Effect of different levels of fat and inulin on the microbial growth and metabolites in probiotic yogurt containing nonviable bacteria

Effects of different levels of fat and inulin on bacterial cell counts, degree of proteolysis and concentrations of organic acids in the yogurt containing inactivated cells of probiotic strains Bifidobacterium animalis and Lactobacillus acidophilus were investigated. Results showed that both L. acidophilus and B. animalis grew well in the yogurt samples reaching cell counts higher than 10⁶ CFU mL^?1 at the final pH of 4.5. Inulin at the concentration of 1% had no significant effects on the production of organic acids and cell counts of L. acidophilus, but promoted the growth of B. animalis with a reduction in the degree of proteolysis. Generally, different fat levels showed significant effects on the production of organic acids and nonsignificant effects on the cell counts of probiotic bacteria and degree of proteolysis. In case of lactic acid, the ratio of L‐ (+)to D‐ (?) isomer ranged from 50/50 to 80/20 in yogurt samples.     

Co-doping effects of (Al,Ti, Mg) on the microstructure and electrical behavior of ZnO-based ceramics

Co-doped ZnO-based ceramics using Al, Ti, and Mg ions in different ratios were synthesized with the objective to investigate the doping effects on the crystalline features, microstructure and the electrical behavior. For Al and Ti doping, a coexistence of crystalline phases was shown with a major wurtzite ZnO structure and secondary spinel phases (ZnAl₂O₄, Zn₂TiO₄, or Zn_aTi_bAl_cO_d), while Mg doping did not alter significantly the structural features of the wurtzite ZnO phase. The electrical behavior induced by Al, Ti, and Mg co-doping in different ratios was investigated using Raman, electron paramagnetic resonance (EPR) and ²⁷Al and ⁶⁷Zn solid-state nuclear magnetic resonance (NMR). Al doping induces a high electrical conductivity compared to other doping elements. In particular, shallow donors from Zn_i-Al_Zn defect structures are inferred from the characteristic NMR signal at about 185 ppm; that is, quite far from the usual oxygen coordinated Al. The Knight shift effect emanating from a highly conducting Al-doped ZnO ceramics was considered as the origin of this observation. Oppositely, as Ti doping leads to the formation of secondary spinel phases, EPR analysis shows a high concentration of Ti³⁺ ions which limit the electrical conductivity. The correlation between the structural features at the local order, the involved defects and the electrical behavior as function of the doping process are discussed.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

Multi carrier energy systems and energy hubs: Comprehensive review,survey and recommendations

In this paper, the multi carrier energy (MCE) systems are reviewed from different point of views including mathematical models, integrated components and technologies, uncertainty management, planning objectives, environmental pollution, resilience, and robustness. The basic of MCE systems is formed by combination of cooling, heating and power (CCHP). The natural gas and electricity are the main inputs to MCE systems and the cooling, heating, and electricity are the common outputs. The regular energy converters in the MCE systems are combined heat and power (CHP), gas boiler, absorption-electrical chillers, power to gas (P2G) and fuel-cell. The generic energy storages are electrical, heating, cooling, hydrogen, carbon dioxide (CO₂) and hydro systems.     

Mesoporous silica coated gold nanorods: a multifunctional theranostic platform for radiotherapy and X-ray imaging

Journal of Porous Materials - The development of theranostic nanostructures is one of the most advanced branches of pharmaceutical and medical sciences in the world today. Due to the unique...     

Controller Design Based On Wavelet Neural Adaptive Proportional Plus Conventional Integral-Derivative For Bilateral Teleoperation Systems With Time-Varying Parameters

International Journal of Control, Automation and Systems - In this study, a new controller method based on wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID)...     

Adaptive robust control-based energy management of hybrid PV-Battery systems with improved transient performance

Energy management of hybrid photovoltaic (PV)-battery systems still serve as a challenging task owing to their complex and nonlinear characteristics, multicomponent structures, and the extensive range of environmental factors disturbing their nominal performance. The hybrid energy system developed in this study encompasses PV arrays, a battery component, one boost converter, and one bidirectional boost converter. In this paper, we propose a novel adaptive robust control framework for the optimal energy management of the PV-battery systems under many operating conditions and subject to unmodelled dynamics. An improved exponential-like adaptive integral sliding mode (EISM) control coupled to neural network approximator is introduced using a multi-rate convergence tweaking mechanism for the sliding surface to improve the transient performance of the closed-loop system. Furthermore, the entire dynamics of the hybrid energy system is considered unknown, unlike the previous studies that only assumed the parametric uncertainties. The global asymptotic stability of the system is guaranteed, and the effectiveness of this novel framework is compared to benchmark studies.