Four new hybrid (bolaphile/amphiphile) ion-pairs were synthesized. Electron microscopy indicated that each of these forms bilayer membranes upon dispersion in aqueous media. Membrane properties have also been examined by differential scanning calorimetry, microcalorimetry, temperature-dependent fluorescence anisotropy measurements, and UV-vis spectroscopy. The T-m values for the vesicular 1, 2, 3, 4, and 5 were 38, 12, 85, 31.3, and 41.6 degrees C, respectively. Interestingly the T-m values for 1 and 3 were found to depend on their concentration. The entrapment of small solute and the release capability have also been examined to demonstrate that these bilayers form enclosed vesicles. X-ray diffraction of the cast films has been performed to understand the nature and the thickness of these membrane organizations. The membrane widths ranged from 33 to 47 Angstrom. Finally, the above observations have been analyzed in light of the results obtained from molecular modeling studies. Thus we have demonstrated that membrane properties can be modulated by simple structural changes at the amphiphile level. It was shown that by judicious incorporation of central, isomeric, disubstituted aromatic units as structural anchors into different bolaphiles, one can modulate the properties of the resulting vesicles.

Synthesis and vesicle formation from hybrid bolaphile/ amphiphile ion-pairs. evidence of membrane property modulation by molecular design

A chiral ditopic crownether functionalized oligo(p-phenylenevinylene), COPV, has been synthesized and fully characterized. The binding properties and the organization of COPV have been studied in solution and in the solid state. In chloroform, due to the benzo-15-crown-5 moieties, COPV is able to bind cations. In case of K+ a strong 2 : 2 complex is formed in which two COPVs are sandwiched between two potassium ions. Na+ is bound following a two-step negative cooperative process to form a 2 : 1 Na+/COPV complex. When COPV in the presence of potassium ions is drop-cast on a silicon substrate, fibers are formed. In waterCOPV forms stable helical aggregates as can be concluded from the Cotton effect, the UV-vis and fluorescence spectroscopy. AFM and light scattering studies show that ellipsoidal aggregates are present in water.

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Tuning the self-assembly of a ditopic crown ether functionalized oligo(p-phenylenevinylene)

Publisher Summary This chapter discusses eight-membered and larger rings. The chapter reviews meso- and macrocycles possessing heteroatoms and/or subheterocyclic rings, including carbon–oxygen rings, carbon–nitrogen rings, carbon–sulfur rings, carbon–oxygen/carbon–nitrogen rings, carbon–nitrogen–oxygen rings, carbon–nitrogen–sulfur rings, carbon–phosphorus–oxygen rings, carbon–phosphorus–sulfur rings, carbon–sulfur–oxygen rings, carbon–nitrogen–sulfur–oxygen rings, carbon–silicon/selenium/tellurium rings, carbon–metal rings, carbon–nitrogen–metal rings, carbon–oxygen–nitrogen–metal rings, carbon–silicon/germanium–nitrogen–metal rings, and carbon–phosphorus–oxygen–metal rings. The reversible acid-catalyzed transacetalation of a cyclophane has been shown to undergo a ring-fusionlring-fission process to generate a mixture of polymer cyclicformaldehyde acetals by means of oxonium ion intermediates. A methanofullerene derivative possessing an ammonium subunit has been prepared and subsequently shown to form a supramolecular complex with a porphyrin-crown ether conjugate. The synthesis and study of these fullerene-containing supramolecular photoactive devices have also been reported. Some interesting non-medical applications have been found for pyrimidines.

Chapter 8 Eight-membered and larger rings

The huge amount of data generated by the Internet of Things (IoT) devices needs the computational power and storage capacity provided by cloud, edge, and fog computing paradigms. Each of these computing paradigms has its own pros and cons. Cloud computing provides enhanced data storage and computing power but causes high communication latency. Edge and fog computing provide similar services with lower latency but limited capacity, capability, and coverage. A single computing paradigm cannot fulfill all the requirements of IoT devices and a federation between them is needed to extend their capacity, capability, and services. This federation is beneficial to both subscribers and providers and also reveals research issues in traffic offloading between clouds, edges, and fogs. Optimization has traditionally been used to solve the problem of traffic offloading. However, in such a complex federated system, traditional optimization cannot keep up with the strict latency requirements of decision-making, ranging from milliseconds to sub-seconds. Machine learning approaches, especially reinforcement learning, are consequently becoming popular because they could quickly solve offloading problems in dynamic environments with some unknown information. This study provides a novel federal classification between cloud, edge, and fog and presents a comprehensive research roadmap on offloading for different federated scenarios. We survey the relevant literature on the various optimization approaches used to solve this offloading problem and compare their salient features. We then provide a comprehensive survey on offloading in federated systems with machine learning approaches and the lessons learned as a result of these surveys. Finally, we outline several directions for future research and challenges that have to be faced in order to achieve such a federation.

Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A Survey

Against the backdrop of the booming supply chain finance and logistics industry, Logistics 4.0 has emerged. However, the financing capacity of today's logistics companies is still unable to respond to the needs of the rapid development of the supply chain. On the one hand, the problem of logistics companies’ lack of existing high-value collateral in supply chain finance has led to a clutter of their credit data and difficulty in verifying their creditworthiness. On the other hand, the massive access to logistics companies’ business information can also lead to privacy leaks. At the same time, the transparency feature of blockchain is used to solve the financing dilemma of many industries in supply chain finance. On this basis, we propose a blockchain-based financing scheme (BFS) for logistics company. BFS utilises more efficient and interpretative smart contract technology, as well as improved privacy information query and invocation algorithms, to realise automatic control of entity node privacy information flow and significantly simplifying the steps and lowering the thresholds to financing for logistics companies, while protecting the privacy of their data. After extensive simulation testing, BFS can run supply chain blocks at a stable transaction throughput of around 280 RPS, with data transfers that meet the financing needs of logistics companies, providing a higher and more stable performance than the native Hyperledger Fabric.

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2088698?needAccess=true

BFS: A blockchain-based financing scheme for logistics company in supply chain finance

Green communications and networking technologies boost the interconnection and communication of Internet of Things (IoT) devices, so as to facilitate the task offloading. Artificial Intelligence (AI) based task offloading scheme is being widely studied. However, most of AI based task offloading schemes only reward the devices that process tasks locally, and do not consider the untrusted devices. To solve these issues, a Trust based Multi-Agent Imitation Learning (T-MAIL) scheme is proposed by us to improve task offloading for edge computing in smart cities. Firstly, we established a full task offloading incentive model, in which edge devices can get comprehensive reward from local processing and task re-offloading. Secondly, we proposed an active trust acquisition method, which can obtain the device trust efficiently and accurately. Finally, the new task offloading incentive scheme and trust acquisition method are introduced into multi-agent imitation learning. The experimental results show that, the proposed T-MAIL will effectively improve task offloading. Compared with MILP and DQN based task offloading solution, the average task completion time is reduced by 5.5% and 52.7% respectively. Compared with MILP scheme, the task offloading rate is increased by 19.2%. In addition, the trust difference ratio between trusted devices and untrusted devices can reach 56.1%.

Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

The synthetic single-chain aza crown ethers containing phenyl group as a rigid segment form self assembly of ordered bilayer membranes as an aqueous dispersion and a cast film.

Self assembly of bilayer membranes from single-chain aza crown ether

This paper proposes a sliding mode adaptive control strategy for the trajectory control of quadrotor unmanned aerial vehicles (UAVs) when uncertain disturbances may exist in the system during flight. The proposed controller controls the position and attitude of a quadrotor UAV with control laws designed based on the sliding mode control theory. The external disturbances in the UAV system are accurately estimated and compensated based on an adaptive law. Stability analysis based on the Lyapunov stability theory shows that the designed adaptive controller can ensure the convergence to a desired trajectory under the presence of uncertain disturbances. Simulation results show that the robustness and control performance of the proposed adaptive controller is better than that of a PID controller and a controller based on sliding mode control only.

Trajectory Control of Quadrotor Unmanned Aerial Vehicles with Sliding Mode Adaptive Method

Traffic state estimation (TSE) is an important aspect of the Internet of Vehicles (IoV) for road path planning and better driving experience. In IoV, with the support of edge intelligence, real-time traffic data can be processed by edge computing (EC) servers for informed decision-making. However, collecting trajectory information from vehicles in a centralized manner may increase transmission delay and cause driver privacy leakage problems. In this paper, we firstly propose a federated learning (FL) framework for TSE, named FedTSE, with privacy preservation by jointly considering TSE accuracy, model computation, and transmission cost. Then, a TSE model is designed based on the long short-term memory (LSTM) as the local training model for joint prediction of vehicular speed and traffic flow. Considering the resource limitation of computation/communication, we further propose a deep reinforcement learning (DRL)-based algorithm for model parameter uploading/downloading decisions to improve the estimation accuracy of local models and balance the tradeoff between computation and communication cost. Simulation results show the proposed FedTSE achieves a lower cost and higher prediction training accuracy in TSE.

Chunsheng Zhu

Papers

Trust-Based Multi-Agent Imitation Learning for Green Edge Computing in Smart Cities

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

Self assembly of bilayer membranes from single-chain aza crown ether

Trajectory Control of Quadrotor Unmanned Aerial Vehicles with Sliding Mode Adaptive Method

FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV