Noble Metal Nanocrystal‐Incorporated Fullerene‐Like Polyoxometalate Based Microspheres

Monodisperse fullerene‐like polyoxometalate‐based microspheres are synthesized using a single‐phase approach which is quick, easier to control surfactant to polyoxometalate (POM) molar ratios and result in better control over the size‐distribution range. Noble‐metal nanocrystals are successfully incorporated in the surfactant encapsulated complex multilayered spherical assemblies without any change in their layered morphology. Combining the merits of fullerene‐like POMs and monodisperse noble metal nanocrystals, these monodisperse hybrid spheres show interesting composition‐ and size‐dependent photochemical properties.     

Using Digital Socialization to Support Geographically Dispersed AEC Project Teams

In the field of knowledge management research, socialization means to convert individual into group tacit knowledge. This process matters from the outset of an architecture, engineering, and construction (AEC) project to enhance collaborative work. Face-to-face meetings and phone calls undoubtedly facilitate socialization. However, meetings can be hard to timetable and expensive when AEC teams are geographically dispersed, whereas phone calls are cheap but offer limited capabilities for problem solving. Further, both media are not good at supporting asynchronous socialization. This study investigates the extent Internet-based media can promote cross-firm socialization and enhance collaborative work. The cross fertilization of findings from an exploratory case study with theory in computer-supported collaborative work (CSCW) informs the development of a conceptual framework on digital socialization. This framework underpins IDRAK—a proof-of-concept of a rich Internet application prototype to promote socialization in AEC projects. Our main contribution is the design of a novel methodology to evaluate the usability of digital systems to support socialization at the early design stage of an AEC project. The results from our lab experiments suggest that IDRAK can satisfactorily and efficiently enhance collaborative work. However, more research is needed, first, to evaluate the effectiveness of IDRAK to improve design quality and asynchronous socialization; and second, to investigate how other CSCW features can improve the performance of IDRAK-like systems.     

Perovskite SrFe1-xTixO3-δ (x < = 0.1) cathode for low temperature solid oxide fuel cell

Stable and compatible cathode materials are a key factor for realizing the low-temperature (LT, ≤600?°C) operation and practical implementations of solid oxide fuel cells (SOFCs). In this study, perovskite oxides SrFe_1-xTi_xO_3-δ (x?< = 0.1), with various ratios of Ti doping, are prepared by a sol-gel method for cathode material for LT-SOFCs. The structure, morphology and thermo-gravimetric characteristics of the resultant SFT powders are investigated. It is found that the Ti is successfully doped into SrFeO_3-δ to form a single phase cubic perovskite structure and crystal structure of SFT shows better stability than SrFeO_3-δ. The dc electrical conductivity and electrochemical properties of SFT are measured and analysed by four-probe and electrochemical impedance spectra (EIS) measurements, respectively. The obtained SFT exhibits a very low polarization resistance (R_p), .01 Ωcm² at 600?C. The SFT powders using as cathode in fuel cell devices, exhibit maximum power density of 551?mW?cm^?2 with open circuit voltage (OCV) of 1.15?V at 600?C. The good performance of the SFT cathode indicates a high rate of oxygen diffusion through the material at cathode. By enabling operation at low temperatures, SFT cathodes may result in a practical implementation of SOFCs.     

One‐Pot Lipase Entrapment Within Silica Particles to Prepare a Stable and Reusable Biocatalyst for Transesterification

In order to enhance the reusability, Rhizomucor miehei lipase was entrapped in a single step within silica particles having an oleic acid core (RML@SiO₂). Characterization of RML@SiO₂ by scanning and transmission electron microscopy and Fourier transform infrared studies supported the lipase immobilization within silica particles. The immobilized enzyme was employed for transesterification of cottonseed oil with methanol and ethanol. Under the optimum reaction conditions of a methanol‐to‐oil molar ratio of 12:1 or ethanol‐to‐oil molar ratio of 15:1, stirring speed of 250 revolutions/min (flask radius = 3 cm), reaction temperature of 40 °C, and biocatalyst concentration of 5 wt% (with respect to oil), more than 98 % alkyl ester yield was achieved in 16 and 24 h of reaction duration in case of methanolysis and ethanolysis, respectively. The immobilized enzyme did not require any buffer solution or organic solvent for optimum activity; hence, the produced biodiesel and glycerol were free from metal ion or organic molecule contamination. The activation energies for the immobilized enzyme‐catalyzed ethanolysis and methanolysis were found to be 34.9 ± 1.6 and 19.7 ± 1.8 kJ mol^?1, respectively. The immobilized enzyme was recovered from the reaction mixture and reused in 12 successive runs without significant loss of activity. Additionally, RML@SiO₂ demonstrated better reusability as well as stability in comparison to the native enzyme as the former did not lose the activity even upon storage at room temperature (25–30 °C) over an 8‐month period.     

Data Compression for Proof Replay

We describe a compressing translation from SAT solver generated propositional resolution refutation proofs to classical natural deduction proofs. The resulting proof can usually be checked quicker than one that simply simulates the original resolution proof. We use this result in interactive theorem provers, to speed up reconstruction of SAT solver generated proofs. The translation is fast and scales up to large proofs with millions of inferences.     

Soil–conduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits

Neural Computing and Applications - Marston’s load theory is commonly used for understanding the soil–conduit interaction. However, there are no practical methods available which can...     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.