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Forecasting the China Container Freight Index with Ensemble Models
No full textKonteyner taşımacılığında navlun oranlarının nasıl değişeceğini sektördeki paydaşların öngörebilmesi oldukça önemlidir. Bu çalışma, literatürde ilk defa olmak üzere, konteyner taşımacılığında navlun oranlarının değişimini gösteren en önemli göstergelerden biri olan CCFI (Çin Konteyner Navlun Endeksi) 'nin tahmini için toplu zaman serisi modelleri sunmaktadır. Çalışmanın sonuçları, modellerin CCFI’nin tahmininde oldukça iyi sonuçlar verdiğini ve önemli bir karar destek sistemi olarak kullanılabileceğini göstermektedir.Stakeholders in the sector need to be able to predict how freight rates will change in container transportation. For the first time in the literature, this study presents aggregate time series models for the prediction of CCFI (China Container Freight Index), one of the most critical indicators showing the change of freight rates in container shipping. The study results show that the models provide promising results in forecasting CCFI and can be used as an essential decision support system
Predicting financial distress using supervised machine learning algorithms : An application on Borsa Istanbul
No full textABSTRACT Purpose- The main purpose of this study is to identify the most significant variables to detect financial distress earlier and to find the best machine learning algorithm model. Methodology-This study has used Support Vector Machine, Logistic Regression, Random Forest and K-nearest neighbors method techniques to predict the financial distress prediction for the companies of Turkey between 2012 and 2021. Findings- As a result of the study, it has been determined that Random Forest provides the best results in terms of precision, accuracy, and recall. Further, this study has found the most important five independent variables to determine the financial distress status of the firms. In this way, it has been found that Current Assets/ Current Liabilities, Working Capital / Total Assets, Gross profit / Revenue, Retained Earnings / Total Assets and Sales growth rate are the most useful variables to determine financial distress status of Turkish firms earlier. Conclusion- This study has concluded that cash ratios and profitability ratios and sales growth are the most important independent variables to determine financial distress one-year ahead. Furthermore, it has been found that random forest is the best machine learning method among other supervised machine learning methods used in this study
How refugee entrepreneurs improvise: bricolage in an emerging economy
No full textThis comprehensive Research Handbook provides insights into entrepreneurship across a range of country contexts, migration corridors and national policies to provide a collection of conceptual, empirical and policy-focused findings addressing transnational diaspora entrepreneurship. Chapters illustrate the phenomenon, considering what it is, how it works and how it is regulated
Artificial intelligence versus natural intelligence in mineral processing
Get PDFThis article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, MLMachine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use
Biocompatibility of polymers
No full textBiocompatibility is defined as the ability of a material to perform with an appropriate host response in a specific application. Biocompatible polymers have gained significant importance during the past decades due to their capability to meet specific requirements for various applications such as tissue engineering, genetic disease treatment, and drug delivery. Biocompatibility testing of polymer-based medical devices is an essential requirement for regulatory approval. “EN ISO 10993 Biological evaluation of medical devices” includes several substandards prepared to manage biological risk and evaluate the biocompatibility of medical devices. The source of the polymeric biomaterial is determined by which biocompatibility test methods should be applied to the material itself. In this chapter, we present biocompatibility test methods and the main requirements of some common biocompatible polymers. In addition, we introduce some typical biomedical applications of these polymers after a detailed explanation of common natural and synthetic polymers. © 2023 Elsevier Ltd. All rights reserved
Bioreactors for tissue engineering
No full textBioreactors have been widely used in various fields of biological production for many years. Their ability to provide a tightly controlled environment during the process and to allow for monitoring and intervention to the process parameters make them quite favorable to use in biological production lines. Also, bioreactors are widely employed in tissue engineering applications. Ideally, a tissue engineering bioreactor should have the capability to effectively regulate various environmental factors, such as pH, oxygen levels, temperature, nutrient transportation and waste elimination. Additionally, it should facilitate sterile operations, such as sampling and feeding, as well as automated procedures. The general approach for these applications include immobilization of suitable cells within porous, biodegradable and biocompatible scaffolds. These scaffolds serve as frameworks for tissue formation and the cell/scaffold constructs are cultured within a bioreactor, which creates a dynamic in vitro setting conducive to tissue growth. As the technology for these systems and required conditions continue to become more complex, these bioreactor designs will also evolve with time to help treat patients with diseases related to tissue damage. There are specific designs for various kinds of bioreactors (spinner flasks, rotating wall vessel bioreactors, perfusion systems, pulsatile systems, strain systems, hollow fiber systems, wave bioreactors, microfluidic bioreactors, compression and hydrostatic systems) in the market which allows better outcomes for certain applications such as cardiovascular tissue engineering, bladder tissue engineering, neural tissue engineering, cornea tissue engineering, kidney tissue engineering, musculoskeletal tissue engineering, lung tissue engineering and gastrointestinal tissue engineering. All of these different systems and their special applications for tissue engineering studies are explained in this chapter with their specific advantages and disadvantages which make them favorable with the physicochemical environment they provide. When current developments are examined and evaluated, it is seen that bioreactors will have enhanced designs that will help them better mimic the physiological pathways of cells, tissues and their interaction with the surroundings to have better solutions for whole organ, bone, and regenerative tissue engineering applications in the future
Characterization of site-isolated iridium atoms supported on reduced graphene aerogel at an exceptional Ir loading of 23.8 wt% with synchrotron and electron microscopy techniques
Get PDFAtomically dispersed supported metal catalysts provide substantial advantages when compared to their traditional counterparts. They offer 100% dispersion of expensive noble metals, exhibit intriguing catalytic properties, and provide insights into the relationship between structure and catalytic activity [1]. However, there are several challenges these novel class of catalysts are facing, such as their limited stability in reaction environment and limited metal loadings (typically below 1 wt%). To overcome these issues, we have used a novel and promising support, reduced graphene aerogel (rGA), consisting of numerous bonding sites for Ir, having a significantly high surface area (>700 m2 /g), and great electronic properties. Thanks to these outstanding features of rGA, we achieved an exceptional Ir loading of 23.8 wt%. The presence of individual Ir atoms at such high loading on the surface of rGA was confirmed by combining atomic-resolution images obtained using a Hitachi HF5000 Cs-corrected cold FEG aberrationcorrected scanning transmission electron microscope and X-ray absorption spectroscopy data. These findings highlight rGA's potential as an exceptional support material for expensive noble metal complexes to achieve extraordinary loadings in atomically dispersed supported metal catalysts
Group identity fabrication theory - A communication-ecological account with social-theoretical implications
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A stacked multi-sensor platform for real-time MRI guided interventions
No full textWe present a stacked temperature, pressure, and localization platform, targeted for minimally invasive surgical and diagnostic applications under Magnetic Resonance Imaging. The platform comprises a micro-fabricated three-layer (Titanium-Parylene-Titanium) membrane pressure sensor, a Gallium Arsenide band-gap temperature sensor, and a magnetic material on double prism retro-reflector that benefits from Magneto-Optic Kerr effect as a magnetic field sensor, to provide localization feedback under Magnetic Resonance Imaging. All sensors can be addressed with a single fiber optic cable, where the collected light is directed to a spectrometer and a polarimeter. For the three-layer microfabricated membrane sensor, an analytical formulation is derived, linking the pressure to optical intensity. Moreover, finite-element simulation results are provided, verifying analytical findings. Wavelength division multiplexing is exploited to address the sensors simultaneously. We measured sensitivities of 0.025 millidegree/Gauss rotation of polarization, 1.5 nm/mmHg displacement (in agreement with simulation results and analytical findings), 0.36 nm/degrees C. bandgap wavelength shift for magnetic field, pressure, and temperature sensors; respectively. With further development, the proposed device can be adapted to a clinical setting for use in Magnetic Resonance assisted surgical procedures
Corrosion response and biocompatibility of graphene oxide (GO)–serotonin (Ser) coatings on Ti6Al7Nb and Ti29Nb13Ta4.6Zr (TNTZ) alloys fabricated by electrophoretic deposition (EPD
Get PDFIn this study, Ti6Al7Nb and Ti29Nb13Ta4.6Zr (TNTZ) alloys coated with graphene oxide (GO) and serotonin (Ser) by electrophoretic deposition (EPD) technique were evaluated for possible usage as an orthopedic implant in terms of their in-vitro corrosion response, biocompatibility, and wettability. In-vitro corrosion analyses were carried out to determine the electrochemical response of the coatings in Hanks’ solution (named as SBF) at body temperature (37 ?C). Biocompatibility of the coated materials was evaluated by direct contact method using normal mouse calvarial preosteoblast cell line (MC3T3-E1 Subclone 4). To this purpose, cytotoxic effect and cell proliferation rate were evaluated. The wettability test was performed using static contact angle method (sessile drop technique). The results showed that only GO and GO+Ser coatings had a negative effect on the corrosion resistance of TNTZ alloy. However, the Icorr value of the GO+Ser coatings improved almost 2 and 4 times compared to only GO coated Ti6Al7Nb and uncoated Ti6Al7Nb, respectively. GO+Ser coating made the substrates more hydrophilic, making the surface more suitable for protein adsorption and cell adhesion. Obtained results showed that GO+Ser coated Ti6Al7Nb was more favorable to osteoblast survival (106% viability after 24- h incubation), adhesion and proliferation (almost 6 times faster after 3 days of incubation) compared to only GO coated Ti6Al7Nb (87% viability). Confocal microscope analysis confirmed WST-8 cytotoxicity test results and non-cytotoxicity of the modified surfaces. The GO+Ser coated Ti6Al7Nb possesses better biomedical potential than GO coated Ti6Al7Nb