PUL-Mediated Plant Cell Wall Polysaccharide Utilization in the Gut Bacteroidetes

Plant cell wall polysaccharides (PCWP) are abundantly present in the food of humans and feed of livestock. Mammalians by themselves cannot degrade PCWP but rather depend on microbes resident in the gut intestine for deconstruction. The dominant Bacteroidetes in the gut microbial community are such bacteria with PCWP-degrading ability. The polysaccharide utilization systems (PUL) responsible for PCWP degradation and utilization are a prominent feature of Bacteroidetes. In recent years, there have been tremendous efforts in elucidating how PULs assist Bacteroidetes to assimilate carbon and acquire energy from PCWP. Here, we will review the PUL-mediated plant cell wall polysaccharides utilization in the gut Bacteroidetes focusing on cellulose, xylan, mannan, and pectin utilization and discuss how the mechanisms can be exploited to modulate the gut microbiota.     

Box-Behnken Design a Key Tool to Achieve Optimized PCL/Gelatin Electrospun Mesh

Hybrid electrospun nanofibers of polycaprolactone (PCL)/gelatin are considered as drug-delivery systems for increasing the treatment efficacy in superficial (skin) wounds. Continuous delivery of therapeutic agents, skin extracellular matrix similarity, management of wound exudate, and antimicrobial barrier effect are the major advantages of electrospun nanofibers in skin applications. Additionally, combining the favorable properties of PCL and gelatin, regarding their biocompatibility, biodegradability and mechanical performance have been revealed promising parameters to be considered for blend in hybrid structures. However, the usual optimization protocol of nanofibers’ production in electrospinning is based on the observation of one-variable-at-time being this methodology expensive and time-consuming. Therefore, in this research work, a statistical model based on four input variables namely, the flow rate, the needle-working distance, the applied voltage, and the ratio of PCL in the solution, is developed to predict the behavior of nanofibers. The performance of nanofibers is monitored by measurements of fiber's diameter, mesh's thickness, and mesh's permeability. Overall, the model showed to be statistically significant (p-value < 0.05) and an independent analysis validated the predicted response for optimal condition. Finally, a delivery study is performed to evaluate the electrospun mesh performance as a drug carrier.     

Review on synthesis of porous TiO2-based catalysts for energy conversion systems

Porous TiO₂-based catalysts have recently received remarkable attention in the field of energy conversion systems, including hydrogen/oxygen evolution reaction, oxygen/nitrogen reduction reaction, and photodegradation of pollutants owing to their unique structure, large surface area, and good chemical stability. In this report, we review existing research on porous TiO₂-based catalysts for energy conversion systems during the past four years. First, the advantages of porous TiO₂-based catalysts are introduced. Next, the synthetic approaches in developing porous TiO₂-based catalysts are summarized. The different types of energy conversion systems based on porous TiO₂-based catalysts are then presented. Finally, the challenges and future perspectives in synthesizing porous TiO₂-based catalysts are discussed.     

Mechanisms of Reciprocal Regulation of Gonadotropin-Releasing Hormone (GnRH)-Producing and Immune Systems: The Role of GnRH,Cytokines and Their Receptors in Early Ontogenesis in Normal and Pathological Conditions

Different aspects of the reciprocal regulatory influence on the development of gonadotropin-releasing hormone (GnRH)-producing- and immune systems in the perinatal ontogenesis and their functioning in adults in normal and pathological conditions are discussed. The influence of GnRH on the development of the immune system, on the one hand, and the influence of proinflammatory cytokines on the development of the hypothalamic-pituitary-gonadal system, on the other hand, and their functioning in adult offspring are analyzed. We have focused on the effects of GnRH on the formation and functional activity of the thymus, as the central organ of the immune system, in the perinatal period. The main mechanisms of reciprocal regulation of these systems are discussed. The reproductive health of an individual is programmed by the establishment and development of physiological systems during critical periods. Regulatory epigenetic mechanisms of development are not strictly genetically controlled. These processes are characterized by a high sensitivity to various regulatory factors, which provides possible corrections for disorders.     

Use of a smartphone-based Student Response System in large active-learning Chemical Engineering Thermodynamics classrooms

Smartphones are a promising tool as student response systems (SRS) for interactive teaching due to their widespread diffusion. Here, the main purpose is to assess the efficacy of smartphone-based SRS in large classroom settings of undergraduate Thermodynamics, as representative of engineering courses requiring high-level cognitive skills for problem solving. Four sets of multiple-choice questions were presented during the course. Overall, the results refer to 1055 students between control and SRS classes, each corresponding to a3 years period.One of the main results of this work is the strong linear correlation between the average questionnaire score and the final exam grade (R² = 0.91). A similar correlation, although with a lower value of R², is already found in the first questionnaire, thus showing the SRS high predictive power of class performance. The results of this study provide guidance for a quantitative use of smartphone-based SRS in teaching basic disciplines. The SRS monitoring capability allows early detection of struggling students, thus paving the way to personalized tutoring and improved student engagement in active learning practices. This approach is especially important in emergency situations, such as the SARS-Cov-2 pandemic, when distance learning is widely adopted, and remote interactive tools are highly needed.     

Vitamin A fortification: Recent advances in encapsulation technologies

Vitamin A is an essential micronutrient whose deficiency is still a major health concern in many regions of the world. It plays an essential role in human growth and development, immunity, and vision, but may also help prevent several other chronic diseases. The total amount of vitamin A in the human diet often falls below the recommended dietary allowance of approximately 900–1000 $\mu$g/day for a healthy adult. Moreover, a significant proportion of vitamin A may be degraded during food processing, storage, and distribution, thereby reducing its bioactivity. Finally, the vitamin A in some foods has a relatively low bioavailability, which further reduces its efficacy. The World Health Organization has recommended fortification of foods and beverages as a safe and cost-effective means of addressing vitamin A deficiency. However, there are several factors that must be overcome before effective fortified foods can be developed, including the low solubility, chemical stability, and bioavailability of this oil-soluble vitamin. Consequently, strategies are required to evenly disperse the vitamin throughout food matrices, to inhibit its chemical degradation, to avoid any adverse interactions with any other food components, to ensure the food is palatable, and to increase its bioavailability. In this review article, we discuss the chemical, physical, and nutritional attributes of vitamin A, its main dietary sources, the factors contributing to its current deficiency, and various strategies to address these deficiencies, including diet diversification, biofortification, and food fortification.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.