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There have been many studies focusing on edge server deployment and service placement in mobile edge computing (MEC) respectively, but rare works took both of them into consideration. However, edge server deployment and service placement are coupling issues in practice, where the former affects the latter. Besides, the economic benefit of the MEC platform is also a consideration. Due to different service request rates and prices, appropriate service placement solutions are needed to increase the overall profit. In this paper, we propose a complete process combining edge server and service placement, where service placement explicitly takes into account the structure of current edge server placement and different service request rates and prices. We design a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity and the computing capacity of each edge server. We propose a two-step method including the clustering algorithm and nonlinear programming to solve the formulated problem. Extensive evaluations based on the real-world dataset demonstrate that the proposed algorithm outperforms the baseline methods.

Reverse auction-based incentive mechanisms have been widely adopted to stimulate mobile workers to participate in mobile crowdsensing (MCS), where workers need to provide location information for winner selection. However, most existing mechanisms rely on trusted platforms. The worker’s location data uploaded to platforms thus can be easily exposed. Recent works start to incorporate location privacy in designing incentive mechanisms, but they have not considered the task’s location privacy and the worker’s location privacy simultaneously. Therefore, we propose a bilateral location privacy-preserving mechanism for MCS on untrusted platforms. In our model, each worker adopts differential privacy to obfuscate his location locally and then submits the obfuscated location together with the bid information. Besides, instead of the exact location, the task requester is only required to upload the task profile and the task’s obfuscated location. Then, we propose the lowest-cost winner selection mechanism which aims to minimize the social cost of winner selection under the location constraint while ensuring task quality requirements, and adopt the critical payment determination mechanism to determine the payments for the winners, which satisfies truthfulness, individual rationality, and computational efficiency. Theoretical analysis and extensive experiments on real-world datasets show the effectiveness of the proposed mechanisms.

Currently, Mobile Edge Computing (MEC) becomes a burgeoning paradigm to tackle the contradiction between delay-sensitive tasks and resource-limited mobile/IoT devices. However, a single MEC server is usually not able to satisfy the heavy computation tasks considering its limited storage and computation capability. Thus, the cooperation of MEC servers provides an effective way to accommodate this issue. In this paper, we study the joint task offloading and resource allocation problem in the scenario with cooperative MEC servers. We first define resource fairness among IoT devices from the user experience perspective. Then we formulate a joint optimization problem by taking into account the system efficiency and fairness, which is shown to be NP-hard and thus intractable. To solve this problem, we propose a two-level algorithm: The upper-level algorithm, inspired by evolutionary strategies, is able to search superior offloading schemes globally; While the lower-level algorithm, taking into account fairness among all tasks, is able to generate resource allocation schemes that make full use of server resources. Comprehensive evaluation results demonstrate the efficiency and fairness of the proposed algorithm compared to baselines.

Acenapththylene-imide (AnI), similar to naphthalene diimide (NDI), is an outstanding building block for organic functional materials and has gained a lot of research attention. Herein, Sulphur and Selenium-embedded AnI-containing polycyclic aromatic hydrocarbon molecules, AnI-SQ and AnI-SeQ, with [1,2,5]thiadiazolo[3,4-g]quinoxaline (SQ) and [1,2,5]selenadiazolo[3,4-g]quinoxaline (SeQ) are designed and synthesized with low-lying LUMO energy levels. The absorption and emission of AnI-SQ and AnI-SeQ displayed a bathochromic shift upon protonation of the C = N bond. Besides, theoretical calculation indicates remarkable rigid planar backbones for both AnI-SQ and AnI-SeQ. Through self-assembly with polymeric Pluronic® F-127, corresponding hydrophilic nanoparticles (NPs) were prepared with low cytotoxicity. And AnI-SQ NPs could be applied for in vitro two-photon fluorescence imaging.

Current OER electrocatalysts are hardly applicable for industrial use, which demands high current density (≥ 1000 mA cm-2) at low overpotential (≤ 300 mV) with long-term stability (≥ 100 h). Herein self-supported heterojunction catalyst, NiCo-OH@NixFeyO4 on Fe foam (FF), is in situ synthesized using two-step corrosion engineering. It only requires an overpotential 275 mV to drive the current density of 1000 mA cm-2 with good long-term stability. Theoretical calculations reveal that such good performance is attributable to electron transfer from NiCo-OH to NixFeyO4 which weakens the adsorption energy of reaction intermediate (OOH*) to promote the release of O2 and lowers the free energy barriers for the reaction. Furthermore, a water splitting cell with NiCo-OH@NixFeyO4/FF as anode and CoP@FeP/FF as cathode demonstrates its potential for industrial application. The study presents a general strategy for in situ synthesis of heterojunction catalysts on metal foams using controlled corrosion engineering for various catalytic applications. We would like to thank financial support by The International Postdoctoral Exchange Fellowship Program (No. PC2021035).

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data credibility, simultaneously. Moreover, traditional privacy and reliability-aware incentive schemes are usually challenging to realize real-time reward distribution. To this end, we first point out a single time slice of failure problem in real-time incentive mechanisms and propose a two-layer truth discovery model (TLTD) to resolve this problem. Then, a reliability-aware real-time incentive mechanism (RRIM) is designed based on the proposed TLTD. In order to evaluate data reliability in a privacy-preserving manner, we build a privacy-preserving truth discovery solution (PriTD) based on secure computation protocols. Finally, our proposed system (PRICE) integrating the aforementioned protocols realizes real-time reward distribution, data reliability evaluation, and privacy protection, simultaneously. Theoretical analysis and experimental evaluations on a synthetic and real-world dataset demonstrate the feasibility and efficiency of the proposed PRICE.

Background Genome-wide functional genetic studies of food-borne pathogens could provide a comprehensive understanding of the mechanisms underline their survival and pathogenesis in the harsh environments during food processing and infections. Traditional methods such as physical mutagenesis, chemical mutagenesis, and labeled DNA fragment insertion mutation techniques are time-consuming, labor-intensive and low precision. Although RNA-Seq and other omics methods enable high-throughput research on a genome-wide scale, the obtained results of gene transcription or expression cannot establish direct causal connection between the genetic determinants and particular functions. Scope and approach Transposon sequencing (Tn-seq) is an emerging microbial functional genomic technology based on transposon mutagenesis and high-throughput sequencing technologies, which is able to quantitatively track millions of independent mutants in a single experiment, and therefore efficiently map the “genotype-phenotype” relationship and the genetic interaction networks of a bacterium. Key findings and conclusions This article summarizes the common types and hazards of food-borne pathogens, the development of Tn-seq technology, and its applications in studying the physiology, pathogenesis, and environmental survival of food-borne pathogens.

The minimum travel distance of task participants is one of the significant optimization objectives of privacy-preserving task assignment in mobile crowdsensing (MCS). However, when the travel distance is minimized, most of the previous schemes only focus on the task participant privacy and disregard the task requester privacy. Moreover, existing solutions usually only support the constraint of a single type, such as equality constraints or range constraints. In this paper, we propose a bilateral privacy-preserving Task Assignment mechanism for MCS (iTAM), which protects not only the task participants privacy but also the task requesters privacy and can minimize the travel distance. Furthermore, iTAM provides both equality and range constraints of task assignment by utilizing the Paillier cryptosystem. To accommodate the multiple relations between the task participants and the task, we propose the single/multiple task participants selection problems for a task requiring task participants to compete and cooperate. Experimental evaluations over synthetic and real-world data illustrate that iTAM is feasible and effective. Compared with the state-of-the-art, iTAM positively solves the optimal problem of travel distance. The complexities of iTAM are $\mathcal {O}(n)$ O ( n ) and $\mathcal {O}(n\log n)$ O ( n log n ) for a single and multiple task participants selection problems, respectively.

Task assignment is a critical issue in mobile crowdsensing (MCS), an emerging sensing paradigm applied to realize various sensing applications for smart cities. Existing task assignment schemes mostly require the exact location information of tasks and workers for optimization, which inevitably brings the issue of location privacy leakage. Therefore, researchers have started investigating privacy-preserving task assignment schemes. However, most of these works either make inaccurate assignments or only concentrate on workers’ privacy. In this article, we propose a novel bilateral privacy-preserving and accurate task assignment framework in fog-assisted MCS, called BRAKE. Specifically, we utilize the multisecret sharing scheme to preserve location privacy in the MCS task assignment, where tasks and workers only need to provide the secret shares of their real location information to fog nodes. Moreover, we consider distance-oriented and time-oriented tasks for assignment optimization and propose an adaptive top-k worker selection algorithm to accurately select the most suitable workers. The security analysis proves that BRAKE can resist collusion attacks, and the extensive evaluation results demonstrate the efficiency and accuracy of BRAKE.

Listeria monocytogenes (Lm) is a foodborne pathogen that can cause severe human illness. Standard control measures for restricting bacterial growth, such as refrigeration, are often inadequate as Lm grows well at low temperatures. To identify genes involved in growth at low temperatures, a powerful functional genomics method Tn-seq was performed in this study. This genome-wide screening comprehensively identified the known and novel genetic determinants involved in low-temperature growth. A novel gene

MicroRNAs (miRNAs) are vital post-transcriptional regulators that participate in host-pathogen interactions by modulating the expression of cellular factors. Previous studies have demonstrated that feline panleukopenia virus (FPV) alters miRNA expression levels within host cells. However, the relationship between FPV replication and host miRNAs remains unclear. Here, we demonstrated that FPV infection significantly altered cellular miR-92a-1-5p expression in F81 cells by upregulating the expression of specificity protein 1 (SP1). Furthermore, we observed that miR-92a-1-5p enhanced interferon (IFN-α/β) expression by targeting the suppressors of cytokine signaling 5 (SOCS5) that negatively regulates NF-κB signaling and inhibits FPV replication in host cells. These findings revealed that miR-92a-1-5p plays a crucial role in host defense against FPV infection.

Mobile crowd sensing (MCS) is an emerging sensing paradigm that can be applied to build various smart city and IoT applications. In an MCS application, the participation level of mobile users plays an essential role. Thus a great many incentive mechanisms have been proposed to motivate users. However, most of these works focus on the bidding behavior of users and overlook the feature of task requesters. Specifically, there exists a disparity between the low payment a requester would like to make and the high reward a user would like to receive. In this work, we address this issue by designing a group-buying-based online incentive mechanism, which contains two stages: In Stage I, a price learning algorithm is designed to select winning tasks for each group of sensing tasks and obtain a competitive total budget for recruiting users. In Stage II, an online auction is conducted between group agents and online users before a given recruitment deadline. Through theoretical analysis and extensive evaluations, we show that the proposed mechanisms possess computational efficiency, individual rationality, budget balance, truthfulness, and good performance.

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to motivate the participation of task participants. However, a monetary incentive mechanism is generally challenging to prevent malicious task participants and a dishonest task requester. Moreover, prior quality-aware incentive schemes are usually failed to preserve the privacy of task participants. Meanwhile, most existing privacy-preserving incentive schemes ignore the data quality of task participants. To tackle these issues, we propose a privacy-preserving and data quality-aware incentive scheme, called PACE. In particular, data quality consists of the reliability and deviation of data. Specifically, we first propose a zero-knowledge model of data reliability estimation that can protect data privacy while assessing data reliability. Then, we quantify the data quality based on the deviation between reliable data and the ground truth. Finally, we distribute monetary rewards to task participants according to their data quality. To demonstrate the effectiveness and efficiency of PACE, we evaluate it in a real-world dataset. The evaluation and analysis results show that PACE can prevent malicious behaviors of task participants and a task requester, and achieves both privacy-preserving and data quality measurement of task participants.

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method.

The gear transmission system is widely used in high-speed centrifugal pump to improve the operating speed and hydraulic performances of the whole pump. Vibration characteristics and the stability of these high-speed rotor systems with gear transmission have great impacts on the stability of the whole fluid transmission system of the plant. Based on the lumped-mass method and the principle of displacement equilibrium of the rotor system, a coupled lateral-torsional dynamic model describing the gear-rotor-seal-bearing (GRSB) system of high-speed centrifugal pumps which has considered the nonlinear factors within the gear pair, nonlinear forces of bearings, and those of the seals is proposed. Then, the stability and nonlinear vibration responses of a model GRSB system under different gear transmission ratios (i) have been studied. The following conclusions are drawn from the results: (1) The components with frequencies like fp, f g , fm, and 2fm have great impacts on the vibration responses of the gear pair, especially the fm component; moreover, the amplitude of fm first increases and then decreases with the ratio increase and reaches the maximum value under the ratio of 3. (2) A jump motion state will occur when the ratio i is 1.25 and the stability of the system is obviously worse than the bifurcation state. Quite different from those under the other states, under this jump motion state, the 0.2 f g component and 0.5fp component will appear in the vibration responses of both gears and become the most contributed two factors to the responses of the driven gear. (3) In the design process, the transmission ratio of a high-speed centrifugal pump with a simplified GRSB system should be specially designed to avoid the jump-point state and the maximum-amplitude-of-fm state to ensure the stability of the system as well as reduce the mechanical impacts and noises.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

The magnetic anomalies generated by the ferromagnetic targets are usually buried within uncontrollable interference sources, such as the power frequency and random noises. In particular, the variability of the geomagnetic field and the low signal-to-noise ratio (SNR) of the magnetic anomalies cannot be avoided. In this paper, to improve the performance of magnetic anomaly detection (MAD) with a low SNR, we propose a novel structured low-rank (SLR) decomposition-based MAD method. In addition, a new framework based on the SLR and singular value decomposition (SVD) is constructed, dubbed SLR-SVD, and the corresponding working principle and implemented strategy are elaborated. Through comparing the SLR-SVD with two state-of-the-art methods, including principal component analysis and SVD, the results demonstrate that the proposed SLR-SVD can not only suppress the noise sufficiently, i.e., improving 55.26% approximately of the SNR, but also retain more boundary information of magnetic anomalies, i.e., decreasing approximately 68.05% of the mean squared error and improving approximately 28.47% of the structural similarity index.

With the rapid development of wireless networks and mobile devices, mobile crowdsensing (MCS) has enabled many smart city applications, which are key components in the Internet of Things. In an MCS system, the sufficient participation of mobile workers plays a significant role in the quality of sensing services. Therefore, researchers have studied various incentive mechanisms to motivate mobile workers in the literature. The existing works mostly focus on optimizing one objective function when selecting workers. However, some sensing tasks are associated with more than one objective inherently. This motivates us to investigate biobjective incentive mechanisms in this article. Specifically, we consider the scenario where the MCS system selects workers by optimizing the completion reliability and spatial diversity of sensing tasks. We first formulate the incentive model with two optimization goals and then design two online incentive mechanisms based on the reverse auction. We prove that the proposed mechanisms possess desirable properties, including computational efficiency, individual rationality, budget feasibility, truthfulness, and constant competitiveness. The experimental results indicate that the proposed incentive mechanisms can effectively optimize the two objectives simultaneously.

In mobile edge computing (MEC), it is challenging to offload tasks to appropriate edge nodes due to the heterogeneity in both tasks and edge nodes. Most existing task offloading mechanisms mainly aim at optimizing the global system performance, e.g., social welfare, while ignoring the personal preferences of the individual tasks and edge nodes. However, in an open MEC system, a task offloading decision is prone to be unstable if edge nodes or task owners have incentives to deviate from the decided allocation, and seek for alternative choices to improve their own utilities. In addition, to win the competition, task owners may gradually adjust their payments, which brings new challenge in achieving the stability of the system. To address the above issues, this paper constructs a distributed many-to-many matching model to capture the interaction between mobile tasks and edge nodes, with the consideration of their diverse resource requirements and availabilities. Based on this, we design both distributed and centralized stable matching based algorithms to jointly offload the tasks to edge nodes, and determine their payments. We prove that the proposed mechanisms achieve several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed schemes can get optimal social welfare, when the considered tasks are homogeneous in terms of their resource requirements. Finally, we conduct simulations to validate the effectiveness of the proposed work.

In mobile crowd sensing, task allocation is of vital importance, and it has attracted much attention in recent years. Though there have been many studies focusing on task allocation, rare works took sensing duration of tasks into consideration. However, sensing duration plays a key role for the success of many sensing tasks. For example, when the crowd sensing system needs to monitor the crowd flow in locations of interest, it is better to allocate this task to workers who can record a video of certain duration rather than those who can only take a picture. In this article, we try to solve this problem by designing a duration-sensitive task allocation model, where each task is associated with a specific sensing duration. The model aims at maximizing the number of completed tasks under the constraints of sensing duration and task capacity of each worker. To find an efficient task allocation scheme for the model, we design a utility function that can reflect the probability of task completion by using the exponential distribution. Then, an efficient greedy heuristic is proposed based on the utility function. Extensive evaluations based on the simulated and real-world datasets demonstrate that the proposed algorithm outperforms the baseline methods.

Photodynamic therapy (PDT) is a clinically implemented modality to tackle intractable diseases, including cancer. Nevertheless, its efficacy is largely compromised by some resistance factors from t...

A reliable mobile crowdsensing (MCS) application usually relies on sufficient participants and trustworthy data. However, privacy concerns reduce participants’ willingness to participate in sensing tasks. The uncertainty of participant behavior and heterogeneity of sensing devices result in the unreliability of sensing data and further bring unreliable MCS services. Hence, it is crucial to estimate the reliability of sensing data and protect privacy. Unfortunately, most existing privacy-preserving data estimation solutions are designed for single-type data. In practice, however, heterogeneous sensing data are ubiquitous in data integration tasks. To this end, we propose a privacy-preserving reliability estimation solution of heterogeneous data for MCS, called IronM, which is effective for text, number, and multimedia data (e.g., image, audio, and video). Specifically, IronM first formulates the reliability assessment of text, number, and multimedia data as equality and range constraints, and then estimates the reliability of heterogeneous data through our proposed privacy-preserving hybrid constraints assessment mechanism. Privacy analysis demonstrates that IronM can not only evaluate the reliability of heterogeneous data but also protect data confidentiality. The experimental results in real-world datasets show the effectiveness and efficiency of IronM.

With the advancement of smartphones, mobile crowdsourcing has become a new computing paradigm to efficiently support novel mobile applications. Achieving good service quality of these applications, however, necessitates the participation of large number of smartphones, which can be obtained via providing suitable incentives to smartphone users. Nonetheless, most existing incentive mechanisms assume a centralized platform for recruiting smartphones, which is prone to expose the privacy of both smartphones and task requesters. In this context, this article studies a distributed truthful incentive mechanism (DTIM) for mobile crowdsourcing, where multiple auction rounds can be conducted locally in each smartphone and task requester. Specifically, in each auction round, the participating smartphones act as the sellers and submit bids to compete for their intended crowdsourcing tasks. The task requesters, on the other hand, act as the buyers that decide on the sellers and corresponding payments, depending on their submitted bids. Finally, based on the offered payment, each smartphone selects a buyer for trading to optimize its utility. It is shown that the proposed incentive mechanism is strategy-proof, budget balanced, individually rational, and computationally efficient. Numerical results provided corroborate the beneficial properties of the scheme.

Colorectal cancer (CRC) is a common malignancy with high morbidity and mortality worldwide. To date, chemotherapy plays an important role in the treatment of CRC patients. Multidrug resistance (MDR) is one of the major hurdles in chemotherapy for CRC, and the underlying mechanisms need to be explored. Studies have demonstrated that Wnt/β-catenin signaling plays a critical role in oncogenesis and tumor development, and its function in inhibiting apoptosis could facilitate tumor chemoresistance. Recent investigations have also suggested the regulatory effects of the Wnt/β-catenin signaling pathway in response to chemotherapeutic agents in CRC. Here, we particularly focus on reviewing the evidences suggesting the mechanisms of Wnt/β-catenin signaling in the chemoresistance modulation of colorectal cancer.

The building of a mature material library is critically important for the practical application of any smart responsive optoelectronic materials. For thermochromic luminophores, as an important category of smart materials, however, it remains challenging to achieve this target due to the complexity and limitations of existing construction mechanisms. Here, we propose a simple and efficient strategy for developing a thermochromic luminophore library by exploiting the synergistic effects between peripheral flexible chain-related molecular dynamics and molecular aggregation structure-related luminescence properties of donor–acceptor–donor type luminophores. The strategy provided herein facilitates the construction of a prototype thermochromic material library of 13 dyes with a luminescence color-switching window covering the whole visible region and a wide temperature-response range from 35 °C to 190 °C. Based on this material library and a nano ink preparation method, reversible information displays and twelfth-level data encryptions based on temperature changes are demonstrated.

Users can invoke various composite services in Edge Computing (EC) with the development of Service Computing. Generally, users cannot invoke the optimal composite service due to the migration of component service or failure in the edge device. In this study, a Composite Service Provisioning mechanism in EC (CSP-EC) is proposed. It starts when the location of component service changes and terminates when the optimal composite service is obtained. CSP-EC employs dynamic optimization process to meet the real-time requirements. It first caches m optimized intermediate solutions from the first m users, which can be reused by other users to get the final optimal solution. Moreover, a multipopulation Estimation of Distribution Algorithm is used to reduce the probability of falling into a local optimum, and the roulette mechanism is used to accelerate the optimization process. Extensive simulations are conducted based on the real-world dataset. The results show that the proposed mechanism achieves higher quality, better stability, and shorter execution time compared with other schemes.

Objective. To investigate hypoxia-induced Nestin regulates lung cancer viability and metabolism by targeting transcription factors Nrf2, STAT3, and SOX2. Methods. Eighty-four cases of nonsmall cell lung cancer (nonsmall cell lung cancer, NSCLC), which had been treated from June 2020 to February 2021, were randomly selected from our clinicopathology database. Immunohistochemical staining of collected tissue cells was performed to assess the expression patterns of Nestin, STAT3, Nrf2, and SOX2. Data were quantified and statistically analyzed using one-way and two-way ANOVA tests with P < 0.05 . Results. Clinicopathological findings showed significant differences in lymph node metastasis, tissue differentiation, and histology on induction of Nestin expression; Nestin expression correlated with STAT3, Nrf2, and SOX2 expression.Nestin/STAT3/SOX2/Nrf2 are involved in angiogenesis and lung cancer development. Conclusion. Hypoxia-induced Nestin promotes the progression of nonsmall lung cancer cells by targeting the downstream transcription factors STAT3, Nrf2, and SOX2.

Facial appearance transfer (FAT) is a critical component of various facial editing tasks. It aims to transfer the facial appearance of a reference into a target with good visual consistency. When there are considerable visual differences between a reference and a target, however, it may introduce visual artifacts into the results. To tackle this problem, we propose a facial appearance map with illumination-aware and region-aware properties that allows seamless FAT. We formulate the appearance-map generation as label propagation (LP) on a similarity graph, and propose a new regularization structure to facilitate the adaptive appearance-map diffusion. Solving the original LP model of appearance map in general requires on the order $O(kn^2)$ time for an $n$ -nodes graph where each node has $k$ neighbors. It may be computationally prohibitive for an image with a large spatial resolution. To tackle this problem, we mathematically analyze the graph-based LP model and propose a fast algorithm with smart subset sampling. It selects a subset with $m$ nodes of the graph with $n$ nodes ( $m\ll n$ ) to approximate the solution to the original system, which significantly reduces its computational requirements from $O(kn^2)$ to $O(m^2n)$ . Based on the adaptive LP-based appearance map, we construct a framework to achieve various editing effects with FAT, including face replacement, face dubbing, face swapping, and transfiguring. Comparisons with related methods show the effectiveness of the adaptive LP model for FAT. Qualitative and quantitative evaluations verify the computational improvements of the approximation algorithm.

Online social networks (OSNs) are becoming more and more prevalent in people’s life, but they face the problem of privacy leakage due to the centralized data management mechanism. The emergence of distributed OSNs (DOSNs) can solve this privacy issue, yet they bring inefficiencies in providing the main functionalities, such as access control and data availability. In this article, in view of the above-mentioned challenges encountered in OSNs and DOSNs, we exploit the emerging blockchain technique to design a new DOSN framework that integrates the advantages of both traditional centralized OSNs and DOSNs. By combining smart contracts, we use the blockchain as a trusted server to provide central control services. Meanwhile, we separate the storage services so that users have complete control over their data. In the experiment, we use real-world data sets to verify the effectiveness of the proposed framework.

Mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading schemes. Many existing works focus on offloading tasks to the edge servers while ignoring the heterogeneity and diversity of computation services, which is also important in MEC. In this paper, we investigate the joint problem of online task offloading and service placement—downloading and deploying the service-related resources at edge servers—in the dense MEC network. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Due to the uncertainty of task demands, it is impossible to make an online long-term optimal decision. Therefore, we propose an online algorithm based on the two-timescale Lyapunov optimization without requiring the future information. By making asynchronous decisions on service placement and task offloading, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm is more competitive than benchmarks.

The Wnt/β-catenin signaling pathway is one of the highly conserved signaling pathway widely reported to play essential roles in the development of various tumors and human cancers, thus serving as a potential target for anticancer therapy. However, the specific effects of the related proteins in the Wnt/β-catenin signaling pathway in nasopharyngeal carcinoma (NPC) still remain elusive. Thus, this study was performed to uncover the correlation between the Wnt/β-catenin signaling pathway-related proteins and the clinical characteristics and prognosis of NPC. NPC tissues were revealed to present high expression of β-catenin and v-myc myelocytomatosis viral oncogene homolog (c-MYC) but low expression of Dickkopf-3 (DKK-3). Immunohistochemical staining revealed that DKK-3 was positively linked to but β-catenin and c-MYC were negatively linked to differentiation, tumor-node-metastasis (TNM) stage and lymph node metastasis of patients with NPC. In addition, c-MYC was identified to be positively correlated to DKK-3 in NPC tissues. The positive expression of β-catenin and c-MYC had negative relations with and that of DKK-3 had positive relations with survival rate of patients with NPC, which was analyzed by Kaplan-Meier method. Moreover, it was shown that later TNM stage and positive expression of β-catenin were risk factors for NPC-related death. These findings provide evidence that the proteins related to the Wnt/β-catenin signaling pathway (DKK-3, β-catenin, and c-MYC) participate in the development of NPC and positive expression of DKK-3 and negative expression of β-catenin, and c-MYC can serve as essential prognostic biomarkers, shedding new light on the prognosis and treatment of NPC.

The emerging mobile crowdsensing applications are able to facilitate people's life in various aspects. A key factor to ensure that these applications can provide high-quality service is the sufficient participation of normal smartphone users. Therefore, a lot of effort has been made to design incentive mechanisms to motivate users to participate. Most of these works assume that users are associated with homogeneous costs across the whole sensing area, based on which many utility optimization models are proposed. In this paper, we consider the scenario where smartphone users have heterogeneous costs across the sensing area, i.e., users in different regions have different cost distributions. In this scenario, traditional mechanisms may generate sensing holes and recruit insufficient users in some regions with higher costs, which may lead to unsatisfactory service. To accommodate this issue, we propose two optimization models, which aim at maximizing the user cardinality and the sensing utility function for each region of the whole sensing area, respectively. We then design effective incentive mechanisms, which possess the desirable properties, including computational efficiency, individual rationality, budget feasibility, truthfulness, and good competitiveness. By conducting extensive experiments on the real-world geographical dataset, we demonstrate the effectiveness of the proposed mechanisms in achieving the good quality of service.

The purpose of the paper is to compare hardenability and aging precipitation behaviour of three 7xxx aluminium alloys (7150, 7055 and 7056). The hardenability has been studied by Jominy test. The a...