Kinetic analysis of biohydrogen production from complex dairy wastewater under optimized condition

Present work describes a kinetic analysis of various aspects of biohydrogen production in batch test using optimized conditions obtained previously. Monod model and Logistic equation have been used to find growth kinetic parameters in batch test under uncontrolled pH. The values of μ_m, K_s, and X_m were 0.64 h⁻¹, 15.89 g-COD L⁻¹, and 7.26 g-VSS L⁻¹, respectively. Modified Leudeking-Piret and Michaelis–Menten equation corroborates a flux of energy to hydrogen production pathway and energy sufficiency in the system. Modified Gompertz equation illustrates that the overall rate and hydrogen yield at 15 g-COD L⁻¹ was higher compared to a dark fermentation of other wastewaters. Besides, Andrew's equation also suggests that since the higher value of K_I (19.95 g-COD L⁻¹), k (255 mL h⁻¹ L⁻¹) was not inhibited at high S. The experimental results implied that the entire products during the fermentation process were growth and substrate degradation associated. The result also confirms that the acetate and butyrate were substantially used for hydrogen production in acidogenic metabolism under uncontrolled pH.     

Benchmarking nanotechnology for high-performance and low-power logic transistor applications

Recently there has been tremendous progress made in the research of novel nanotechnology for future nanoelectronic applications. In particular, several emerging nanoelectronic devices such as carbon-nanotube field-effect transistors (FETs), Si nanowire FETs, and planar III-V compound semiconductor (e.g., InSb, InAs) FETs, all hold promise as potential device candidates to be integrated onto the silicon platform for enhancing circuit functionality and also for extending Moore's Law. For high-performance and low-power logic transistor applications, it is important that these research devices are frequently benchmarked against the existing Si logic transistor data in order to gauge the progress of research. In this paper, we use four key device metrics to compare these emerging nanoelectronic devices to the state-of-the-art planar and nonplanar Si logic transistors. These four metrics include: 1) CV/I or intrinsic gate delay versus physical gate length L/sub g/; 2) energy-delay product versus L/sub g/; 3) subthreshold slope versus L/sub g/; and 4) CV/I versus on-to-off-state current ratio I/sub ON//I/sub OFF/. The results of this benchmarking exercise indicate that while these novel nanoelectronic devices show promise and opportunities for future logic applications, there still remain shortcomings in the device characteristics and electrostatics that need to be overcome. We believe that benchmarking is a key element in accelerating the progress of nanotechnology research for logic transistor applications.     

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

In control systems theory, the Markov decision process (MDP) is a widely used optimization model involving selection of the optimal action in each state visited by a discrete-event system driven by Markov chains. The classical MDP model is suitable for an agent/decision-maker interested in maximizing expected revenues, but does not account for minimizing variability in the revenues. An MDP model in which the agent can maximize the revenues while simultaneously controlling the variance in the revenues is proposed. This work is rooted in machine learning/neural network concepts, where updating is based on system feedback and step sizes. First, a Bellman equation for the problem is proposed. Thereafter, convergent dynamic programming and reinforcement learning techniques for solving the MDP are provided along with encouraging numerical results on a small MDP and a preventive maintenance problem.     

A fluorogenic peptide containing the processing site of human SARS corona virus S-protein: kinetic evaluation and NMR structure elucidation

Human severe acute respiratory syndrome coronavirus (hSARS-CoV) is the causative agent for SARS infection. Its surface glycoprotein (spike protein) is considered to be one of the prime targets for SARS therapeutics and intervention because its proteolytic maturation by a host protease is crucial for host-virus fusion. Using intramolecularly quenched fluorogenic (IQF) peptides based on hSARS-CoV spike protein (Abz-(755)Glu-Gln-Asp-Arg-Asn-Thr-Arg-Glu-Val-Phe-Ala-Gln(766)-Tyx-NH(2)) and in vitro studies, we show that besides furin, other PCs, like PC5 and PC7, might also be involved in this cleavage event. Through kinetic measurements with recombinant PCs, we observed that the peptide was cleaved efficiently by both furin and PC5, but very poorly by PC7. The cleavage could be blocked by a PC-inhibitor, alpha1-PDX, in a dose-dependent manner. Circular dichroism spectra indicated that this peptide possesses a high degree of sheet structure. Following cleavage by furin, the sheet content increased, possibly at the expense of turn and random structures. (1)H NMR spectra from 2D COSY and ROESY experiments under physiological buffer and pH conditions indicated that this peptide possesses a structure with a turn at its C-terminal segment, close to the cleavage site. The data suggest that the cleavable peptide bond is located within the most exposed domain; this is supported by the nearby turn structure. Several strong to weak NMR ROESY correlations were detected, and a 3D structure of the spike IQF peptide that contains the crucial cleavage site R(761) E has been proposed.     

Comparative analysis of in vitro ultraviolet radiation protection of fabrics woven from cotton and bamboo viscose yarns

Ultraviolet protection factor (UPF) of fabrics made of 100% cotton and 100% bamboo viscose yarns were studied and a comparative analysis carried out using curve fitting technique. Bamboo viscose fabrics showed higher shrinkage, cover percentage, areal density and UPF compared to its cotton counterpart woven with identical yarn counts and fabric sett. However, the predictive model of cotton fabric UPF using fabric areal density as the input was able to estimate the UPF of bamboo viscose fabrics with very good accuracy. Furthermore, the 100% cotton and 100% bamboo viscose fabrics showed the same UPF if their cover percentage and areal density is similar. It is inferred from the analysis that the apparently higher UPF of bamboo viscose fabrics can be attributed to their higher cover percentage and areal density instead of bamboo’s inherent UV protective property which has been claimed in various literatures.     

Laser composite surfacing of AISI 304 stainless steel with titanium boride for improved wear resistance

The present study concerns development of a hard in situ boride-dispersed composite layer on the surface of AISI 304 stainless steel substrate to improve the wear resistance property. Laser processing was carried out by melting the surface of sand-blasted AISI 304 stainless steel substrate using a continuous wave CO₂ laser and simultaneous deposition of a mixture of K₂TiF₆ (potassium titanium hexafluoride) and KBF₆ (potassium hexafloroborate) (in the weight ratio of 2:1) using Ar as shrouding environment. Powder feed rate was maintained constant at 4 g/min. Irradiation results in dissociation of a pre-deposited mixture along with a part of the stainless steel substrate, intermixing and rapid solidification to form the composite layer on the surface. The micro-structure of composite layer consists of dispersion of titanium boride particles in AISI 304 stainless steel matrix. Volume fraction of particles is found to be uniform throughout the composite layer, though varied with laser parameters. The micro-hardness of the surface was improved 250–350 VHN as compared to 220 VHN of the AISI 304 stainless steel substrate with a significant improvement in wear resistance property. The mechanism of wear was found to be a combination of adhesive and abrasive in as-received stainless steel. However, it was predominantly abrasive for laser composite surfaced stainless steel.     

Optical sectioning microscope with a binary hologram based beam scanning

We describe the development of a beam scanning microscope that can perform optical sectioning based on the principle of confocal microscopy. The scanning is performed by a laser beam diffracted from a dynamic binary hologram implemented using a liquid crystal spatial light modulator. Using the proposed scanning mechanism, unlike the conventional confocal microscopes, scanning over a two-dimensional area of the sample can be obtained without the use of a pair of galvo mirror scanners. The proposed microscope has a number of advantages, such as superior frame to frame repeatability, simpler optical arrangement, increased pixel dwell time relative to the time between two pixels, illumination of only the sample points without pulsing the laser, and absolute control over the amplitude and phase of the illumination beam on a pixel to pixel basis. The proposed microscope can be particularly useful for applications requiring very long exposure time or very large working distance objective lenses. In this paper we present experimental implementation of the setup using a nematic liquid crystal spatial light modulator and proof-of-concept experimental results.     

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

Fabrication of ZnO-functionalized polypyrrole microcomposite as a protective coating to enhance anticorrosion performance of low carbon mild steel

In the present work, PPy, ZnO, and polypyrrole/zinc oxide (PPy/ZnO) microcomposites (1, 2, and 5 wt%) were prepared and their properties have been tuned for anticorrosion applications on low carbon mild steel. The synthesized products: ZnO, PPy, and composites were characterized by various sophisticated analytical techniques such as XRD, FTIR, Raman, FESEM, EDX, UV–VIS, TGA, and BET. The band frequencies observed at 480 and 588 cm⁻¹ in FTIR spectrum correspond to stretching vibrations of Zn-O and N-H bonds, respectively, broadening of the bands in the composites indicate strong interactions between ZnO and PPy matrix. The potentiodynamic polarization study of PPy and PPy/ZnO composite was carried out in 3.5% NaCl solution to investigate the corrosion resistance efficiency. PPy/1 wt% ZnO (I_corr = 190 nA) composite coating on low carbon mild steel was observed to exhibit best corrosion protection property compared to PPy (121 μA), 2 and 5 wt% ZnO (242, 295 nA) composites. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48319.     

BIST/Digital-Compatible Testing of RF Devices Using Distortion Model Fitting

Testing of Radio Frequency (RF) circuits for nonlinearity specifications generally requires the use of multiple test measurements thereby contributing to increased test cost. Prior RF test methods have suffered from significant test calibration effort (training for supervised learners) when using compact tests or from increased test time due to direct specification measurement. On the other hand, due to aggressive technology scaling, there are plenty of digital transistors available that can be used to simplify testing of Analog/Mixed-Signal (AMS) and RF devices. In this paper, an RF test methodology is developed that: (a) allows RF devices to be tested for several distortion specifications using distortion model fitting algorithms in test time comparable to what can be achieved using supervised learning techniques while retaining the accuracy of direct specification measurement, (b) allows multiple RF specifications to be determined concurrently from a single data acquisition and (c) allows digital-compatible testing/BIST to be performed using digital testers or on-chip built in self-test (BIST) circuitry. With regard to (a), a key benefit is that no training of supervised learning algorithm is necessary. The proposed method based on distortion model fitting is shown to give excellent results across common RF performance metrics while providing ~10× improvements in test time compared to previous methods.