Use of a computer for the optimal results of treatment of diabetes mellitus with insulin

The authors present material on the optimization of diabetes mellitus treatment, with the aid of a computer. There were 320 patients under observation. Mathematical models describing the process of the patients treatment and permitting to pronosticate the blood and urine sugar level during the treatment were developed. The use of the imitation experiment on a computer permitted to test several therapeutic variants for the given patient and to choose the optimum one, leading to the most rapid compensation. The second task consisted in the maintenance in the patients of blood glucose variations within definite limits, in the course of 24 hours. An individual model of blood glucose dynamics in the course of 24 hours is set on the computer, and then the optimal scheme of insulin treatment is chosen. Practical testing of such scheme showed its efficacy.     

Diffusivity,shrinkage and simulated drying of litchi fruit (Litchi Chinensis Sonn.)

Litchi (Litchi Chinensis Sonn.) is an important commercial fruit in Thailand and Vietnam. Litchi fruit is consumed both as fresh and dried products. Also most of the export of litchi is in the form of dried whole litchi fruit. Thermo-physical properties and drying model of litchi fruit is important for optimum design of litchi dryer. This paper presents moisture diffusivity, shrinkage and finite element simulated drying of litchi fruit. The moisture diffusivities of litchi were determined by minimizing the sum of square of deviations between the predicted and experimental values of moisture content of thin layer drying under controlled conditions of air temperature and relative humidity. The components in the form of cylinder for seed and seed stalk and slab for seed coat, shell and flesh were dried in thin layers at the air temperatures of 50, 60, 70 and 80 °C and relative humidity in the range of 10–25%. The mean diffusivity of flesh, seed and shell of litchi fruit increased with temperature and was expressed by the Arrhenius-type equation, but the diffusivities of seed coat and seed stalk were independent of temperature. The moisture diffusivities of seed coat and seed stalk were much lower than those of the other parts of the litchi. The shrinkage of litchi fruit has also been determined experimentally and it was expressed as a function of moisture reduction. A two-dimensional finite element model has been developed to simulate moisture diffusion in litchi fruit during drying. Shrinkage of the flesh and different component diffusivities of litchi during drying were also taken into account. The finite element model was programmed in Compaq Visual FORTRAN version 6.5. This finite element model satisfactorily predicts the moisture diffusion during drying. Moisture contents in the different components in the litchi fruit during drying were also simulated. This study provides an understanding of the transport processes in the different components of the litchi fruit.     

Heavy metal accumulation in vegetables irrigated with water from different sources

The present study was carried out to assess levels of different heavy metals like iron, manganese, copper and zinc, in vegetables irrigated with water from different sources. The results indicated a substantial build-up of heavy metals in vegetables irrigated with wastewater. The range of various metals in wastewater-irrigated plants was 116–378, 12–69, 5.2–16.8 and 22–46 mg/kg for iron (Fe), manganese (Mn), copper (Cu) and zinc (Zn), respectively. The highest mean levels of Fe and Mn were detected in mint and spinach, whereas the levels of Cu and Zn were highest in carrot. The present study highlights that both adults and children consuming vegetables grown in wastewater-irrigated soils ingest significant amount of these metals. However, the values of these metals were below the recommended maximum tolerable levels proposed by the [Joint FAO/WHO Expert Committee on Food Additives (1999). Summary and conclusions. In 53rd Meeting, Rome, June 1–10, 1999]. However, the regular monitoring of levels of these metals from effluents and sewage, in vegetables and in other food materials is essential to prevent excessive build-up of these metals in the food chain.     

Neuroscience and architecture: What does the brain tell to an emotional experience of architecture via a functional MR study?

Environment psychologically affects individuals. According to the base of cognitive psychology, there is a direct relationship between human behavior, environment, and emotional process. Assuming that pleasantness and unpleasantness are associated with peripheral nervous system activation, the current study aims to explore if the pleasant or unpleasant architectural places can stimulate the brain regions engaged in emotions or not. As the main contribution, we used functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) changes to effectively detect the brain's region that mainly responds to the emotional-perceptual processes. Based on the results of examining the emotional assessment model of “Pleasure-Arousal” applied to 140 students, 30 most-rated images representing 15 pleasant and 15 unpleasant places were shown to 32 participants in a 1.5-T MRI scanner. After applying standard preprocessing steps (re-alignment, slice-timing, co-registration, segmentation, normalization, and smoothing) to functional MR images, first-level analysis was applied to each subject. The results were evaluated using statistical corrections at different levels for female and male participants with the second-level analysis. In conclusion, it has been shown that there is a significant linkage between environmental experience and brain activation so that the architectural qualities can change blood flow in specific brain regions.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Effect of malting and roasting of chickpea on functional and nutritional qualities of its protein fractions

Chickpea is gaining importance as a sustainable plant-based protein source due to its high protein content and protein bioavailability. Malting and roasting, the traditional processing techniques, can be used to improve the protein quality of chickpea. The effect of two processing techniques on the water-soluble albumin fraction, salt-soluble globulin fraction, alcohol-soluble prolamin fraction, and alkali-soluble glutelin fraction was studied. Globulin content decreased from 45.85% to 37.08%, whereas the albumin fraction was found to increase from 4.16% to 7.08%, after 6 days of germination. It was observed that malting and roasting resulted in significant changes in the protein quality parameters and decrease in anti-nutritional factors. Tannin content reduced by 53.18% and 40.44%, and the trypsin inhibitor activity reduced by 14.91% and 34.59% after malting and roasting, respectively. The protein efficiency ratio, biological value, and nutritional index improved due to malting and roasting. The biological value of raw chickpea protein was found to be 74.97%, and it increased to 81.89% and 79.27% on malting and roasting, respectively, and protein digestibility values were also increased. Microstructural and diffraction patterns of chickpea flours were examined. Amino acid scores for infants and adults were calculated, and sulphur-containing amino acids were found to be the limiting amino acids in chickpea.     

Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO

In this work, load flow problems of both radial distribution networks (RDNs) and mesh distribution networks (MDNs) have been solved using hybrid fuzzy-PSO algorithm. A new voltage stability index (VSI) is also indicated. Based on the suggested load flow, distributed generation (DG) is ready to conduct through the requirement; and with the support of inserting the optimal-sized DG unit in an exact way, the distribution system’s stability is also studied. The exact position of each DG unit has been computed using “loss sensitivity analysis,” whereas the optimal sizing of each DG unit has been done with the help of hybrid artificial bee colony and Cuckoo search algorithm. The suggested method is tested in the regular 33-node and 69-node RDNs as well as in 85-node and 119-node MDNs. The transcendence of the proposed operation has been centered with the aid of comparison to the other existing methods. The suggested VSI is also correlated with other two existing VSIs before and after placement of DG unit(s).

     

Highly birefringent large negative dispersion-flattened photonic crystal fibre for broadband residual dispersion compensation

A triangular lattice photonic crystal fibre is presented in this paper for residual dispersion compensation. The fibre exhibits a flattened negative dispersion of ?992.01 ± 6.93 ps/(nm-km) over S+C+L wavelength bands and ?995.83 ± 0.42 ps/(nm-km) over C-band. The birefringence is about 4.4 × 10^?2 at the excitation wavelength of 1550 nm which is also very high. Full vector finite element method (FEM) with a perfectly matched absorbing layer (PML) boundary condition is applied to numerically investigate the guiding properties of this PCF. The fibre operates at fundamental mode only. All these properties endorse this fibre as a suitable candidate for compensating residual dispersion and polarization maintaining applications.