\nand asthma in various countries to facilitate the\ndevelopment of relevant local standard-of-care documents\nfor patients.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

The conservation of large carnivores is a formidable challenge for biodiversity conservation. Using a data set on the past and current status of brown bears (Ursus arctos), Eurasian lynx (Lynx lynx), gray wolves (Canis lupus), and wolverines (Gulo gulo) in European countries, we show that roughly one-third of mainland Europe hosts at least one large carnivore species, with stable or increasing abundance in most cases in 21st-century records. The reasons for this overall conservation success include protective legislation, supportive public opinion, and a variety of practices making coexistence between large carnivores and people possible. The European situation reveals that large carnivores and people can share the same landscape.

Abstract The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

The research focuses on identifying the way in which Romanian universities managed to provide knowledge during the Coronavirus pandemic, when, in a very short time, universities had to adapt the educational process for exclusively online teaching and learning. In this regard, we analyzed students’ perception regarding online learning, their capacity to assimilate information, and the use of E-learning platforms. An online survey based on a semi-structured questionnaire was conducted. Data was collected from 762 students from two of the largest Romanian universities. The results of the research revealed that higher education institutions in Romania were not prepared for exclusively online learning. Thus, the advantages of online learning identified in other studies seem to diminish in value, while disadvantages become more prominent. The hierarchy of problems that arise in online learning changes in the context of the crisis caused by the pandemic. Technical issues are the most important, followed by teachers’ lack of technical skills and their teaching style improperly adapted to the online environment. However, the last place was assigned by students to the lack of interaction with teachers or poor communication with them. Based on these findings, research implications for universities and researchers are discussed.

About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement.

The availability of allergen molecules ('components') from several protein families has advanced our understanding of immunoglobulin E (IgE)-mediated responses and enabled 'component-resolved diagnosis' (CRD). The European Academy of Allergy and Clinical Immunology (EAACI) Molecular Allergology User's Guide (MAUG) provides comprehensive information on important allergens and describes the diagnostic options using CRD. Part A of the EAACI MAUG introduces allergen molecules, families, composition of extracts, databases, and diagnostic IgE, skin, and basophil tests. Singleplex and multiplex IgE assays with components improve both sensitivity for low-abundance allergens and analytical specificity; IgE to individual allergens can yield information on clinical risks and distinguish cross-reactivity from true primary sensitization. Part B discusses the clinical and molecular aspects of IgE-mediated allergies to foods (including nuts, seeds, legumes, fruits, vegetables, cereal grains, milk, egg, meat, fish, and shellfish), inhalants (pollen, mold spores, mites, and animal dander), and Hymenoptera venom. Diagnostic algorithms and short case histories provide useful information for the clinical workup of allergic individuals targeted for CRD. Part C covers protein families containing ubiquitous, highly cross-reactive panallergens from plant (lipid transfer proteins, polcalcins, PR-10, profilins) and animal sources (lipocalins, parvalbumins, serum albumins, tropomyosins) and explains their diagnostic and clinical utility. Part D lists 100 important allergen molecules. In conclusion, IgE-mediated reactions and allergic diseases, including allergic rhinoconjunctivitis, asthma, food reactions, and insect sting reactions, are discussed from a novel molecular perspective. The EAACI MAUG documents the rapid progression of molecular allergology from basic research to its integration into clinical practice, a quantum leap in the management of allergic patients.

Allergic rhinoconjunctivitis (AR) is an allergic disorder of the nose and eyes affecting about a fifth of the general population. Symptoms of AR can be controlled with allergen avoidance measures and pharmacotherapy. However, many patients continue to have ongoing symptoms and an impaired quality of life; pharmacotherapy may also induce some side-effects. Allergen immunotherapy (AIT) represents the only currently available treatment that targets the underlying pathophysiology, and it may have a disease-modifying effect. Either the subcutaneous (SCIT) or sublingual (SLIT) routes may be used. This Guideline has been prepared by the European Academy of Allergy and Clinical Immunology's (EAACI) Taskforce on AIT for AR and is part of the EAACI presidential project "EAACI Guidelines on Allergen Immunotherapy." It aims to provide evidence-based clinical recommendations and has been informed by a formal systematic review and meta-analysis. Its generation has followed the Appraisal of Guidelines for Research and Evaluation (AGREE II) approach. The process included involvement of the full range of stakeholders. In general, broad evidence for the clinical efficacy of AIT for AR exists but a product-specific evaluation of evidence is recommended. In general, SCIT and SLIT are recommended for both seasonal and perennial AR for its short-term benefit. The strongest evidence for long-term benefit is documented for grass AIT (especially for the grass tablets) where long-term benefit is seen. To achieve long-term efficacy, it is recommended that a minimum of 3 years of therapy is used. Many gaps in the evidence base exist, particularly around long-term benefit and use in children.

During 2015 the ATLAS experiment recorded [Formula: see text] of proton-proton collision data at a centre-of-mass energy of [Formula: see text]. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton-proton collision data.

After the Paris Climate Conference (COP21), many countries start progressing towards carbon neutrality targets. In doing so, green technology innovations (GTIs) and clean energy are the essential factors that can help to achieve the carbon neutrality goal. Therefore, this paper examines the linkages between green technology innovation and renewable energy and carbon dioxide emissions based on the STIRPAT model in Turkey during the time of 1990-2018. The study used testing like "unit-root" to verify the variables' integrative properties containing the information for structural breaks. Also, the bootstrapping ARDL-bound testing technique is used to analyze the relationship between the variables. The causal relationship between green technology innovation, energy consumption, renewable energy, population, income per capita, and carbon dioxide emissions is tested through a Granger causality test. The empirical findings show that green technology innovation, renewable energy, energy consumption, population, income per capita, and carbon dioxide emissions are co-integrated for the long-term association. Additionally, green technology innovation and renewable energy decline carbon dioxide emissions, whereas energy consumption, population, and per capita enhance carbon emissions. This paper helps the policymakers design a comprehensive policy for strengthening environmental sustainability through green technology innovation and renewable energy, specifically in the region of Turkey.

Food allergy can result in considerable morbidity, impairment of quality of life, and healthcare expenditure. There is therefore interest in novel strategies for its treatment, particularly food allergen immunotherapy (FA-AIT) through the oral (OIT), sublingual (SLIT), or epicutaneous (EPIT) routes. This Guideline, prepared by the European Academy of Allergy and Clinical Immunology (EAACI) Task Force on Allergen Immunotherapy for IgE-mediated Food Allergy, aims to provide evidence-based recommendations for active treatment of IgE-mediated food allergy with FA-AIT. Immunotherapy relies on the delivery of gradually increasing doses of specific allergen to increase the threshold of reaction while on therapy (also known as desensitization) and ultimately to achieve post-discontinuation effectiveness (also known as tolerance or sustained unresponsiveness). Oral FA-AIT has most frequently been assessed: here, the allergen is either immediately swallowed (OIT) or held under the tongue for a period of time (SLIT). Overall, trials have found substantial benefit for patients undergoing either OIT or SLIT with respect to efficacy during treatment, particularly for cow's milk, hen's egg, and peanut allergies. A benefit post-discontinuation is also suggested, but not confirmed. Adverse events during FA-AIT have been frequently reported, but few subjects discontinue FA-AIT as a result of these. Taking into account the current evidence, FA-AIT should only be performed in research centers or in clinical centers with an extensive experience in FA-AIT. Patients and their families should be provided with information about the use of FA-AIT for IgE-mediated food allergy to allow them to make an informed decision about the therapy.

Combined measurements of Higgs boson production and decay using up to 80

Severe droughts have the potential to reduce forest productivity and trigger tree mortality. Most trees face several drought events during their life and therefore resilience to dry conditions may be crucial to long-term survival. We assessed how growth resilience to severe droughts, including its components resistance and recovery, is related to the ability to survive future droughts by using a tree-ring database of surviving and now-dead trees from 118 sites (22 species, >3,500 trees). We found that, across the variety of regions and species sampled, trees that died during water shortages were less resilient to previous non-lethal droughts, relative to coexisting surviving trees of the same species. In angiosperms, drought-related mortality risk is associated with lower resistance (low capacity to reduce impact of the initial drought), while it is related to reduced recovery (low capacity to attain pre-drought growth rates) in gymnosperms. The different resilience strategies in these two taxonomic groups open new avenues to improve our understanding and prediction of drought-induced mortality.

IMPORTANCE: Myelofibrosis (MF) is a BCR-ABL-negative myeloproliferative neoplasm characterized by anemia, splenomegaly, debilitating constitutional symptoms, and shortened survival. Fedratinib, a JAK2-selective inhibitor, previously demonstrated clinically beneficial activity in patients with MF in early-phase trials. OBJECTIVE: To evaluate the efficacy and safety of fedratinib therapy in patients with primary or secondary (post-polycythemia vera or post-essential thrombocythemia) MF. DESIGN, SETTING, AND PARTICIPANTS: Double-blind, randomized, placebo-controlled phase 3 study in 94 sites in 24 countries in which 289 adult patients (≥18 years of age) with intermediate-2 or high-risk primary MF, post-polycythemia vera MF, or post-essential thrombocythemia MF were randomly assigned between December 2011 and September 2012 to once-daily oral fedratinib, at a dose of 400 mg or 500 mg, or placebo, for at least 6 consecutive 4-week cycles. MAIN OUTCOMES AND MEASURES: The primary end point was spleen response (≥35% reduction in spleen volume from baseline as determined by magnetic resonance imaging or computed tomography) at week 24 and confirmed 4 weeks later. The main secondary end point was symptom response (≥50% reduction in total symptom score, assessed using the modified Myelofibrosis Symptom Assessment Form). RESULTS: The primary end point was achieved by 35 of 96 (36% [95% CI, 27%-46%]) and 39 of 97 (40% [95% CI, 30%-50%]) patients in the fedratinib 400-mg and 500-mg groups, vs 1 of 96 (1% [95% CI, 0%-3%]) in the placebo group (P < .001). Symptom response rates at week 24 were 33 of 91 (36% [95% CI, 26%-46%]), 31 of 91 (34% [95% CI, 24%-44%]), and 6 of 85 (7% [95% CI, 2%-13%]) in the fedratinib 400-mg, 500-mg, and placebo groups, respectively (P < .001). Common adverse events with fedratinib treatment were anemia, gastrointestinal symptoms, and increased levels of liver transaminases, serum creatinine, and pancreatic enzymes. Encephalopathy was reported in 4 women who received fedratinib 500 mg/d. A diagnosis of Wernicke encephalopathy was supported by magnetic resonance imaging in 3 cases and suspected clinically in 1 case. CONCLUSIONS AND RELEVANCE: Fedratinib therapy significantly reduced splenomegaly and symptom burden in patients with MF. These benefits were accompanied by toxic effects in some patients, the most important being encephalopathy of unknown mechanism. Clinical development of fedratinib was subsequently discontinued. TRIAL REGISTRATION: clinicaltrials.gov identifier: NCT01437787.

Machining operation optimization improves the quality of the product, reduces cost, enhances overall efficiency by reducing human error, and enables consistent and efficient operation. It is a vital decision-making process and achieves the best solution within constraints. It reduces reliance on machine-tool technicians and handbooks to identify cutting parameters, as a lack of awareness of the optimal combination of machining parameters leads to several machining inefficiencies. Subsequently, the optimization of the machining process is more useful for units of production, particularly machining units. In multi-objective optimization (MOO) problems, weights of importance are assigned, mostly identical. But, nowadays, the weights assignment techniques have received a lot of consideration from the professionals and researchers in MOO problems. Various techniques are developed to assign weights of significance to responses in MOO. The Entropy weights method (EWM) continues to work pleasingly across diverse machining operations to allocate objective weights. In this paper, a literature review is conducted to classify the articles on EWM applications in machining operations. The categorization proposal for the EWM reviews included 65 academic articles from different journals, books, and conferences since the year 2009. The EWM applications were separated into 18 categories of conventional and non-conventional machining operations. The implementation procedure of EWM is presented with an example along with method development. Scholarly articles in the EWM applications are further inferred based on (1) implementation of EWM in different machining operations, (2) MOO methods used with entropy weights in machining operations, (3) application of entropy weights by citation index and publication year, and (4) entropy weights applications in other fields. The review paper provided constructive insight into the EWM applications and ended with suggestions for further research in machining and different areas.

Abstract Hymenoptera venom allergy is a potentially life‐threatening allergic reaction following a honeybee, vespid, or ant sting. Systemic‐allergic sting reactions have been reported in up to 7.5% of adults and up to 3.4% of children. They can be mild and restricted to the skin or moderate to severe with a risk of life‐threatening anaphylaxis. Patients should carry an emergency kit containing an adrenaline autoinjector, H 1 ‐antihistamines, and corticosteroids depending on the severity of their previous sting reaction(s). The only treatment to prevent further systemic sting reactions is venom immunotherapy. This guideline has been prepared by the European Academy of Allergy and Clinical Immunology's ( EAACI ) Taskforce on Venom Immunotherapy as part of the EAACI Guidelines on Allergen Immunotherapy initiative. The guideline aims to provide evidence‐based recommendations for the use of venom immunotherapy, has been informed by a formal systematic review and meta‐analysis and produced using the Appraisal of Guidelines for Research and Evaluation ( AGREE II ) approach. The process included representation from a range of stakeholders. Venom immunotherapy is indicated in venom‐allergic children and adults to prevent further moderate‐to‐severe systemic sting reactions. Venom immunotherapy is also recommended in adults with only generalized skin reactions as it results in significant improvements in quality of life compared to carrying an adrenaline autoinjector. This guideline aims to give practical advice on performing venom immunotherapy. Key sections cover general considerations before initiating venom immunotherapy, evidence‐based clinical recommendations, risk factors for adverse events and for relapse of systemic sting reaction, and a summary of gaps in the evidence.

Abstract The European Vegetation Archive ( EVA ) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation‐ plot databases on a single software platform. Data storage in EVA does not affect on‐going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA , contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large‐scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database .

Abstract Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.

Abstract Five biologicals have been approved for severe eosinophilic asthma, a well‐recognized phenotype. Systematic reviews (SR) evaluated the efficacy and safety of benralizumab, dupilumab, mepolizumab, omalizumab and reslizumab (alphabetical order) compared to standard of care for severe eosinophilic asthma. PubMed, Embase and Cochrane Library were searched to identify RCTs and health economic evaluations, published in English. Critical and important asthma‐related outcomes were evaluated for each of the biologicals. The risk of bias and the certainty of the evidence were assessed using GRADE. 19 RCTs (three RCTs for benralizumab, three RCTs for dupilumab, three RCTs for mepolizumab, five RCTs for omalizumab and five RCTs for reslizumab), including subjects 12 to 75 years old (except for omalizumab including also subjects 6‐11 years old), ranging from 12 to 56 weeks were evaluated. All biologicals reduce exacerbation rates with high certainty of evidence: benralizumab incidence rate ratio (IRR) 0.53 (95% CI 0.39 to 0.72), dupilumab (IRR) 0.43 (95% CI 0.32 to 0.59), mepolizumab IRR 0.49 (95% CI 0.38 to 0.66), omalizumab (IRR) 0.56 (95% CI 0.40 to 0.77) and reslizumab (IRR) 0.46 (95% CI 0.37 to 0.58). Benralizumab, dupilumab and mepolizumab reduce the daily dose of oral corticosteroids (OCS) with high certainty of evidence. All evaluated biologicals probably improve asthma control, QoL and FEV 1 , without reaching the minimal important difference (moderate certainty). Benralizumab, mepolizumab and reslizumab slightly increase drug‐related adverse events (AE) and drug‐related serious AE (low to very low certainty of evidence). The incremental cost‐effectiveness ratio per quality‐adjusted life year value is above the willingness to pay threshold for all biologicals (moderate certainty). Potential savings are driven by decrease in hospitalizations, emergency and primary care visits. There is high certainty that all approved biologicals reduce the rate of severe asthma exacerbations and for benralizumab, dupilumab and mepolizumab for reducing OCS. There is moderate certainty for improving asthma control, QoL, FEV 1 . More data on long‐term safety are needed together with more efficacy data in the paediatric population.

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.