Microwave Heated Chemical Vapor Infiltration: Densification Mechanism of SiCf/SiC Composites

Silicon carbide fiber-reinforced silicon carbide matrix composites (SiC_f/SiC) have been produced using microwave heated chemical vapor infiltration. Preferential densification of the composite from the inside out was clearly observed. Although an average relative density of only 55% was achieved in 24 h, representative of an ∼26% increase over the initial fiber vol%, the center of the preform densified to 73% of the theoretical. The densification mechanisms were investigated using X-ray absorptiometry and scanning electron microscopy. The initial inverse temperature profile obtained, which was found to result in the efficient filling of the intratow porosity, although not the intertow porosity, flattened out after approximately 6 h as the densification front moved outward toward the edges. Although not investigated directly, the evidence suggested that this was caused by changes in both the thermal conductivity and microwave absorption characteristics as the samples densified.     

Studies on an araldite-based membrane of copper hexacyanoferrate (III) as a caesium ion-sensitive electrode

Solid membranes of copper hexacyanoferrate (III) in araldite are evaluated as a caesium ion-sensitive electrode. The electrode can be used for caesium determination in the concentration range of 10⁻¹ to 10-⁴M . The potentials generated across the membrane are reproducible and steady potentials are attained in about 1 to 2min. The same electrode can be used over a period of 6 months without significant change in potential. The electrode can be used in the pH ranges 2.5–6.0 at 10⁻² M Cs⁺ and 3.0–6.0 at 10⁻³ M CS⁺, and in presence of a number of interfering ions. Potentiometric titration of caesium nitrate with 12-moIybdophosphoric acid was also carried out using the membrane as an end point indicator.     

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.     

Spectral selectivity and accelerated life testing of Al-PbS solar selective surfaces

High absorptance and low thermal emittance selective surfaces have been formed by evaporating lead sulphide onto Al evaporated Al substrates. The dependence of selectivity, α/?, on thickness has been studied. The maximum selectivity was obtained for PbS thickness of 400 Å. The emissivity is found to increase with temperature. The coatings are stable up to a temperature of 240°C in air. Long term ultraviolet irradiation causes the photo-oxidation of PbS to PbSO₄ which results in the increase of emittance and a decrease in absorptance. When cycled between 0°C and 200°C, the coatings do not show any signs of degradation.     

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.     

Effect of Electromigration on the Mechanical Performance of Sn-3.5Ag Solder Joints with Ni and Ni-P Metallizations

The effect of moderate electric current density (1 × 10³ to 3 × 10³ A/cm²) on the mechanical properties of Ni-P/Sn-3.5Ag/Ni-P and Ni/Sn-3.5Ag/Ni solder joints was investigated using a microtensile test. Thermal aging was carried out at 160°C for 100 h while the current was passed. The interfacial microstructure and intermetallic compound (IMC) growth were analyzed. It was found that, at these levels of current density, there were no observable voids or hillocks. Samples aged at 160°C without current stressing failed mostly inside the bulk solder with significant prior plastic deformation. The passage of current was found to cause brittle failure of the solder joints and this tendency for brittle failure increased with increasing current density. Fractographic analysis showed that, in most of the electrically stressed samples, fracture occurred at the interface region between the solder and the joining metals. The critical current density that caused brittle fracture was about 2 × 10³ A/cm². Once brittle fracture occurred, the tensile toughness, defined as the energy per unit fractured area, was usually lower than ~5 kJ/m², compared with the case of ductile fracture where this value was typically greater than ~9 kJ/m². When comparing the two types of joint, the brittle failure was found to be more severe with the Ni than with the Ni-P joint. This work also found that the passage of electric current affects the IMC growth rate more significantly in the Ni than in the Ni-P joint. In the case of the Ni joint, the Ni₃Sn₄ IMC at the anode side was appreciably thicker than that formed at the cathode side. However, in the case of electroless Ni-P metallization, this difference was much smaller.     

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.