TY  - CHAP
A1  - Mansurov, Zulkhair A.
A1  - Jandosov, Jakpar
A1  - Chenchik, D.
A1  - Azat, Seitkhan
A1  - Savitskaya, Irina S.
A1  - Kistaubaeva, Aida
A1  - Akimbekov, Nuraly
A1  - Digel, Ilya
A1  - Zhubanova, Azhar Achmet
T1  - Biocomposite Materials Based on Carbonized Rice Husk in Biomedicine and Environmental Applications
T2  - Carbon Nanomaterials in Biomedicine and the Environment
N2  - This chapter describes the prospects for biomedical and environmental engineering applications of heterogeneous materials based on nanostructured carbonized rice husk. Efforts in engineering enzymology are focused on the following directions: development and optimization of immobilization methods leading to novel biotechnological and biomedical applications; construction of biocomposite materials based on individual enzymes, multi-enzyme complexes and whole cells, targeted on realization of specific industrial processes. Molecular biological and biochemical studies on cell adhesion focus predominantly on identification, isolation and structural analysis of attachment-responsible biological molecules and their genetic determinants. The chapter provides a short overview of applications of the biocomposite materials based of nanostructured carbonized adsorbents. It emphasizes that further studies and better understanding of the interactions between CNS and microbial cells are necessary. The future use of living cells as biocatalysts, especially in the environmental field, needs more systematic investigations of the microbial adsorption phenomenon.
Y1  - 2020
SN  - 978-981-4800-27-3
U6  - http://dx.doi.org/10.1201/9780429428647-2
SP  - 3
EP  - 32
PB  - Jenny Stanford Publishing Pte. Ltd.
CY  - Singapore
ER  - 
TY  - CHAP
A1  - Akimbekov, Nuraly
A1  - Zhanadilovna, Abdieva G.
A1  - Ualieva, Perizat S.
A1  - Abaihanovna, Zhusipova D.
A1  - Digel, Ilya
A1  - Savitskaya, Irina S.
A1  - Zhubanova, Azhar Achmet
T1  - Functionalization of Carbon Based Wound Dressings with Antimicrobial Phytoextracts for Bioactive Treatment of Septic Wounds
T2  - Carbon Nanomaterials in Biomedicine and the Environment
N2  - The treatment of septic wounds with curative dressings based on biocomposites containing sage and marigold phytoextracts was effective in in vitro and in vivo experiments. These dressings caused the purification of the wound surface from purulent-necrotic masses three days earlier than in the other experimental groups. The consequence of an increase in incidents of severe course of the wound and the observed tendency to increase the number of adverse effects is the development of long-term recurrent wound processes. To treat purulent wounds, the following tactics were used: The purulent wounds of animals were covered with the examined wound dressing, and then the next day samples were taken, the procedure was performed once in 2 days. To obtain the active nanostructured sorbents such as carbonized rice husks, they are functionalized with biologically active components possessing antimicrobial, anti-inflammatory, antitoxic, immunomodulating, antiallergic and other types of properties.
Y1  - 2020
SN  - 978-981-4800-27-3
U6  - http://dx.doi.org/10.1201/9780429428647-11
SP  - 211
EP  - 228
PB  - Jenny Stanford Publishing
CY  - Singapore
ER  - 
TY  - JOUR
A1  - Ketelhut, Maike
A1  - Brügge, G. M.
A1  - Göll, Fabian
A1  - Braunstein, Bjoern
A1  - Albracht, Kirsten
A1  - Abel, Dirk
T1  - Adaptive iterative learning control of an industrial robot during neuromuscular training
JF  - IFAC PapersOnLine
N2  - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.
KW  - Iterative learning control
KW  - Robotic rehabilitation
KW  - Adaptive control
Y1  - 2020
U6  - http://dx.doi.org/10.1016/j.ifacol.2020.12.741
SN  - 2405-8963
VL  - 53
IS  - 2
SP  - 16468
EP  - 16475
PB  - Elsevier
CY  - Amsterdam
ER  -