The 2-oxoglutarate-dependent oxygenase JMJD6 catalyses oxidation of lysine residues to give 5S-hydroxylysine residues

Amino acid analyses reveal that JMJD6-catalysed hydroxylation of RNA-splicing regulatory protein fragments occurs to give hydroxylysine products with 5S stereochemistry. This contrasts with collagen lysyl hydroxylases, which give 5R-hydroxylated products. The work suggests that more than one subfamily of lysyl hydroxylases has evolved and illustrates the importance of stereochemical assignments in proteomic analyses.     

Maximal limited similarity-based rough set model

Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS-RSM. The results demonstrate that MLS-RSM model outperforms NS-RSM and can approximate the target set more efficiently.     

Optimal robot‐environment interaction using inverse differential Riccati equation

An optimal robot‐environment interaction is designed by transforming an environment model into an optimal control problem. In the optimal control, the inverse differential Riccati equation is introduced as a fixed‐end‐point closed‐loop optimal control over a specific time interval. Then, the environment model, including interaction force, is formulated in a state equation, and the optimal trajectory is determined by minimizing a cost function. Position control is proposed, and the stability of the closed‐loop system is investigated using the Lyapunov direct method. Finally, theoretical developments are verified through numerical simulation.     

Modeling and simulation of solar-powered liquid desiccant regenerator for open absorption cooling cycle

This paper presents the modeling and simulation of solar-powered desiccant regenerator used for open absorption cooling cycles. The input heat, which is the total radiation incident on an inclined surface, is evaluated via a solar radiation model in terms of the location, day of the year, and time of the day. Calcium Chloride (CaCl₂) is applied as the working desiccant in this investigation. The solar radiation model is integrated with the desiccant regenerator model to produce a more realistic simulation. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rate, on the performance of the system is investigated. It has been found that the vapor pressure difference has a maximum value for a given regenerator length. It is also shown that for specified operating conditions, a maximum value of the coefficient of performance occurs at a given range of air and solution flow rates. Therefore, it is essential to select the design parameters for each ambient condition to maximize the coefficient of performance of the system.     

Process Optimization,Physical Properties,and Environmental Stability of an α-Tocopherol Nanocapsule Preparation Using Complex Coacervation Method and Full Factorial Design

α-Tocopherol (α-Toc) has valuable biological activity, but its activity is limited when exposed to environmental factors. Nanocapsules can be used to overcome this problem. Using nanocapsules in the range of 100–200 nm is more beneficial. A 2⁴ full factorial design was carried out to optimize the size of nanocapsules using the complex coacervation method. The four factors were the amount of the wall material, the ratio of core material to wall material, the pH of the solution, and the speed of the homogenizer. The smallest nanocapsules (176 nm) were obtained at a wall content (gelatine and pectin) of 0.8 mg, a percentage of core material (α-Toc) to wall material of 20%, a pH = 4.5, and a homogenizer speed of 12,000 rpm. The encapsulation efficiency was 90.6 ± 1.1%, and the encapsulation yield was 83.4 ± 1.6%. Assessment of the stability of α-Toc after 1 month showed that encapsulation could improve its stability in the presence of three influential factors: humidity, light, and temperature.     

Effects of Acidity and Method of Preparation on Nucleate Pool Boiling of Nanofluids

Past research has shown contradicting trends in the rate of heat transfer during pool boiling of nanofluids, which could be attributed either to their stability or to their method of preparation or to both. An experimental study has been conducted to investigate the effects of electrostatic stabilization and preparation method of nanofluids on their pool boiling rate of heat transfer. Nanofluids made from water and alumina nanoparticles at 0.1 vol% concentration were used. The effect of electrostatic stabilization was investigated by changing the pH value from 6.5, neutral, to 5, acidic. The effect of preparation method has been investigated by using nanofluids prepared from dry particles and from ready-made suspensions. Compared with water, all nanofluids investigated resulted in deterioration in the rate of heat transfer during pool boiling. Neutral nanofluids made from ready-made suspensions and from dry particles resulted into almost the same deterioration in the rate of heat transfer of 49% and 45%, respectively, with respect to that of pure water. The most significant effect of electrostatic stabilization was found in the case of acidic nanofluids made from dry particles, which resulted in deterioration in the rate of heat transfer of 31%. However, acidic nanofluids made from ready-made suspensions resulted in a deterioration of 46%, which is almost the same as that of suspension-made and dry particles-made nanofluids. These results indicate that electrostatic stabilization using acid addition is most effective with nanofluids made from dry particles.     

A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

Designing genetic regulatory networks using fuzzy Petri nets approach

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.     

A Sliding Mode Controller Based on Robust Model Reference Adaptive Proportional-integral Control for Stand-alone Three-phase Inverter

This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral (RMRA-PI) control for a stand-alone voltage source inverter (SA-VSI). The proposed controller has two control loops where the coefficients of PI controller are regulated by the adaptive sliding law. This method is used to regulate the output voltage of the inverter under different load conditions and uncertainty, and adapts the output to the reference model to reduce the total harmonic distortion (THD). In this paper, the stability of the proposed controller is proven by using Lyapunov’s theory and Barbalet’s lemma. The proposed controller performs well in voltage regulation such as low THD under sudden load change and uncertainty. Also, the results of the proposed controller are compared with PI controller to show the effectiveness of the presented control system.     

Multiscale modeling of elastic properties of cortical bone

We model cortical bone as a composite material with hierarchical structure. At a nanostructural level, bone is composed of cross-linked collagen molecules, containing water and non-collagenous proteins in their gaps, reinforced with hydroxyapatite-like nanocrystals. Such a nanocomposite structure represents a mineralized collagen fibril, which serves as a primary building block of bone. At a sub-microstructural level (few microns), the mineralized collagen fibrils are embedded in an extrafibrillar hydroxyapatite matrix to form a single lamella, which also contains the lacunar cavities. At a microstructural level (hundreds of microns) one can distinguish two lamellar structures in the mature cortical bone: osteons, made of concentric layers of lamellae surrounding long hollow Haversian canals, and interstitial lamellae made of remnants of old osteons. At a mesostructural level (several millimeters), the cortical bone is represented by a random collection of osteons and resorption cavities in the interstitial lamellae. A macrostructural level is the whole bone level containing both the cortical (compact) and trabecular (spongy) bone types. In this paper, we predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, spanning from the nanostructural to mesostructural levels, using micromechanics methods and composite materials laminate theories. The results obtained at a lower scale serve as inputs for the modeling at a higher scale. The predictions are in good agreement with the experimental data reported in literature.