
These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.
Citation: Mazhar Ali Dootio, Abdullah Lakhan, Ali Hassan Sodhro, Tor Morten Groenli, Narmeen Zakaria Bawany, Samrat Kumar. Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 513-536. doi: 10.3934/mbe.2022024
[1] | Gayane Semerjyan, Inesa Semerjyan, Mikayel Ginovyan, Nikolay Avtandilyan . Characterization and antibacterial/cytotoxic activity of silver nanoparticles synthesized from Dicranum scoparium moss extracts growing in Armenia. AIMS Biophysics, 2025, 12(1): 29-42. doi: 10.3934/biophy.2025003 |
[2] | Shen Helvig, Intan D. M. Azmi, Seyed M. Moghimi, Anan Yaghmur . Recent Advances in Cryo-TEM Imaging of Soft Lipid Nanoparticles. AIMS Biophysics, 2015, 2(2): 116-130. doi: 10.3934/biophy.2015.2.116 |
[3] | Christophe A. Monnier, David C. Thévenaz, Sandor Balog, Gina L. Fiore, Dimitri Vanhecke, Barbara Rothen-Rutishauser, Alke Petri-Fink . A guide to investigating colloidal nanoparticles by cryogenic transmission electron microscopy: pitfalls and benefits. AIMS Biophysics, 2015, 2(3): 245-258. doi: 10.3934/biophy.2015.3.245 |
[4] | O.S. Sorzano Carlos, Vargas Javier, Otón Joaquín, Abrishami Vahid, M. de la Rosa-Trevín José, del Riego Sandra, Fernández-Alderete Alejandro, Martínez-Rey Carlos, Marabini Roberto, M. Carazo José . Fast and accurate conversion of atomic models into electron density maps. AIMS Biophysics, 2015, 2(1): 8-20. doi: 10.3934/biophy.2015.1.8 |
[5] | Nicholas Spellmon, Xiaonan Sun, Wen Xue, Joshua Holcomb, Srinivas Chakravarthy, Weifeng Shang, Brian Edwards, Nualpun Sirinupong, Chunying Li, Zhe Yang . New open conformation of SMYD3 implicates conformational selection and allostery. AIMS Biophysics, 2017, 4(1): 1-18. doi: 10.3934/biophy.2017.1.1 |
[6] | Suleyman Yilmaz . Using elastic scattering to determination of diseases via urine samples. AIMS Biophysics, 2021, 8(4): 307-317. doi: 10.3934/biophy.2021024 |
[7] | Michelle de Medeiros Aires, Janine Treter, Antônio Nunes Filho, Igor Oliveira Nascimento, Alexandre José Macedo, Clodomiro Alves Júnior . Minimizing Pseudomonas aeruginosa adhesion to titanium surfaces by a plasma nitriding process. AIMS Biophysics, 2017, 4(1): 19-32. doi: 10.3934/biophy.2017.1.19 |
[8] | Ateeq Al-Zahrani, Natasha Cant, Vassilis Kargas, Tracy Rimington, Luba Aleksandrov, John R. Riordan, Robert C. Ford . Structure of the cystic fibrosis transmembrane conductance regulator in the inward-facing conformation revealed by single particle electron microscopy. AIMS Biophysics, 2015, 2(2): 131-152. doi: 10.3934/biophy.2015.2.131 |
[9] | Adam Redzej, Gabriel Waksman, Elena V Orlova . Structural studies of T4S systems by electron microscopy. AIMS Biophysics, 2015, 2(2): 184-199. doi: 10.3934/biophy.2015.2.184 |
[10] | Vittoria Raimondi, Alessandro Grinzato . A basic introduction to single particles cryo-electron microscopy. AIMS Biophysics, 2022, 9(1): 5-20. doi: 10.3934/biophy.2022002 |
These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.
Natural polymers tend to be preferred over synthetic polymers because of their intrinsic properties, such as, biodegradability, biocompatibility, and non-toxicity, which favor their application in the biomedical area [1],[2]. Polysaccharides are natural polymers with more than ten monosaccharides linked by glycosidic bonds; some present important functional properties derived from their structural characteristics [3]. Sulfated polysaccharides from marine algae have attracted increasing attention in recent years since they have reported various biological activities, biocompatibility, biodegradability, moisture retention, and colloidal properties [4]. These polysaccharides can be used to develop multiple renewable biomaterials such as drug delivery vehicles, dressings, and others with a biomedical approach [5]. Nevertheless, understanding these macromolecules' functionalities and potential uses requires comprehending their biophysical and microstructural properties.
There are studies regarding the extraction and potential application of sulfated polysaccharides present in marine microalgae from different regions of the world, with the information being less abundant than for marine macroalgae. Due to the intricate processes involved in microalga-sulfated polysaccharide extraction and purification and their chemical structure complexity, only a few structures have been resolved for these macromolecules. Fucose, xylose, galactose, ribose, glucose, mannose, rhamnose, and uronic acids are monosaccharides composing microalga-sulfated polysaccharides [6]. Sulfated polysaccharides present bioactive properties related to their structural characteristics, such as sugar components, molecular weight, and sulfate content. These polysaccharides have been reported to present antitumoral, anticoagulant, antioxidant, and antiviral properties, among others [3],[6],[7]. The degree of sulfation in marine alga polysaccharides may depend on the algae's genus, species, season, geographic location, and reproductive stage. This sulfate content may affect the polysaccharide bioactivities [8].
Furthermore, because the characteristics of sulfated polysaccharides from algae can vary depending on the growth conditions, different maritime regions may produce differences in the structure and functionality of these biopolymers. We examined the morphology, physical properties, and chain conformation of sulfated polysaccharides derived from the Sea of Cortés microalga Chaetoceros muelleri. This maritime region in Northwestern Mexico is also called California Gulf and has been little explored concerning sulfated polysaccharides in microalgae. Additionally, the Sea of Cortés is recognized as an area of global marine conservation significance and has been a protected UNESCO World Heritage Site since 2005 [5]. In this regard, the current research is crucial to developing a thorough scientific understanding of the sulfated polysaccharides in C. muelleri growing in the Sea of Cortés.
The sulfated polysaccharide recovered from microalgae C. muelleri (CMSP) present in the Sea of Cortés was extracted as described in a previous study [6],[9]. Sigma-Aldrich Chemical Company, located in St. Louis, MO, USA, supplies all chemical reagents.
A Nicolet iS50 FT-RI spectrometer (Madison, WI, USA) was used to record the FTIR spectrum of dry CMSP powder. The iS50 ATR analysis was used to analyze the sample. The spectrum was captured between 4000 and 400 cm−1 [9]. The degree of sulfate substitution (DS) in CMSP (sulfates per disaccharide repeat unit) was estimated from the proportion of absorbances at 845 and 2920 cm−1. The band at 2920 related to C-H stretching serves as a benchmark for the overall amount of sugar, and the band at 845 cm−1 is related to the 3-linked galactose-4-sulfate [10].
Macromolecular characteristics such as weight-average molar mass (Mw), number-average molar mass (Mn), polydispersity index (PI), intrinsic viscosity ([η]), radius of gyration (RG), hydrodynamic radius (Rh), characteristic ratio (C∞), persistence length (q), and Mark–Houwink–Sakurada constants (α and K) were determined using SEC-MALS. A MALS detector (DAWN HELOS-II 8) coupled with a Viscometer (ViscoStar-II) and a refractive index (Optilab T-rEX) (Wyatt Technology Corp., Santa Barbara, CA, USA) was utilized [9]. A flow rate of 0.7 mL/min (50 mM NaNO3/0.02% NaN3 filtered 0.45 µm, Millipore) was applied to an HPLC System (Agilent Technologies, Inc., Santa Clara, CA, USA). The Shodex OH-pak SBH-Q-804 and 805 columns (Shodex Showa Denco K.K., Tokyo, JPN) were used. The ASTRA 6.1 software was applied and developed by Wyatt Technology to analyze and characterize macromolecules. The refractive index increment (dn/dc) used was 0.146 mL/g. C∞and q values were calculated as previously reported [9].
Dynamic light scattering (DLS) analysis was applied to investigate zeta potential (ζ), conductivity, and diffusion coefficient (D) in CMSP. Furthermore, Phase Analysis Light Scattering (PALS) enabled us to study electrophoretic mobility (µ) [9]. The software DYNAMICS 7.3.1.15 (Wyatt Technology Corp., Santa Barbara, CA, USA) and a Möbiuζ (Wyatt Technology Corp., Santa Barbara, CA, USA) were utilized at 25 °C. The sample was dispersed at 0.1% (w/v) using distilled water as the solvent. The Smoluchowski equation was used to calculate electrophoretic mobility with a liquid electric permittivity of 79 and a viscosity of 0.89 cP.
CMSP was examined using a 750× magnification and a 10 kV voltage in a field emission scanning electron microscope (SEM) (JEOL 5410LV, JEOL, Peabody, MA, USA). Secondary and backscattered electron imaging modes were used to acquire SEM images [6].
CMSP presented characteristic FTIR absorption bands described regarding sulfated polysaccharides in the literature from marine algae (Figure 1) [10],[11]. The signals at 3378 and 2920 cm−1 are associated with the stretching vibration of O–H and C–H links [12]. The 1668–1611 cm−1 bands are attributed to the existence of amide I and amide II groups [9]. The signal at 1388 cm−1 has been related to the C=O group, which may be attributed to carbonyl groups from uronic acids in the molecule. The band at 1102 cm−1 corresponds to the vibrational bond energies of the C–O–C and C–O structures related to glycosidic linkage in polysaccharides [13].
The small band at 1250 and 845 cm−1 indicates the presence of S=O and C–O–S linkages, corresponding to the vibration of the sulfate ester group, confirming sulfate's presence in the polysaccharide. It has been reported that the sulfate content in marine polysaccharides can be estimated from the absorbance ratio at 845/2920 cm−1. The absorbance at 845 cm−1 has been ascribed to 3-linked galactose-4-sulfate, while the absorbance at 2920 cm−1 associated with C-H has been utilized as an indicator for the total amount of sugar [10]. Based on the ratio of absorbances at 845 and 2920 cm−1, CMSP's degree of sulfate substitution (DS, sulfates per disaccharide repeat unit) is estimated to be 0.5. Desulfated kappa-carrageenan and sulfated agarose have DS of 0.24 and 0.12, respectively, determined from the 845/2920 absorbances ratio [10]. Knowing the sulfate content and other structural characteristics in polysaccharides, for instance, molecular weight and intrinsic viscosity, is essential to know the relationships between the biopolymers and their potential application [14]. In addition, different sulfate content may modify these polysaccharides' physicochemical properties and bioactivities [15].
Figure 2 displays the CMSP elution profile along with signals for the light scattering (LS), differential refractive index (dRI), and ultraviolet (UV). A notable peak eluting before minute 10 was part of the sample's bimodal LS response. Small residual proteins may cause the UV signal at high elution times [9]. Table 1 displays the macromolecular properties of CMSP. The radius of gyration (RG), hydrodynamic radius (Rh), intrinsic viscosity [η], Mw, and polydispersity index (PI) values of CMSP are comparable to those found in the literature for sulfated polysaccharides in marine algae [9],[16].
K and α values provide information about the conformation of polysaccharides in the Mark–Houwink–Sakurada equation about intrinsic viscosity. An α value of 1.26 corresponds to a very rigid chain, while 0.50–0.80 represents flexible random coil structures. Moreover, a low K value denotes a compact coil conformation, and a high K value denotes an expanded coil conformation [17]–[19]. The α and K values obtained for CMSP suggest the existence of a flexible and compact random coil structure in these macromolecules.
We computed the C∞ and q parameters for the first time. The q value was lower than those reported for macroalga polysaccharides such as alginate [9]. The q value represents the distance over which the chain's direction persists and characterizes the polymer chain flexibility. A lower q value indicates a flexible coil conformation in CMSP. C∞ is the ratio of the end-to-end distance of the freely joined chain of n bonds of known length to the observed end-to-end distance of the polysaccharide chain. The C∞ value depends on the polysaccharide stiffness; numbers from 7 up to 9 correspond to flexible polymers [17]–[19]. It is important to note that SEC-MALS yields an average chain conformational space. Nevertheless, structural and conformational elucidation of these macromolecules is necessary for understanding their function. The CMSP conformational characteristics are reported for the first time in this study.
Weight-average molar mass (Mw, kDa) | 1933 |
Polydispersity index (PI, Mw/Mn) | 1.1 |
Intrinsic viscosity ([η], mL/g) | 577 |
Radius of gyration (RG, nm) | 62 |
Hydrodynamic radius (Rh, nm) | 44 |
Characteristic ratio (C∞) | 7.8 |
Persistence length (q, nm) | 2.2 |
Mark–Houwink–Sakurada | |
Exponent α | 0.76 |
Coefficient K | 9.76 x 10−3 |
CMSP's physical characteristics are presented in Table 2. In a dispersed macromolecule, the outer region where the ions are less associated has a boundary. The potential in this area is the zeta potential (ζ), and when the macromolecule moves, the ions outside the boundary remain there. CMSP's sulfate content causes the ζ potential to be negatively charged. Additionally, a particle's electrophoretic mobility (µ), which is inversely related to conductivity and proportional to the zeta potential (Henry Equation), is represented by its velocity when exposed to an electric field [20]. The diffusion coefficient of CMSP was also determined, representing the fluctuations in scattered light intensity. In this study, ζ potential, µ, conductivity, and D were higher than those reported for sulfated polysaccharides in Navicula sp. [9], possibly due to a high sulfate content in CMSP. These physical characteristics enable a better understanding of macromolecule behavior under aqueous conditions and a possible application in diverse areas, especially those involving biological systems.
Zeta potential (ζ, mV) | −26.43 ± 3.64 |
Electrophoretic mobility (µ, µm cm/s V) | −2.07 ± 0.28 |
Conductivity (mS/cm) | 1.25 ± 0.01 |
Diffusion coefficient (D, cm2/s) | 1.79 × 10−8 ± 1.14 × 10−9 |
Water as solvent and measurements at 25 °C. The results are expressed as mean ± SD.
CMSP surface microstructure was irregular, with many pores of uneven shape and size (Figure 3). The micrograph suggests repulsive forces between the molecules, resulting in many intermolecular pores, probably related to the negative charge from sulfate groups. The sample also displayed inhomogeneous lumps, suggesting the entanglement and aggregation of CMSP chains. According to a report, a rough surface with some holes could signify the molecule has a branched structure [21]. The CMSP surface morphology generally is similar to that reported for other polysaccharides from marine algae [6]–[8]. Polysaccharides' surface morphology and interface interactions impact the molecule's functional properties, such as mechanical strength, environmental affinity, and electrostatic interaction. Additionally, these molecule functionalities define possible applications in medicine, food, and biomaterials.
We investigated the chain conformation, physical characteristics, and morphology of CMSP. According to the findings, CMSP in an aqueous solution has a compact and flexible random coil conformation and is negatively charged because it contains sulfate. In addition, polysaccharide dispersion presents conductivity and electrophoretic mobility. CMSP surface morphology is rough and forms irregular clusters. This information is essential to predict the CMSP interaction with other molecules and biological systems, which might open the door to new possibilities in applying little-explored marine microalgae sulfated polysaccharides in diverse areas such as biomedicine.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
[1] |
L. A. Mastoi, Q. Mastoi, M. Elhoseny, M. S. Memon, M. A. Mohammed, Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using iot assisted mobile fog cloud, Enterp. Inf. Syst., (2021), 1–23. doi: 10.1080/17517575.2021.1883122. doi: 10.1080/17517575.2021.1883122
![]() |
[2] |
H. Zhu, P. Tiwari, A. Ghoneim, M. S. Hossain, A collaborative ai-enabled pretrained language model for aiot domain question answering, IEEE Trans. Ind. Inf., (2021). doi: 10.1109/TII.2021.3097183. doi: 10.1109/TII.2021.3097183
![]() |
[3] |
A. Lakhan, M. Ahmad, M. Bilal, A. Jolfaei, R. M. Mehmood, Mobility aware blockchain enabled offloading and scheduling in vehicular fog cloud computing, IEEE Trans. Intell. Transp. Syst., (2021), doi: 10.1109/TITS.2021.3056461. doi: 10.1109/TITS.2021.3056461
![]() |
[4] |
S. Mishra, H. Thakkar, P. K. Mallick, P. Tiwari, A. Alamri, A sustainable ioht based computationally intelligent healthcare monitoring system for lung cancer risk detection, Sustainable Cities Soc., 103079, (2021). doi: 10.1016/j.scs.2021.103079. doi: 10.1016/j.scs.2021.103079
![]() |
[5] |
A. Lakhan, M. S. Memon, M. Elhoseny, M. A. Mohammed, M. Qabulio, M. Abdel-Basset, et al., Cost-efficient mobility offloading and task scheduling for microservices iovt applications in container-based fog cloud network, Cluster Comput., (2021), 1–23. doi: 10.1007/s10586-021-03333-0. doi: 10.1007/s10586-021-03333-0
![]() |
[6] |
A. Lakhan, M. A. Mohammed, A. N. Rashid, S. Kadry, T. Panityakul, K. H. Abdulkareem, et al., Smart-contract aware ethereum and client-fog-cloud healthcare system, Sensors, 21 (2021), 4093. doi: 10.3390/s21124093. doi: 10.3390/s21124093
![]() |
[7] |
A. Lakhan, M. A. Dootio, T. M. Groenli, A. H. Sodhro, M. S. Khokhar, Multi-layer latency aware workload assignment of e-transport iot applications in mobile sensors cloudlet cloud networks, Electronics, 10 (2021), 1719. doi: 10.3390/electronics10141719. doi: 10.3390/electronics10141719
![]() |
[8] |
M. Hussain, L. F. Wei, A. Lakhan, S. Wali, S. Ali, A. Hussain, Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing, Sustainable Comput. Inf. Syst., 30 (2021), 100517. doi: 10.1016/j.suscom.2021.100517. doi: 10.1016/j.suscom.2021.100517
![]() |
[9] |
A. Lakhan, X. Li, Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks, Computing, 102 (2020), 105–139. doi: 10.1007/s00607-019-00733-4. doi: 10.1007/s00607-019-00733-4
![]() |
[10] | A. Lakhan, L. Xiaoping, Energy aware dynamic workflow application partitioning and task scheduling in heterogeneous mobile cloud network, in 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB), IEEE, (2018), 1–8. doi: 10.1109/ICCBB.2018.8756442. |
[11] | A. Lakhan, X. Li, Content aware task scheduling framework for mobile workflow applications in heterogeneous mobile-edge-cloud paradigms: Catsa framework, in 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), IEEE, (2019), 242–249. doi: 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00044. |
[12] |
A. Lakhan, X. Li, Mobility and fault aware adaptive task offloading in heterogeneous mobile cloud environments, EAI Endorsed Trans. Mobile Commun. Appl., 5 (2019). doi: 10.4108/eai.3-9-2019.159947. doi: 10.4108/eai.3-9-2019.159947
![]() |
[13] |
J. Qian, P. Tiwari, S. P. Gochhayat, H. M. Pandey, A noble double-dictionary-based ecg compression technique for ioth, IEEE Internet Things J., 7 (2020), 10160–10170. doi: 10.1109/JIOT.2020.2974678. doi: 10.1109/JIOT.2020.2974678
![]() |
[14] |
F. Zhang, M. M. Wang, Stochastic congestion game for load balancing in mobile edge computing, IEEE Internet Things J., (2020). doi: 10.1109/JIOT.2020.3008009. doi: 10.1109/JIOT.2020.3008009
![]() |
[15] |
A. Lakhan, Q. Mastoi, M. A. Dootio, F. Alqahtani, I. R. Alzahrani, F. Baothman, et al., Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network, electronics, 10 (2021), 1974. doi: 10.3390/electronics10161974. doi: 10.3390/electronics10161974
![]() |
[16] |
F. H. Khoso, A. Lakhan, A. A. Arain, M. A. Soomro, S. Z. Nizamani, K. Kanwar, A microservice-based system for industrial internet of things in fog-cloud assisted network, Eng., Technol. Appl. Sci. Res., 11 (2021), 7029–7032. doi: 10.48084/etasr.4077. doi: 10.48084/etasr.4077
![]() |
[17] |
F. H. Khoso, A. A. Arain, A. Lakhan, A. Kehar, S. Z. Nizamani, Proposing a novel iot framework by identifying security and privacy issues in fog cloud services network, Int. J, 9 (2021), 592–596. doi: 10.30534/ijeter/2021/10952021. doi: 10.30534/ijeter/2021/10952021
![]() |
[18] |
A. Lakhan, R. Singh, Implementation of etl tool for data warehousing for non-hodgkin lymphoma (nhl) cancer in public sector, pakistan, Int. J., 9 (2021). doi: 10.30534/ijeter/2021/27972021. doi: 10.30534/ijeter/2021/27972021
![]() |
[19] |
A. Lakhan, F. H. Khoso, A. A. Arain, K. Kanwar, Serverless based functions aware framework for healthcare application, Int. J., 9 (2021). doi: 10.30534/ijeter/2021/19942021. doi: 10.30534/ijeter/2021/19942021
![]() |
[20] |
U. Rehman, M. A. S. A. Lakhan, A review on state of the art in flipped classroom technology a blended e-learning, Int. J., 9 (2021). doi: 10.30534/ijeter/2021/22972021. doi: 10.30534/ijeter/2021/22972021
![]() |
[21] | I. A. Jamali, A. Lakhan, D. Kumar, A. R. Mahessar, R. lodhi, Energy efficient task assignment algorithm framework in mo-bile cloud computing, GSJ, 6 (2018), 171. |
[22] | A. L. Mujeeb-ur Rehman, Z. Hussain, F. H. Khoso, A. A. Arain, Cyber security intelligence and ethereum blockchain technology for e-commerce, Int. J., 9 (2021). |
[23] | A. Lakhan, D. K. Sajnani, M. Tahir, M. Aamir, R. Lodhi, Delay sensitive application partitioning and task scheduling in mobile edge cloud prototyping, in International Conference on 5G for Ubiquitous Connectivity, Springer, (2018), 59–80. |
[24] | D. K. Sajnani, A. R. Mahesar, A. Lakhan, I. A. Jamali, R. Lodhi, M. Aamir, Latency aware optimal workload assignment in mobile edge cloud offloading network, in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), IEEE, (2018), 658–662. doi: 10.1109/CompComm.2018.8780954. |
[25] |
D. K. Sajnani, A. R. Mahesar, A. Lakhan, I. A. Jamali, et al., Latency aware and service delay with task scheduling in mobile edge computing, Commun. Network, 10 (2018), 127. doi: 10.4236/cn.2018.104011. doi: 10.4236/cn.2018.104011
![]() |
[26] |
A. H. Sodhro, Z. Luo, A. K. Sangaiah, S. W. Baik, Mobile edge computing based qos optimization in medical healthcare applications, Int. J. Inf. Manage., 45 (2019), 308–318. doi: 10.4236/cn.2018.104011. doi: 10.4236/cn.2018.104011
![]() |
[27] |
A. H. Sodhro, S. Pirbhulal, V. H. C. De Albuquerque, Artificial intelligence-driven mechanism for edge computing-based industrial applications, IEEE Trans. Ind. Inf., 15 (2019), 4235–4243. doi: 10.1109/TII.2018.2889692
![]() |
[28] |
M. Muzammal, R. Talat, A. H. Sodhro, S. Pirbhulal, A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks, Inf. Fusion, 53 (2020), 155–164. doi: 10.1109/TII.2019.2902878. doi: 10.1109/TII.2019.2902878
![]() |
[29] | H. Magsi, A. H. Sodhro, F. A. Chachar, S. A. K. Abro, G. H. Sodhro, S. Pirbhulal, Evolution of 5g in internet of medical things, in 2018 international conference on computing, mathematics and engineering technologies (iCoMET), IEEE, (2018), 1–7. doi: 10.1109/ICOMET.2018.8346428. |
[30] |
T. Zhang, A. H. Sodhro, Z. Luo, N. Zahid, M. W. Nawaz, S. Pirbhulal, et al., A joint deep learning and internet of medical things driven framework for elderly patients, IEEE Access, 8 (2020), 75822–75832. doi: 10.1109/ACCESS.2020.2989143. doi: 10.1109/ACCESS.2020.2989143
![]() |
[31] |
T. Li, Z. Wang, Y. Chen, C. Li, Y. Jia, Y. Yang, Is semi-selfish mining available without being detected? Int. J. Intell. Syst., 2021. doi: 10.1002/int.22656. doi: 10.1002/int.22656
![]() |
[32] |
A. A. Mutlag, M. K. A. Ghani, M. A. Mohammed, A. Lakhan, O. Mohd, K. H. Abdulkareem, et al., Multi-agent systems in fog-cloud computing for critical healthcare task management model (chtm) used for ecg monitoring, Sensors, 21 (2021), 6923. doi: 10.3390/s21206923. doi: 10.3390/s21206923
![]() |
[33] | X. Yu, Z. Wang, Y. Wang, F. Li, T. Li, Y. Chen, et al., Impsuic: A quality updating rule in mixing coins with maximum utilities, Int. J. Intell. Syst., 36 (2020), 1182–1198. |
[34] |
T. Li, Y. Chen, Y. Wang, Y. Wang, M. Zhao, H. Zhu, et al., Rational protocols and attacks in blockchain system, Secur. Commun. Networks, 2020 (2020), 1–11. doi: 10.1155/2020/8839047. doi: 10.1155/2020/8839047
![]() |
[35] |
G. Yang, Y. Wang, Z. Wang, Y. Tian, X. Yu, S. Li, Ipbsm: An optimal bribery selfish mining in the presence of intelligent and pure attackers, Int. J. Intell. Syst., 35 (2020), 1735–1748. doi: 10.1002/int.22270. doi: 10.1002/int.22270
![]() |
[36] |
Y. Wang, G. Yang, T. Li, L. Zhang, Y. Wang, L. Ke, et al., Optimal mixed block withholding attacks based on reinforcement learning, Int. J. Intell. Syst., 35 (2020), 2032–2048. doi: 10.1002/int.22282. doi: 10.1002/int.22282
![]() |
[37] |
X. Liu, X. Yu, H. Zhu, G. Yang, Y. Wang, X. Yu, et al., A game-theoretic approach of mixing different qualities of coins, Int. J. Intell. Syst., 35 (2020), 1899–1911. doi: 10.1002/int.22277. doi: 10.1002/int.22277
![]() |
[38] |
Ö. Çelikel, T. Ovatman, Distributed application checkpointing for replicated state machines, Scalable Comput.: Pract. Exper., 22 (2021), 67–79. doi: 10.12694/scpe.v22i1.1840. doi: 10.12694/scpe.v22i1.1840
![]() |
[39] |
R. Wang, N. Chen, X. Yao, L. Hu, Fasdq: Fault-tolerant adaptive scheduling with dynamic qos-awareness in edge containers for delay-sensitive tasks, Sensors, 21 (2021), 2973. doi: 10.3390/s21092973. doi: 10.3390/s21092973
![]() |
Weight-average molar mass (Mw, kDa) | 1933 |
Polydispersity index (PI, Mw/Mn) | 1.1 |
Intrinsic viscosity ([η], mL/g) | 577 |
Radius of gyration (RG, nm) | 62 |
Hydrodynamic radius (Rh, nm) | 44 |
Characteristic ratio (C∞) | 7.8 |
Persistence length (q, nm) | 2.2 |
Mark–Houwink–Sakurada | |
Exponent α | 0.76 |
Coefficient K | 9.76 x 10−3 |
Zeta potential (ζ, mV) | −26.43 ± 3.64 |
Electrophoretic mobility (µ, µm cm/s V) | −2.07 ± 0.28 |
Conductivity (mS/cm) | 1.25 ± 0.01 |
Diffusion coefficient (D, cm2/s) | 1.79 × 10−8 ± 1.14 × 10−9 |
Water as solvent and measurements at 25 °C. The results are expressed as mean ± SD.
Weight-average molar mass (Mw, kDa) | 1933 |
Polydispersity index (PI, Mw/Mn) | 1.1 |
Intrinsic viscosity ([η], mL/g) | 577 |
Radius of gyration (RG, nm) | 62 |
Hydrodynamic radius (Rh, nm) | 44 |
Characteristic ratio (C∞) | 7.8 |
Persistence length (q, nm) | 2.2 |
Mark–Houwink–Sakurada | |
Exponent α | 0.76 |
Coefficient K | 9.76 x 10−3 |
Zeta potential (ζ, mV) | −26.43 ± 3.64 |
Electrophoretic mobility (µ, µm cm/s V) | −2.07 ± 0.28 |
Conductivity (mS/cm) | 1.25 ± 0.01 |
Diffusion coefficient (D, cm2/s) | 1.79 × 10−8 ± 1.14 × 10−9 |