Thermal conductivity modeling of nanofluids

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.In the present work, a mathematical model for predicting effective thermal conductivity of nanofluids containing spherical nanoparticles is developed. This model takes into account the effects of an interfacial nanolayer formed by liquid molecule layering on the particle liquid interface as well as microconvection caused by thermal motion of nanoparticles. The present model has been proposed in order to calculate the effective thermal conductivity of nanofluids. The model accounts for the enhancement in effective thermal conductivity of a nanofluid with respect to the suspended nanoparticles size, volume fraction, temperature and thermal conductivities of the nanoparticle and base fluid. The results show that the prediction capability of the developed model is good by the way of comparison with the existing recent experimental data

Bench scale production of xanthan from date extract by Xanthomonas campestris in submerged fermentation using central composite design

In this research, xanthan production from date extract was done by using bacterium Xanthomonas campestris PTCC1473 in submerged fermentation (SmF). The impact of different initial concentrations of carbon (date extract) and nitrogen (NH4NO3) sources on cell growth and xanthan production was evaluated. Inoculum (72 h) from the YMB medium was added to the substrate with defined chemical components (nitrogen, sugar, moisture, ash and pH) and incubated. Central composite design (CCD) was used to evaluate the effects of carbon and nitrogen sources on production yield. The results indicate a decrease of cell growth in carbon source concentration up to 50 g/l. The highest cell growth and xanthan production were achieved at 40 g/l concentration of carbon source. The nitrogen source concentration did not cause a significant effect on cell growth; but the highest concentration of xanthan was produced in 0.2 g/l of nitrogen source. The ratio of carbon to nitrogen content had a significant impact on xanthan production. In the optimum condition, maximum concentration of produced xanthan yield and productivity was seen at 11.2 g/l and 8.19 g/kg.day, respectively.Key words: Xanthan, submerged fermentation, date extract, Xanthomonas campestris, central composite design

Indicators Affecting the Urban Resilience with a Scenario Approach in Tehran Metropolis

Urban resilience refers to the capacity of an urban system to fully recover from unforeseen calamities. This study aims to assess the physical resilience indicators used to measure urban resilience in Tehran, the political and economic capital of Iran, and to pinpoint the most significant direct and indirect influences on urban resilience. The research process divided into two parts. The environmental scanning approach (reviewing papers and published sources, interviewing specialists, and monitoring conferences) and the literature review were employed in the first part to compile a database of the key information on the elements impacting physical resilience. The most significant factors impacting physical resilience over the next ten years were requested to be identified by specialists and intellectuals in the second part. Finally, the MicMac program was used to analyze the data after 29 variables were specified in Delphi. In light of the trace-analysis-dependence diagram, which depicts the instability of the influential factors and the persistence of their impact on other variables, the results demonstrate that Tehran’s physical resilience is in an unstable condition. According to the results, the factors that have the maximum impact on other variables are granularity drivers, emergency evacuation capacity, rescue and security spaces (emergency, fire station, and police station), impermeability, rate of the amendment and retrofitting measures in the buildings of each zone, building age, and the compatibility of land uses. The variables that are most susceptible to change from other variables include the distribution status of dangerous land uses, the quality of the buildings, the rate of historically vulnerable buildings, the vulnerability of internal and external roads, the rate of improvements and retrofitting measures in buildings in each zone, as well as historically vulnerable historical buildings

Effizienter Erwerb chirurgischer Basistechniken durch "blended learning"

Zusammenfasssung: Hintergrund: Große Studierendenzahlen und heterogene Dozierende erschweren einheitliche Kursgestaltungen und die objektive Standardisierung von Prüfungen im chirurgischen Fertigkeitstraining. Diese Arbeit zeigt die Vorteile des Einsatzes neuer Medien im "Blended-learning-Konzept" für das Fertigkeitstraining im Studiengang Humanmedizin der Universität Basel. Material und Methoden: Der studentische chirurgische "Nahtkurs" wurde nach einem Blended-learning-Konzept mit multimedialer CD, Präsenzveranstaltung und SkillsLab restrukturiert. Die Lernziele des Kurses wurden am Ende der Studienjahre anhand von Posten mit Checklisten im OSCE ("objective structured clinical examination") überprüft. Die studentische Kursbeurteilung sowie die Prüfungsergebnisse vor und nach Einführung des "blended learning" wurden miteinander verglichen. Ergebnisse: Sowohl die Beurteilungen der eingesetzten Lehrmittel, des subjektiven Übungserfolges und des prospektiven Nutzens für das Wahlstudienjahr (Praktisches Jahr) als auch die Gesamtkursbeurteilung waren nach Einführung des Blended-learning-Konzeptes signifikant höher als im alten Kursformat. Auch der Anteil an bestandenen Prüfungen war mit einem Zuwachs von 10% signifikant im Vergleich zum alten Kurs erhöht. Schlussfolgerung: "Blended learning” kann sowohl Wahrnehmung und Leistung als auch die Effizienz des Fertigkeitstrainings und der Betreuungszeit verbessern. Dadurch werden indirekt Ressourcen gespart. Chirurgische Verfahren können klar und übersichtlich vermittelt werde

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient’s survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient’s likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises

Learning Everything about Anything: Webly-Supervised Visual Concept Learning

Figure 1: We introduce a fully-automated method that, given any concept, discovers an exhaustive vocabulary explaining all its appearance variations (i.e., actions, interactions, attributes, etc.), and trains full-fledged detection models for it. This figure shows a few of the many variations that our method has learned for four different classes of concepts: object (horse), scene (kitchen), event (Christmas), and action (walking). Recognition is graduating from labs to real-world ap-plications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance varia-tions, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. Our approach leverages vast resources of on-line books to discover the vocabulary of variance, and in-tertwines the data collection and modeling steps to alleviate the need for explicit human supervision in training the mod-els. Our approach organizes the visual knowledge about a concept in a convenient and useful way, enabling a variety of applications across vision and NLP. Our online system has been queried by users to learn models for several inter-esting concepts including breakfast, Gandhi, beautiful, etc. To date, our system has models available for over 50,000 variations within 150 concepts, and has annotated more than 10 million images with bounding boxes. 1

Effects of intravenous Semelil (ANGIPARS�) on diabetic foot ulcers healing: A multicenter clinical trial

Some diabetic foot ulcers, which are notoriously difficult to cure, are one of the most common health problems in diabetic patients .There are several surgical and medical options which already have been introduced for treatment of diabetic foot ulcers, so some patient will require amputation. The purpose of this study was to evaluate the efficacy of intravenous Semelil (ANGIPARS�), a naive herbal extract to accelerate healing of diabetic foot ulcers. A multi-centric randomized controlled trial was conducted to evaluate intravenous Semelil for healing of diabetic foot ulcers. Sixteen diabetic patients were treated with intravenous Semelil, and nine other patients were treated with placebo as control group. Both groups were otherwise treated by wound debridement and irrigation with normal saline solution, systemic antibiotic therapy and daily wound dressing. Before and after intervention, the foot ulcer surface area was measured, by digital photography, mapping and planimetry. After 4 weeks, the mean foot ulcer surface area decreased from 479.93±379.75 mm2 to 198.93±143.75 mm2 in the intervention group (p = 0.000) and from 766.22±960.50 mm2 to 689.11±846.74 mm2 in the control group (p = 0.076). Average wound closure in the treatment group was significantly greater than placebo group (64 vs. 25, p= 0.015). This herbal extract by intravenous rout in combination with conventional therapy is more effective than conventional therapy by itself probably without side effect. However, further studies are required in the future to confirm these results in larger population

Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Management of Low-Grade Gliomas and Radiation Necrosis: A Single-Institution Case Series

Background: Laser interstitial thermal therapy (LITT) has emerged as a minimally invasive treatment modality for ablation of low-grade glioma (LGG) and radiation necrosis (RN). Objective: To evaluate the efficacy, safety, and survival outcomes of patients with radiographically presumed recurrent or newly diagnosed LGG and RN treated with LITT. Methods: The neuro-oncological database of a quaternary center was reviewed for all patients who underwent LITT for management of LGG between 1 January 2013 and 31 December 2020. Clinical data including demographics, lesion characteristics, and clinical and radiographic outcomes were collected. Kaplan-Meier analyses comprised overall survival (OS) and progression-free survival (PFS). Results: Nine patients (7 men, 2 women; mean [SD] age 50 [16] years) were included. Patients underwent LITT at a mean (SD) of 11.6 (8.5) years after diagnosis. Two (22%) patients had new lesions on radiographic imaging without prior treatment. In the other 7 patients, all (78%) had surgical resection, 6 (67%) had intensity-modulated radiation therapy and chemotherapy, respectively, and 4 (44%) had stereotactic radiosurgery. Two (22%) patients had lesions that were wild-type IDH1 status. Volumetric assessment of preoperative T1-weighted contrast-enhancing and T2-weighted fluid-attenuated inversion recovery (FLAIR) sequences yielded mean (SD) lesion volumes of 4.1 (6.5) cm(3) and 26.7 (27.9) cm(3), respectively. Three (33%) patients had evidence of radiographic progression after LITT. The pooled median (IQR) PFS for the cohort was 52 (56) months, median (IQR) OS after diagnosis was 183 (72) months, and median (IQR) OS after LITT was 52 (60) months. At the time of the study, 2 (22%) patients were deceased. Conclusions: LITT is a safe and effective treatment option for management of LGG and RN, however, there may be increased risk of permanent complications with treatment of deep-seated subcortical lesions

Association of interleukin 1 gene cluster and interleukin 1 receptor gene polymorphisms with ischemic heart failure

BACKGROUND: Proinfl ammatory cytokines have been known to play a considerable part in the pathomechanisms of chronic heart failure (CHF). Given the importance of proinfl ammatory cytokines in the context of the failing heart, we assessed whether the polymorphisms of interleukin (IL)-1 gene cluster, including IL-1a, IL-1β, and IL-1 receptor antagonist (IL-1RA) and IL-1R gene are predictors of CHF due to ischemic heart disease. METHODS: Forty- three patients with ischemic heart failure were recruited in this study as patients group and compared with 140 healthy unrelated control subjects. Using polymerase chain reaction with sequence-specifi c primers method, the allele and genotype frequency of 5 single nucleotide polymorphisms (SNPs) within the IL- 1a (-889), IL-1β (-511, +3962), IL-1R (psti 1970), and IL-1RA (mspa1 11100) genes were determined.RESULTS: The frequency of the IL-1β -511/C allele was signifi cantly higher in the patient group compared to that in the control group (p = 0.031). The IL-1β (-511) C/C genotype was signifi cantly overrepresented in patients compared to controls (p = 0.022). CONCLUSIONS: Particular allele and genotype in IL-1β gene were overrepresented in patients with ischemic heart failure, possibly affecting the individual susceptibility to this disease (Tab. 1, Ref. 27). Text in PDF www.elis.sk