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Abstract Owing to the intrinsic hydrophilicity of nanocellulose, films and nanopapers prepared from cellulosic nanomaterials exhibit weak mechanical strength when exposed to high-moisture conditions. In this study, an approach for designing a water resistant, assembled nanopaper through controlled and irreversible aqueous complexation of oppositely charged cellulose nanoconstituents, i.e., cationic cellulose nanocrystals (AH-CNC) and anionic cellulose nanofibers (TO-CNF), is proposed. The fabrication process and features of the nanopaper can be adjusted by altering of the AH-CNC/TO-CNF ratio. For example, the draining time during the filtration of a nanopaper decreased dramatically (480–10 min) when the dosage of nanocelluloses resulted in charge compensation. This dosage also reduced the swelling of the nanopaper. After all charged groups were neutralized, a nanopaper with a wet strength of 11 ± 3 MPa was obtained when immersed in water for 24 h. Furthermore, the electrostatic interaction between the charged nano-entities enhanced the mechanical properties of the nanopaper in dry state (the maximum of tensile strength was 174 ± 3 MPa) and resulted in improved water barrier properties (water vapor transmission rate of 1683 g μm m−2 d−1). This straightforward method based on simply aqueous mixing of two oppositely charged nanomaterials may provide a new pathway for the fabrication of various functionalized films and sheets with advanced characteristics from different type of charged nanoparticles and colloids.
Abstract In this paper, we construct an interactive signage system, which is able to actively talk to users who are passing the signage system and at the same time tries to induce behavioral changes through visual and auditory stimulation. On top of our previous feasibility study, we re-design our proposed system based on the persuasive system design (PSD) model and the behavior change support system (BCSS) theory. The new design consists of 4 parts: recognizing (identifying and classifying users), executing (sending triggers and inducing behavior change), reviewing (recording users’ reaction), and feedback (keeping or improving users’ motivation). To track users’ status and performance, we use the smartphone and the smartwatch to collect users’ bio-data (e.g. heart rate, number of steps) and users’ location. To send user triggers continuously without being interrupted by notifications from other applications, we set the interactive signage in the daily action line of users and present information to users when they pass the signage. In order to make the system more persuasive, we pick up eight features from the 28-feature list of PSD and apply them to our design.
Abstract This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, the road side unit observes the global network state, allocates channels and schedules data packets for all vehicle user equipment-pairs (VUE-pairs). The decision-making procedure is modelled as a discrete-time Markov decision process (MDP). The technical challenges in solving an optimal control policy originate from highly spatial mobility of vehicles and temporal variations in data traffic. To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs. We then propose an online long short-term memory based deep reinforcement learning algorithm to break the curse of high dimensionality in state space faced by each per-VUE-pair MDP. With the proposed algorithm, the optimal channel allocation and packet scheduling decision at each epoch can be made in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.
Abstract Resource sharing in wireless networks has been a hot topic for years. It mainly deals with two main goals: incentive mechanism design to motivate resource owners to contribute resources on the supply side of the market, and resource allocation to efficiently assign the obtained resources to end users on the demand side. Mos existing resource sharing is based on the one-sided model. However, one application of the two-sided model, sharing economy, is reshaping conventional business models with a substantial growth in both market size and profit. We see a great potential to increase profit and efficiency by adopting the two-sided market model in wireless networks, so as to bridge user demand and resource supply simultaneously from both sides of the network. However, as to wireless networks with typical network features, many problems cannot be tackled based upon existing models. In this work, we provide the basic concept of a two-sided market, together with the challenges and applications of using a two-sided market model to tackle the resource sharing problem in various kinds of networks. Potential methodologies to solve resource sharing problems in two-sided markets are also presented sequentially and compared. In the end, future directions for resource sharing under the two-sided market model are discussed.
Light-fueled self-oscillators based on stimuli-responsive soft materials have been explored toward the realization of a myriad of nonequilibrium robotic functions, such as adaptation, autonomous locomotion, and energy conversion. However, the high energy density and unidirectionality of the light field, together with the unscalable design of the existing demonstrations, hinder their further implementation. Herein, a light-responsive lampshade-like smart material assembly as a new self-oscillator model that is unfettered by the abovementioned challenges, is introduced. Liquid crystal elastomer with low phase transition temperature is used as the photomechanical component to provide twisting movement under low-intensity incoherent light field. A spiral lampshade frame ensures an equal amount of light being shadowed as negative feedback to sustain the oscillation upon constant light field from omnidirectional excitation (0°–360° azimuth and 20°–90° zenith). Different-sized oscillators with 6, 15, and 50 mm in diameter are fabricated to prove the possibility of scaling up and down the concept. The results provide a viewpoint on the fast-growing topic of self-oscillation in soft matter and new implications for self-sustained soft robots.
Abstract Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for pervasive cloud computing, i.e., LSTM4PCC, which supports an accurate prediction of cloud resources. LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions. We evaluate LSTM4PCC and compare it with the ARIMA (AutoRegressive Integrated Moving Average) approach in terms of prediction accuracy. The results show that LSTM4PCC performs better than ARIMA.
Abstract A deep eutectic solvent (DES) derived from ferric chloride hexahydrate and betaine chloride (molar ratio of 1:1) was used as hydrolytic media for production of chitin nanocrystals (ChNCs) with a high yield (up to 88.5%). The synergistic effect of Lewis acid and released Brønsted acid from betaine hydrochloride enabled the efficient hydrolysis of chitin for production of ChNCs coupled with ultrasonication with low energy consumption. The obtained ChNCs were with an average diameter of 10 nm and length of 268 nm, and a crystallinity of 89.2% with optimal synthesis conditions (at 100 °C for 1 h with chitin-to-DES mass ratio of 1:20). The ChNCs were further investigated as efficient emulsion stabilizers, and they resulted in stable o/w emulsions even at a high oil content of 50% with a low ChNC dosage of 1 mg/g. Therefore, a potential approach based on a DES on the production of chitin-based nanoparticles as emulsifiers is introduced.
Abstract Five different acidic deep eutectic solvents (DESs) composed of choline chloride and organic acids were applied to fabricate chitin nanocrystals (ChNCs). All DESs resulted in high transmittance and stable ChNCs suspensions with very high mass yield ranging from 78 % to 87.5 % under proper reaction conditions. The acidic DESs had a dual role in ChNCs fabrication, i.e. they promoted hydrolysis of chitin and acted as an acylation reagent. Physicochemical characterization of chitin revealed that the removal of amorphous area during DES treatments led to increased crystallinity of ChNCs and a dimension diversity correlated the DES used. The average diameter and length of individual ChNCs ranged from 42 nm to 49 nm and from 257 nm to 670 nm, respectively. The thermal stability of ChNCs was comparable to that of pristine chitin. Thus, acidic DESs showed to be non-toxic and environmentally benign solvents for production of functionalized chitin nanocrystals.
Abstract Electricity Retailers offer various utility plans in the hope that the increased competition would result in lower prices, improved service, and innovative product offerings. In this paper, we present the retail electric provider’s (REP) optimal pricing strategy for residential customers in smart grid, in which the REP offers multiple utility plans for customers with different needs, which includes a flat-rate plan, a multi-stage plan, and a lump-sum fee plan. The residential customers select the utility plan that maximize their own payoffs by considering their own demands and the pricing strategies of the three plans. In the other way around, the REP optimizes its profit by carefully designing its pricing strategy based on residential customers’ decisions. To obtain insights of such a highly coupled system, we consider a system with one REP and a group of customers in need of electricity. We propose a three-stage Stackelberg game model, in which the REP acts as the leader who decides the specific plans to offer at Stage I, then announces the price for each plan in stage II, and finally the customers act as followers that select plans in stage III. We derive the market equilibrium by analyzing customers’ decisions among the plans under different pricing schemes. Then, we provide the RP’s optimal pricing strategies to maximize its profit. In the end, we give the optimal decisions for REP on the specific plan(s) to offer while considering each customer’s evaluation and demand. Both the analytical and simulation results show that the lump-sum fee plan can maximize RP’s profit in most cases.
Abstract Vehicle-to-everything (V2X) communications can be applied in emergency material scheduling due to their performance in collecting and transmitting disaster-related data in real time. The urgency of disaster depots can be judged based on the disaster area video, and the scenario coefficient can be evaluated for building a fairness model. This paper presents a scenario-based approach for emergency material scheduling (SEMS) using V2X communications. We propose a SEMS model, with the objectives of minimum time and maximum fairness in the cases of multiple supply depots, disaster depots, commodities and transport modes for logistics management of relief commodities. We design the SEMS algorithm based on the artificial fish-swarm algorithm to obtain an optimized solution. The results demonstrate that the SEMS model can enhance the fairness of relief scheduling, especially for disaster depots with small demands compared to the Gini and enhanced Theil fairness models. Moreover, the acquired vehicle speed via V2X communications updates the SEMS model in real time, which approaches a solution closer to reality.
Abstract Transforming potential waste materials into high-value-added sustainable materials with advanced properties is one of the key targets of the emerging green circular economy. Natural mica (muscovite) is abundant in the mining industry, which is commonly regarded as a byproduct and gangue mineral flowing to waste rock and mine tailings. Similarly, chitin is the second-most abundant biomass resource on Earth after cellulose, extracted as a byproduct from the exoskeleton of crustaceans, fungal mycelia, and mushroom wastes. In this study, exfoliated mica nanosheets were individualized using a mechanochemical process and incorporated into regenerated chitin matrix through an alkali dissolution system (KOH/urea) to result in a multifunctional, hybrid hydrogel, and film design. The hydrogels displayed a hierarchical and open nanoporous structure comprising an enhanced, load-bearing double-cross-linked polymeric chitin network strengthened by mica nanosheets possessing high stiffness after high-temperature curing, while the hybrid films (HFs) exhibited favorable UV-shielding properties, optical transparency, and dielectric properties. These hybrid designs derived from industrial residues pave the way toward sustainable applications for many future purposes, such as wearable devices and tissue engineering/drug delivery.
Abstract In the space physics community, processing and combining observational and modeling data from various sources is a demanding task because they often have different formats and use different coordinate systems. The Python package GeospaceLAB has been developed to provide a unified, standardized framework to process data. The package is composed of six core modules, including DataHub as the data manager, Visualization for generating publication quality figures, Express for higher-level interfaces of DataHub and Visualization, SpaceCoordinateSystem for coordinate system transformations, Toolbox for various utilities, and Configuration for preferences. The core modules form a standardized framework for downloading, storing, post-processing and visualizing data in space physics. The object-oriented design makes the core modules of GeospaceLAB easy to modify and extend. So far, GeospaceLAB can process more than twenty kinds of data products from nine databases, and the number will increase in the future. The data sources include, e.g., measurements by EISCAT incoherent scatter radars, DMSP, SWARM, and Grace satellites, OMNI solar wind data, and GITM simulations. In addition, the package provides an interface for the users to add their own data products. Hence, researchers can easily collect, combine, and view multiple kinds of data for their work using GeospaceLAB. Combining data from different sources will lead to a better understanding of the physics of the studied phenomena and may lead to new discoveries. GeospaceLAB is an open source software, which is hosted on GitHub. We welcome everyone in the community to contribute to its future development.
Abstract We propose DeepDefrag, a model-assisted reinforcement learning for spatio-temporal defragmentation of time-varying virtual networks in a cross-layer optical network testbed, which realizes the efficient utilization of computing nodes and lightpaths by co-optimizing scheduling and embedding with fragment matching, reduces >13.5% cost of computing power network.
Abstract In this study, we demonstrate a new bio-derived and non-toxic deep eutectic solvent composed of betaine hydrochloride (Bh) and glycerol (Gl) as a pretreatment medium for birch cellulose (Betula pendula) to prepare cellulose nanofibers (CNFs) using microfluidization. The co-solvent could readily penetrate into cellulose to swell the fibrillar structure and weaken the interaction within the hydrogen bond network. Moreover, the cationization of glycerol and cellulose by betaine hydrochloride further enhances the swelling process. All of these effects promote the nanofibrillation of cellulose and reduce the energy demand in CNF production. A high CNF mass yield of up to 72.5 % was obtained through co-solvent pretreatment using a Bh-to-Gl mole ratio of 1:2 at 150 °C for 1 h. The mole amount of betaine hydrochloride was noted to affect the nanofibrillation process and stability of the CNF suspension. The obtained CNFs possessed a cationic charge of 0.05–0.06 mmol/g, a diameter of 17–20 nm, and a degree of crystallinity of 67.7–74.4 %. The CNFs displayed good thermal stability comparable to that of the pristine cellulose. Thus, this study provides a green and efficient swelling strategy for producing CNFs with a low cationic charge density.
Abstract Zirconia is an inorganic, nonmetallic material with excellent properties. However, the brittleness of the zirconia, resulting from the thermal performance during the heating and cooling process, seriously limits the application of zirconia in the metallurgical, military, and aerospace industries. Al2O3 doped ZrO2 was developed to improve the potential material’s toughness. This paper studied the evolution of the surface functional groups, phase composition, toughening mechanism, and particle morphology of Al2O3 doped ZrO2 during the heating process. Especially microwave heating was selected as the heating method during the experiments to save energy consumption. The results showed that the phase transition temperature was reduced by the microwave sintering technique, which also promoted the transformation between the m-ZrO2 and t-ZrO2, advancing the crystallinity and structural properties of the samples. The specific surface area shows a positive relationship with the microwave heating temperature, while the particle size of the powder decreased with the temperature increase. The optimized sintering effect appears at 1000 °C in the studied roasting temperature range (800 °C–1200 °C) for Al2O3–ZrO2 powders. With the optimized sintering temperature, the void of the granular zirconia material was controlled, and the best micromorphology was obtained. In practical production, the application of microwave sintering and alumina doping is beneficial to saving costs and protecting the environment. Al2O3–ZrO2
Abstract In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.
Abstract For Industrial Internet with TDM-PON, we propose a time-aware deterministic bandwidth allocation (TA-DBA) scheme that allocates proper transmission windows based on flow arrival time and cycle. Simulation results show that TA-DBA can achieve deterministic transmission, and the average bandwidth efficiency is 20.4% higher than FBA.
Abstract A cubic rock salt structured ceramic, Li7Ti3O9F, was fabricated via the conventional solid-state reaction route. The synthesis conditions, sintering characteristics, and microwave dielectric properties of Li7Ti3O9F ceramics were investigated by X-ray diffraction (XRD), thermal dilatometer, Scanning Electron Microscopy (SEM) accompanied with EDS mapping, and microwave resonant measurements. Rietveld refinement, selected area electron diffraction (SAED) pattern and high-resolution transmission electron microscopy (HRTEM) confirmed that Li7Ti3O9F adopts a cubic rock-salt structure. The ceramic sintered at 950 °C presented the optimal microwave properties of εr = 22.5, Q×f = 88,200 GHz, and τf = −24.2 ppm/°C. Moreover, good chemical compatibility with Ag was verified through cofiring at 950 °C for 2 h. These results confirm a large potential for Li7Ti3O9F ceramic to be utilized as substrates in the low temperature cofired ceramic (LTCC) technology. This work provides the possibility to exploit low-temperature-firing ceramics through solid solution between oxides and fluorides.
Abstract With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.
Abstract The RTK/ERK signaling pathway has been implicated in prostate cancer progression. However, the genetic relevance of this pathway to aggressive prostate cancer at the SNP level remains undefined. Here we performed a SNP and gene-based association analysis of the RTK/ERK pathway with aggressive prostate cancer in a cohort comprising 956 aggressive and 347 non-aggressive cases. We identified several loci including rs3217869/CCND2 within the pathway shown to be significantly associated with aggressive prostate cancer. Our functional analysis revealed a statistically significant relationship between rs3217869 risk genotype and decreased CCND2 expression levels in a collection of 119 prostate cancer patient samples. Reduced expression of CCND2 promoted cell proliferation and its overexpression inhibited cell growth of prostate cancer. Strikingly, CCND2 downregulation was consistently observed in the advanced prostate cancer in 18 available clinical data sets with a total amount of 1,095 prostate samples. Furthermore, the lower expression levels of CCND2 markedly correlated with prostate tumor progression to high Gleason score and elevated PSA levels, and served as an independent predictor of biochemical relapse and overall survival in a large cohort of prostate cancer patients. Together, we have identified an association of genetic variants and genes in the RTK/ERK pathway with prostate cancer aggressiveness, and highlighted the potential importance of CCND2 in prostate cancer susceptibility and tumor progression to metastasis.
Abstract This study investigated the longitudinal associations of physical activity and circulating amino acids concentration in peripubertal girls. Three hundred ninety-six Finnish girls participated in the longitudinal study from childhood (mean age 11.2 years) to early adulthood (mean age 18.2 years). Circulating amino acids were assessed by nuclear magnetic resonance spectroscopy. LTPA was assessed by self-administered questionnaire. We found that isoleucine, leucine and tyrosine levels were significantly higher in individuals with lower LTPA than their peers at age 11 (p < 0.05 for all), independent of BMI. In addition, isoleucine and leucine levels increased significantly (~15%) from childhood to early adulthood among the individuals with consistently low LTPA (p < 0.05 for both), while among the individuals with consistently high LTPA the level of these amino acids remained virtually unchanged. In conclusion, high level of physical activity is associated lower serum isoleucine and leucine in peripubertal girls, independent of BMI, which may serve as a mechanistic link between high level of physical activity in childhood and its health benefits later in life. Further studies in peripubertal boys are needed to assess whether associations between physical activity and circulating amino acids in children adolescents are sex-specific.
Abstract Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose two end-to-end video transformer based architectures, namely PhysFormer and PhysFormer++, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. To better exploit the temporal contextual and periodic rPPG clues, we also extend the PhysFormer to the two-pathway SlowFast based PhysFormer++ with temporal difference periodic and cross-attention transformers. Furthermore, we propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and PhysFormer++ and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. Unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer family can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community.
Abstract How to allocate the limited wireless resource in dense radio access networks (RANs) remains challenging. By leveraging a software-defined control plane, the independent base stations (BSs) are virtualized as a centralized network controller (CNC). Such virtualization decouples the CNC from the wireless service providers (WSPs). We investigate a virtualized RAN, where the CNC auctions channels at the beginning of scheduling slots to the mobile terminals (MTs) based on bids from their subscribing WSPs. Each WSP aims at maximizing the expected long-term payoff from bidding channels to satisfy the MTs for transmitting packets. We formulate the problem as a stochastic game, where the channel auction and packet scheduling decisions of a WSP depend on the state of network and the control policies of its competitors. To approach the equilibrium solution, an abstract stochastic game is proposed with bounded regret. The decision making process of each WSP is modeled as a Markov decision process (MDP). To address the signalling overhead and computational complexity issues, we decompose the MDP into a series of single-agent MDPs with reduced state spaces, and derive an online localized algorithm to learn the state value functions. Our results show significant performance improvements in terms of per-MT average utility.
Abstract To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.
Abstract To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.
Abstract A new metallurgical process via aeration for the decomposition of vanadium slag in concentrated NaOH solution was proposed. The improvement of oxygen mass transfer coefficient when using aeration at different NaOH concentration was studied and the effects of critical reaction parameters on vanadium extraction were systematically investigated. The optimal condition was determined to be: alkali concentration of 60%, reaction temperature of 130 °C, alkali-to-ore mass ratio of 6:1, stirring speed of 500 rpm. The yield of vanadium could reach to 97.41% after reacting for 6 h under this reaction condition. The reaction temperature in this new method is 50–270 °C lower than the current liquid oxidation methods reported in the literatures, and the medium alkaline concentration declined from 85% to 60%, exhibiting significant advantages in energy consumption as well as reactor design. Kinetics study indicated that the extraction of vanadium was governed by internal diffusion, and the apparent activation energy was calculated to be 17.57 kJ/mol.
Abstract This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.
Abstract As a close relative to graphene, silicene is advanced in high lithium capacity, yet attracting various manipulation strategies to promote its role in energy storage. Following grain boundary (GB) engineering route as widely used in graphene studies, in this work, first‐principles calculations were performed to investigate adsorption and diffusion behaviors of lithium on silicene with GBs of 4|8 or 5|5|8 defects. In both GB forms, donation of the Li 2s electron to the GBs significantly increases the Li adsorption energy, whereas small energy barriers facilitate the Li migration on the silicene surface. Furthermore, the large hole of GB(4‐8) also permits easy penetration of the Li ions through the defective silicene sieve. These important features demonstrate GBs are beneficial for enhancing capacity and charge speed of the Li batteries. Thus, superior anodes made of silicene with GBs are expected to serve a key solution for future energy storages.
Abstract The rigidity of traditional solid-state surface-enhanced Raman spectroscopy (SERS) substrate hampers their application in the curved structure for nonplanar surface test and in-situ detection. Traditionally, the flexible Raman substrates are often prepared by transferring printing of patterned nanoparticles on the flexible materials such as polymer, paper, etc. However, the replicate patterns are often produced by high-cost instruments. In this study, a low-cost and flexible SERS substrate is prepared by using a microcontact printing technology to transfer three-phase-assembled nanoparticles on a polydimethylsiloxane film, which can stabilize the assembled nanoparticles. Combining with the endonuclease Nt.BbvCI assisted amplification method, a SERS biosensor is constructed for microRNA 21 (miRNA 21) assay. This platform presents a wide dynamic range (100 fM ∼1 nM), achieving a fabulous sensitivity with limit of detection of 11.96 fM for miRNA 21. Furthermore, after being bent 90° for 50 times, the Raman intensity of the flexible substrate shows a negligible change. This versatile flexible substrate exhibits considerable potential for SERS analysis, which also opens a new avenue for preparing flexible devices.
Abstract We propose a suspect fault screening assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks, which is validated in both simulated topology and testbed. Results show that it achieves satisfactory accuracy under different percentage of OPMs and the number of service requests.
Abstract In optical networks, failure localization is essential to stable operation and service restoration. Several approaches have been presented to achieve accurate failure localization of nodes and inter-nodes. However, due to increasing traffic and demand for flexibility, the reconfigurable optical add/drop multiplexer (ROADM) is evolving towards a multi-degree architecture. Therefore, each ROADM is composed of multiple devices, which makes intra-node failures become more complex. In this context, intra-node failure localization can effectively reduce the pressure on network operators to further find specific devices. In this work, we redefine the intra-/inter-node failure model for multi-degree ROADM-based optical networks and propose a suspect fault screen assisted graph aggregation network (SFS-GRN) for intra-/inter-node failure localization. The SFS is responsible for screening out suspect fault devices from all devices and reducing the number of candidate devices. The GRN is used to analyze these monitoring data from an optical performance monitoring (OPM) node and network wide and to determine the most likely failure device. The proposed scheme is evaluated in a nine-node simulated network and three-node testbed network. Extensive results show that the SFS-GRN achieves higher accuracy compared with existing methods under different percentages of OPM deployment, numbers of service requests, and failure types. The SFS can remove more than 98% of devices, which is beneficial to further detection and repair for network operators. Moreover, the proposed strategy takes about 10 ms to detect a potential failure, and it has the potential to be applied to a real scenario.
Abstract This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.
Abstract Multiple laser shock processing (LSP) impacts on microstructures and mechanical properties were investigated through morphological determinations and hardness testing. Microscopic results show that without equal channel angular pressing (ECAP), the LSP-treated lamellar pearlite was transferred to irregular ferrite matrix and incompletely broken cementite particles. With ECAP, LSP leads to refinements of the equiaxed ferrite grain in ultrafine-grained microduplex structure from 400 to 150 nm, and the completely spheroidized cementite particles from 150 to 100 nm. Consequentially, enhancements of mechanical properties were found in strength, microhardness and elongations of samples consisting of lamellar pearlite and ultrafine-grained microduplex structure. After LSP, a mixture of quasi-cleavage and ductile fracture was formed, different from the typical quasi-cleavage fracture from the original lamellar pearlite and the ductile fracture of the microduplex structure.
Abstract Chronic prostatitis (CP) is a complex disease. Fragmentary evidence suggests that factors such as infection and autoimmunity might be associated with CP. To further elucidate potential risk factors, the current study utilized the Fangchenggang Area Male Health and Examination Survey (FAMHES) project; where 22 inflammatory/immune markers, hormone markers, tumor-related proteins, and nutrition-related variables were investigated. We also performed baseline, regression, discriminant, and receiver operating characteristic (ROC) analyses. According to NIH-Chronic Prostatitis Symptom Index (NIH-CPSI), participants were divided into chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS, pain ≥ 4; divided into IIIa and IIIb sub-groups) and non-CPPS (pain = 0; divided into IV and normal sub-groups). Analyses revealed osteocalcin as a consistent protective factor for CP/CPPS, NIH-IIIb, and NIH-IV prostatitis. Further discriminant analysis revealed that ferritin (p = 0.002) and prostate-specific antigen (PSA) (p = 0.010) were significantly associated with NIH-IIIa and NIH-IV prostatitis, respectively. Moreover, ROC analysis suggested that ferritin was the most valuable independent predictor of NIH-IIIa prostatitis (AUC = 0.639, 95% CI = 0.534–0.745, p = 0.006). Together, our study revealed inflammatory/immune markers [immunoglobulin E, Complement (C3, C4), C-reactive protein, anti-streptolysin, and rheumatoid factors], hormone markers (osteocalcin, testosterone, follicle-stimulating hormone, and insulin), tumor-related proteins (carcinoembryonic and PSA), and a nutrition-related variable (ferritin) were significantly associated with CP or one of its subtypes.