The amylose and lipid contents,dimensions, and gelatinisation characteristics of some wheat starches and their A- and B-granule fractions

Starches isolated from 23 bread wheats (Triticum aestivum) and 26 durum wheats (T. durum) contained 26.3-30.6% (mean 29.1%) total amylose, 19.3–25.1% (mean 22.9%) apparent amylose and 783–1144mg 100g^?1 (mean 977 mg 100g^?1) lysophos-pholipids. Gelatinisation temperatures were 57.3–64.9°C (mean 61.8°C) and enthalpies 6.4–11.8 Jg^?1 (mean 9.7Jg^?1) in excess water, measured by differential scanning calorimetry. There were no correlations between any of these parameters. Starch granule size distributions were determined with a Coulter Counter and 100–channel analyser. A-granule mean volumes were 1235–2585μm³ (av. 1778), modal volumes 863–1804μm³ (av. 1264), mean diameters 13.9–16.0μm (av. 13.99), and specific surface areas 0.236–0.302m²g^?1. B-granule mean volumes were 35.4–100.4μm³ (av. 55.9), modal volumes 16.5–54.5μm³ (av. 27.7), mean diameters 3.66–5.07μm (av. 4.09), and specific surface areas 0.684–0.920m²g^?1. The B-granule contents of the starches were 12.8–34.6% (av. 27.3) by weight (sedimentation method) and 13.0–37.3% (av. 24.0) by volume (Coulter method), the latter being the more accurate method.     

Kinetics of oxygen absorption in aqueous sodium dithionite solutions

The absorption and reaction of oxygen in aqueous alkaline solutions of sodium dithionite has been experimentally investigated in a novel gas-liquid contactor. The novel gas-lift bubble column contactor was used to study the kinetics over wide ranges of reactant concentrations, temperature, and pH. The oxygen-sodium dithionite reaction was found to be first-order with respect to dithionite in the range of dithionite concentration < 0.1 M, and second-order in the range of dithionite concentration > 0.1 M. The reaction with respect to oxygen was found to be zero-order for all dithionite concentrations. These results and experimental investigations of the effect of solution alkalinity and temperature on the reaction rate are consistent with previous findings obtained in different gas-liquid contactors. The results thus confirm the feasibility of using the gas-lift bubble column for the kinetics of gas-liquid reactions.     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

     

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...     

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.     

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.