Network coding-aware routing has become an effective paradigm to improve network throughput and relieve network congestion. However, to detect coding opportunities and make routing decision for a ...data flow, most existing XOR coding-aware routing methods need to consume much overhead to collect overhearing information on its possible routing paths. In view of this, we propose low-overhead and dynamic Coding-Aware Routing via Tree-based Address (CARTA) for wireless sensor networks (WSNs). In CARTA, a Multi-Root Multi-Tree Topology (MRMTT) with a tree-based address allocation mechanism is firstly constructed to provide transmission paths for data flows. Then, a low-overhead coding condition judgment method is provided to detect real-time coding opportunities via tree address calculation in the MRMTT. Further, CARTA defines routing address adjustments caused by encoding and decoding to ensure the flows’ routing paths can be adjusted flexibly according to their real-time coding opportunities. It also makes additional constraints on congestion and hop count in the coding condition judgment to relieve network congestion and control the hop counts of routing paths. The simulation results verify that CARTA can utilize more coding opportunities with less overhead on coding, and this is ultimately beneficial for promoting network throughout and balancing energy consumption in WSNs.
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Highly stable zirconium metal-organic frameworks (UiO-66 and UiO-66(NH2)) had been synthesized and investigated to remove arsenic (As) from contaminated water. The As(III, V) removal ...performance was studied by batch experiments and adsorption kinetics. At the pH of 9.2 ± 0.1, UiO-66 had exceptional removal capacities of 205.0 and 68.21 mg/g for As(III) and As(V), respectively. The As removal processes were exothermic and verified as chemisorption reactions according to the calculation of Gibbs free energy and Dubinin-Radushkevich (D-R) isotherm model. Fixed-bed reactor removal experiments indicated that the number of effective treatment volumes reached 2270 and 1775 BVs for As(III) and As(V), respectively, until the most stringent As regulation level of 10 μg/L (initial As concentration at 100 μg/L) was reached. FTIR and XPS study indicated that ZrO bonds of zirconium metal-organic frameworks played a vital role in As adsorption. XANES revealed the As adsorption on UiO-66 without the change of oxidation state. More intriguingly, EXAFS spectra demonstrated the main formation of bidentate mononuclear complexes for As(V), and bidentate binuclear complexes for As(III) on the hexanuclear Zr cluster of UiO-66. The advantages of nontoxicity, high stability, high As adsorption capacity, low-cost and easy availability confirm the highly promising application of zirconium metal-organic frameworks in As-contaminated wastewater remediation.
Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its ...success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods.
In recent years, mobile sinks are used more and more efficiently in sensor networks to collect data for the mobility advantage in balancing energy consumption than static sinks. However, it is still ...a challenge in both efficiency and network cost to avoid generating large amounts of overheads and lots of unnecessary energy consumption, when data source forward sensing data to mobile sink proactively according to their location information broadcasted all over the network. To reduce the overhead and balance the energy consumption in a network, we propose a clue-based data collection routing (CBDCR) protocol for mobile sensor networks. In CBDCR, a mobile sink moves randomly other than the following predesigned trajectories, during which it only broadcasts its location messages by limited hops instead of the whole network. The nodes getting these messages are called watchers who can obtain the upstream or downstream relations and infer the hop(s) from them to the mobile sink, and then a watcher stores this information as a "clue" to the location of mobile sink for data forwarding. As the movement of the mobile sink, more and more nodes are becoming watchers, and so a sensing data can be efficiently forwarded to the mobile sink according to these clues. Numerous simulations are conducted with mobile sinks in network to evaluate the performance of CBDCR, which demonstrate that CBDCR can both reduce the redundant transmission messages significantly and balance the network energy consumption.
Recently, pressure/strain sensor based on triboelectric nanogenerator (TENG) has gained great attentions for monitoring daily health status and robotic research fields, because of their simple device ...structures and self-powered feature. However, higher sensitivity and stretchability are still demanded for satisfying the practical applications. In this work, a self-powered pressure and strain sensor was achieved by utilizing crumpled MXene film as the single-electrode mode TENG. Benefiting from the reconfigurable behavior of the crumpled structure, the MXene TENG showed a maximum areal strain and linear tensile ratio of 2150% and 400%, respectively. Meanwhile, because of the enlarged contact area between triboelectric layers, the output power density of the MXene TENG with micro-crumples was highly improved by 36 times. Therefore, the as prepared sensor exhibited excellent sensitivity of 2.35 V kPa−1 under pressures from 0.3 to 1.0 kPa, which is superior to majority of the reported sensors based on tribo-/piezo- electricity. In addition, the sensor can be easily attached to human joints to capture and collect the complicated movement signals. By combining with Bluetooth transmission technology, wireless real-time monitoring was realized through transmitting human movement signals to a mobile phone App.
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•Micro-crumples were introduced to MXene film to grant it high stretchability.•Output power density of crumpled MXene film based TENG was largely improved by 36 times.•The TENG based pressure sensor shows great sensitivity of 2.35 V kPa−1.•The self-powered sensor was integrated with Bluetooth system to realize wireless human motion detection.
For high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency shifts are time varying during the coherent processing interval (CPI). Thus, the ...conventional range Doppler (RD) ISAR technique does not work properly. By exploiting two-dimensional (2D) sparsity of the target scene, 2D sparse matrix recovery algorithms are applied to achieve super-resolution within a short CPI, during which the Doppler shifts nearly remains constant. Sequential order one negative exponential (SOONE) function is used to measure the sparsity of a 2D signal. A 2D gradient projection (GP) method is developed to solve the SOONE function and thus the 2D-GP-SOONE algorithm is proposed. The algorithm can solve the sparse recovery of 2D signals directly. Then the 2D-GP-SOONE algorithm is used for the dynamic ISAR imaging of maneuvering targets. Theoretical analysis and simulation results show that the proposed method has a lower computational complexity and can achieve the fast recovering of a sparse matrix. Moreover, the proposed method has a better performance in ISAR imaging of maneuvering targets.
•Exploit 2D sparsity of the target scene, sparse matrix recovery method can be used.•We use the SOONE function to measure the sparsity of a 2D signal.•A novel 2D-GP-SOONE algorithm is proposed to achieve the sparse matrix recovery.•Use the 2D-GP-SOONE algorithm to achieve the ISAR imaging of maneuvering targets.
Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent ...component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.
Computation offloading from a mobile device to the edge server is an emerging paradigm to reduce completion latency of intensive computations in mobile-edge computing (MEC). In order to satisfy the ...delay-sensitive computing tasks, offloading time, including task uploading time, task execution time, and results downloading time is adopted as the computational performance metrics for offloading nodes that perform offloaded computing tasks for mobile devices. Therefore, how to minimize the offloading time by selecting an optimal offloading node in MEC is of research importance. This work first investigates a MEC system consisting of mobile devices and heterogeneous edge severs that support various radio access technologies. Then, based on the available bandwidth of heterogeneous edge severs and the location of mobile devices, an optimal offloading node selection strategy is formulated as a Markov decision process (MDP), and solved by employing the value iteration algorithm (VIA). Finally, extensive numerical results demonstrate the effectiveness of the proposed strategy over classic strategies in terms of offloading time.
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Overuse or misuse of antibiotics and their residues in the environment results in the emergence and prevalence of drug-resistant bacteria and leads to serious health problems. Notable ...progress in liposome research has been made in drug delivery and several liposomal drugs have been approved for clinical use owing to its biocompatibility and improved efficacy. Recently, liposomes have been engineered further to release encapsulated drugs on the target of interest in a dose-controlled fashion in response to external stimuli such as light, pH, and heat. Among those, light-activated liposomal drug delivery gained a lot of attention because drug release at the targeted sites can be precisely controlled by varying laser/light duration, energy and beam area. We envision potential applications of the light-activated liposomal delivery systems for effective drug-resistant antimicrobial therapies. The use of light-activated liposomes will be widely spread in antimicrobial therapies if the amount of drug is precisely controlled for a prolonged time at a target location. In this review, we discussed the breadth and depth of various light-activated liposomal drug delivery technology. Emphasis was given to repetitive release mechanism and applications of light-activated liposomes because the repeatability provides stability and precise control of the drug delivery system to prevent overdose of antimicrobials and treat with minimal doses. We described limitations on translation from pre-clinical to clinical settings and strategies to overcome the limitations. Careful consideration of light-responsive materials, lipid composition, laser parameters and laser safety is important when selecting and designing the drug delivery system for successful applications.
Estimates of brain FNs derived from fMRI data play a crucial role in studying brain function. Both static and dynamic FN estimation methods have been proposed.In static FN analyses, hypothesis-driven ...methods rely on prior knowledge, whereas data-driven methods are constrained by model assumption.Dynamic FN analyses encompass window-based and windowless methods, both of which encounter challenges in effective state extraction.Future work will be necessary to enhance the reproducibility and replicability of FN analyses, and to elucidate the shared and unique capabilities of the various methods.
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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