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Fog-aided internet of drones (IoD), where massive training data are collected by drones and analyzed in the fog node, can leverage machine learning to provision various services. Aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. Federated learning (FL) is hence proposed to preserve drone data privacy by performing local training in drones and sharing training model parameters in the fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. In this paper, we investigate the power control of all drones to maximize the FL system security rate constrained by drone battery capacities and the quality of service (QoS) requirement (i.e., FL training time). We formulate this problem as a non-linear programming problem and design an algorithm to obtain the optimum solutions with a low computational complexity. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

Internet of Drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. The performance of IoD is greatly affected by drones’ battery capacities. We hence utilize the energy harvesting technology to charge the batteries and the wireless power control to adjust the drone wireless transmission power in order to address this challenge. In our work, we investigate the joint optimization of power control and energy harvesting control to determine each drone’s transmission power and the transmitted energy from the charging station in time-varying IoD networks. Our objective is to minimize the long-term average system energy cost constrained by the drones’ battery capacities and quality of service (QoS) requirements. A Markov Decision Process (MDP) is formulated to characterize the power and energy harvesting control process in time-varying IoD networks. A modified actor-critic reinforcement learning algorithm is then proposed to tackle our problem and its performance is demonstrated via extensive simulations.

Machine learning models have been built in fog nodes in fog-aided Internet-of-Things (IoT) networks to provision future events prediction and image classification by training data collected from IoT devices. However, sending massive data from all devices to a fog node incurs huge network traffic in wireless links in between. Federated learning is proposed to address the challenge by training models locally in IoT devices and only sharing model parameters in the fog node. In this article, we investigate both the CPU frequency control and wireless transmission power control of all IoT devices to balance the tradeoff between the device energy consumption and federated learning time (consisting of both the computation and communication latencies) in fog-aided IoT networks. We formulate the joint optimization of CPU and power control as a nonlinear programming (NLP) problem with the objective to minimize the energy consumption of all IoT devices constrained by the federated learning time requirement. An alternative direction algorithm, which alternatively optimizes the CPU frequency and wireless transmission power until convergence, is hence designed to solve this problem and its performance is demonstrated via extensive simulations.

As the Chinese financial market has recently opened to the world, the high initial public offering (IPO) underpricing rate in the stock market has generated significant concern. This study investig...

Fog-aided Internet of Drones (IoD) networks employ fog nodes to provide computing resources for the delay-sensitive tasks offloaded from drones. In IoD networks, drones are launched to complete a journey in which several locations of interest are visited. At each location, a drone collects the ground information, generates computing tasks and offloads them to the fog nodes for processing. In our work, we consider both the task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone's flying speed) to minimize the drone‘s journey completion time constrained by the drone's battery capacity and task completion deadlines. We formulate this joint optimization problem as a mixed integer non-linear programming (MINLP) problem. In consideration of the practical scenario that the future task information is difficult to obtain, we design an online algorithm to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The performances of our proposed online algorithm are demonstrated via extensive simulations.

Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things. Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on hypertext induced topic search improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.

Background: Oral tongue squamous cell carcinoma (OTSCC) is the most common malignant tumor of the oral cavity. The aim of this study was to use text mining and data analysis to discover some existing drugs that target genes and to explore potential therapeutic drugs for OTSCC. Methods: We used the text mining tool pubmed2ensembl to extract genes associated with OTSCC, and two datasets (GSE30784, GSE23558) from Gene Expression Omnibus (GEO) were used for the data analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the intersection of the three gene sets. Protein-protein interaction (PPI) network was constructed by STRING, gene module analysis was performed using the Molecular Complex Detection (MCODE), a plug-in in Cytoscape. Lastly, a database of drug-gene interactions was used to identify significant genes to explore potential drugs for the treatment of OTSCC.Results: We produced 403 unique genes associated with oral tongue squamous cell carcinoma through text mining. GSE23558 and GSE 30784 obtained 1637 and 1159 differentially expressed genes (DEGs) through data analysis, respectively. A total of 28 genes were obtained from the intersection gene sets, including 20 up-regulated genes and 8 down-regulated genes. We screened the most significant modules by using MCODE, among which 8 genes were associated with oral tongue squamous cell carcinoma as core genes. Eventually, nine drugs were found to target eight genes.Conclusions: In this study, PLAU, SERPINE1, MMP1, MMP3, MMP10, CXCL10, CXCL12, and SPP1 were potentially key genes involved in the treatment of OTSCC. Furthermore, 12 drugs were identified as potential therapeutic agents for oral tongue squamous cell carcinoma treatment and management

Valued for its toughness and durability, Kaihua paper has been famous in China since the Qing Dynasty and is in the focus of the study on paper life extension. Understanding of the complex degradation behavior of handmade paper under various aging conditions is an essential precondition for preparing contemporary long-life handmade paper. However, reactions sequence and quantitative analysis of microstructure of cellulose and its relation to macroscopic deterioration of handmade paper still are difficult to interpret. Herein, different types of Kaihua handmade papers, produced by various raw materials and crafts, were artificially aged to study the different evolution of their multi-scale structures in dry-heat (DH) and wet-heat (WH) conditions systematically. Two-dimensional correlation spectroscopy (2D-COS) distinguished possible carbonyl vibrations involving hydrolysis and oxidation of cellulose and gave sequential changes of various carbonyl groups, illustrating different evolution behaviors of molecular structure of papers in DH and WH aging process. The energy and distance of hydrogen bond, crystal size and microfiber accessibility were quantitatively calculated during the degradation of cellulose. The dual roles of water molecules, as plasticizers for fibers and as carriers of protons, continuously promoting the hydrolysis of cellulose at molecular and supramolecular scales were elucidated further. In addition, correlations between microstructural evolution and macroscopic deterioration of handmade papers involving reduced mechanical properties and yellowing were revealed.

To provide a better quality of service (QoS), cloud computing paradigm in Internet of Things (IoT) networks has shifted toward the edge. Fog-aided IoT networks deploy fog nodes, which are equipped with computing and storage resources, at the network edge to take over the deadline-driven computing tasks from IoT devices. In the fog node, where multiple virtual machines (VMs) can be rented to process the tasks, fog provisioning is to determine which VM should be rented and how to distribute different tasks to VMs in order to minimize the system cost (i.e., VM rentals). On the other hand, VMs may fail and lead to QoS degradation. Hence, reliability of VMs should also be considered when addressing the fog resource provisioning problem. To improve reliability, more VMs should be rented to satisfy the QoS requirement; this leads to higher system cost. Therefore, there is a tradeoff between reliability and the system cost. In this paper, we investigate the tradeoff of maximizing the reliability and minimizing the system cost for fog resource provisioning in IoT networks. An integer linear programming (ILP) problem is formulated but suffers from a high computational complexity. We then design an alternative algorithm to achieve suboptimal solutions with better time efficiency. The simulation results demonstrate the performances of our proposed algorithm.

Internet of Drones (IoD) utilizes drones as the Internet of Things devices to collect information (e.g., air pollutant level and traffic condition) over different points of interests, and has been explored in several applications such as object tracking and traffic surveillance. However, owing to the limited battery capacity of drones, energy efficient strategies are required in IoD systems. Meanwhile, the varying wireless channel conditions as a drone flies in the air may degrade the user quality of service (QoS). Power control, which adapts to the varying channel conditions and also controls the energy consumption, helps address these challenges. In this paper, we investigate power control in IoD for the data collection service to minimize the energy consumption of a drone while meeting the QoS requirement. The problem is formulated as a sum-of-ratios fractional programming problem, which is NP-complete. We then propose a PowEr conTROL (PETROL) algorithm to solve this problem and derive its rate of convergence. Extensive simulations demonstrate the performances of PETROL.

Background Hyperhidrosis is caused by sympathetic dysfunction of the central or peripheral nervous system. However, intramedullary spinal cord tumors presenting with hyperhidrosis as an initial symptom have been rarely reported in the literature. Case Description This case involves an 18-year-old man who presented with abnormal enhanced sweating and flushing on the bilateral side of his face and neck that had persisted for 6 years. Magnetic resonance (MR) images revealed that at the C7-T2 levels of the spinal cord, a large intramedullary tumor was involved in the cervicothoracic region. The patient underwent gross total resection of the tumor via the fluorescein-guided technique and intraoperative neurophysiologic monitoring. The histopathologic diagnosis revealed ganglioglioma. The symptoms gradually improved after surgery, and the patient presented with virtually complete remission at the end of an 18-month follow-up. Conclusions Few cases of intramedullary spinal cord tumors presenting as hyperhidrosis in clinical manifestation have been reported in the literature. Sympathetic irritation by the tumor, particularly in the location around the gray matter of the lateral spinal cord, may account for the hyperhidrosis as the initial symptom in this patient. Therefore, if a patient has autonomic dysfunction, the spine cord should be additionally examined using MR imaging.

The Internet of Things (IoT) sensing service enables users to monitor the ambient environment by fetching data from IoT sensors. The explosive growth of mobile users and IoT applications injects massive traffic to the IoT network and also speeds up the drainage of sensor batteries. Caching at the IoT gateway (GW), which stores the IoT data and directly send them to the users, can avoid activating sensors too frequently, hence reducing the traffic in the IoT network as well as the energy consumption of sensors. To overcome the limited energy capacity of sensors, energy transmitters (ETs) are deployed to charge them. Practically, the GW and ETs may be owned by different operators, and the GW operator needs to incentivize ETs to provision the charging service. In this paper, we formulate a Stackelberg game in the cache-enabled energy harvesting aided IoT framework to improve the user quality of service. Caching strategies, incentive strategies, and ET transmission power strategies are jointly optimized to find the Stackelberg equilibrium by our proposed alternative direction approach. Simulation results elicit the benefits of our framework and demonstrate the performances of our proposed algorithm.

Fog-aided Internet of Things (IoT) addresses the resource limitations of IoT devices in terms of computing and energy capacities, and enables computational intensive and delay-sensitive tasks to be offloaded to the fog nodes attached to the IoT gateways. A fog node, utilizing the cloud technologies, can lease and release virtual machines (VMs) in an on-demand fashion. For the power-limited mobile IoT devices (e.g., wearable devices and smart phones), their quality of service may be degraded owing to the varying wireless channel conditions. Power control helps maintain the wireless transmission rate and hence the quality of service (QoS). The QoS (i.e., task completion time) is affected by both the fog processing and wireless transmission; it is thus important to jointly optimize fog resource provisioning (i.e., decisions on the number of VMs to rent) and power control. This paper addresses this joint optimization problem to minimize the system cost (VM rentals) while guaranteeing QoS requirements, formulated as a mixed integer nonlinear programming problem. An approximation algorithm is then proposed to solve the problem. Simulation results demonstrate the performance of our proposed algorithm.

International audience; Trust plays a crucial role throughout the entire negotiation process, and culture adds more complexity to the meaning, functions, and dynamics of trust in negotiations. We take a modest step to provide some insights on trust and culture in the context of negotiations and envision what opportunities are ahead of us in this area. Specifically, we provide a “cognitive map” based on the collective wisdom in the extant negotiation literature and focus on raising important questions about six key culture‐related issues that warrant future research: (a) the meaning of trust, (b) the effects of trust, (c) trust development, (d) trust and distrust, (e) trust repair, and (f) trust in virtual negotiations.

The Internet of Things (IoT) sensing service allows systems and users to monitor environment states by transmitting the content sensed by a variety of sensors. Owing to billions of sensors and devices deployed in the IoT system, a huge amount of data (big data) are generated, thus injecting tremendous traffic into the network. Cloud radio access network (C-RAN) is a promising wireless network architecture to accommodate the fast growing IoT traffic and improve the performance of IoT services. Caching in C-RAN, which brings content to the edges, not only alleviates the network traffic, thus improving the end-to-end user quality of service (QoS), but also avoids activating the sensors too frequently, thus reducing their energy consumption. The content placement problem determines what and where to cache in C-RAN. However, the caching performance is highly related to the caching storages. The storage allocation problem determines the storage capacities of network entities. In this paper, we jointly optimize the storage allocation problem and content placement problem in a hierarchical cache-enabled C-RAN architecture for IoT sensing service. We formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network traffic cost. The storage allocation problem and content placement problem are constrained by caching storage budgets and cache capacities, respectively. Two heuristic algorithms are proposed in order to reduce the computational complexity of ILP. Extensive simulations have been conducted to demonstrate that the performances of our proposed algorithms approximate the optimal solutions.

With the widespread adoption of various mobile applications, the amount of traffic in wireless networks is growing at an exponential rate, which exerts a great burden on mobile core networks and backhaul links. Mobile edge caching, which enables mobile edges with cache storages, is a promising solution to alleviate this problem. In this paper, we aim to review the state-of-the-art of mobile edge caching. We first present an overview of mobile edge caching and its advantages. We then discuss the locations where mobile edge caching can be realized in the network. We also analyze different caching criteria and their respective effects on the caching performances. Moreover, we compare several caching schemes and discuss their pros and cons. We further present a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery, and update. For each phase, we identify different issues and review related works in addressing these issues. Finally, we present a number of challenges faced by current mobile edge caching architectures and techniques for further studies.

Cloud radio access network (C-RAN) is a promising wireless network architecture that can reduce the energy consumption by the centralized cloud architecture and subsequently decrease the number of required traditional base station sites and the site support equipments. C-RAN consists of the baseband units (BBUs) and the remote radio heads (RRHs). BBUs are pooled in a central cloud, i.e., the BBU pool is to provide powerful computation and storage resources while RRHs are distributed across multiple sites to provide coverage and interact with user equipments. In order to exploit the benefits of C-RAN, each BBU can be actualized by a virtual machine, i.e., virtual BBU (VB). VBs can be initiated and shut down as needed to serve clusters of RRHs (i.e., many-to-one mapping between RRHs and BBUs). RRHs can be turned into the sleep mode to reduce the energy consumption. In this paper, we jointly optimize BBU-RRH mapping and user association with the objective to minimize the system cost incurred by the energy bill from RRHs and VB rentals under the constraint of user quality of service, which is formulated as an integer linear programming problem. Furthermore, we decompose the joint problem into two subproblems and design a time-efficient algorithm to solve the problem. Simulation results demonstrate that our proposed algorithm performs close to the optimal solutions obtained from CPLEX.

International audience; Teamwork is widely adopted in organisations. Although much evidence indicates that using person‐organisation (P‐O) fit as a selection criterion benefits individual employees, little is known about how this practice influences team functioning. Drawing on the input‐mediator‐outcome model and the research on value congruence, this study built and tested a model that links P‐O fit in recruitment to work teams' performance. Based on data collected from team members, team leaders, human resources managers, and chief executive officers in 96 firms, we found that P‐O fit in recruitment had a positive relationship with team performance and that intrateam trust mediated the relationship between P‐O fit in recruitment and team performance. Further, this mediated relationship existed only when the organisation had a weak, rather than strong, respect‐for‐people culture. This study contributes to the P‐O fit and team literature and has practical implications for human resources practices and team management.

With the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.

Paraquat (PQ) as a widely used herbicide has high toxicity. Therefore, it calls for development of sensitive analytic methods to monitor the residue of PQ in environment. In this work, a photoelectrochemical (PEC) sensing strategy via preparation of the photocathode of ITO/Au/Cu2O is proposed. The introduction of Au film in the PEC system significantly improves the photoelectric property of Cu2O, which may be attributed to the enhanced light utilization and the inhibited self-photoreduction of Cu2O. As expected, under the visible light illumination, the fabricated ITO/Au/Cu2O photocathode shows excellent sensitivity in determination of trace paraquat. The quantitative linear range is from 10-9 to 10-5 mol L-1 and the limit of detection (2.93×10-10 mol L-1) could be reached. For detection of real samples, the ITO/Au/Cu2O photocathode-based PEC sensor demonstrates interference-free, detection reproducibility and acceptable recoveries. This study highlights the heterostructure advantage of metal-semiconductor for promoting the PEC sensing application.

The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculatingKnearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

We investigated the risk factors for diffuse midline gliomas of the spinal cord (DMGSCs). Seventy patients with spinal cord gliomas in two hospitals were analyzed retrospectively. Sixty-nine patients that underwent surgery achieved partial or gross total removal. The patients were subdivided into some groups, based on age, WHO grade, tumor location within the cord, tumor size, and molecular profile: immunohistochemical expression of p53 and ATRX, and mutational status of Histone 3 (H3), and BRAF. Thirty-three patients had an H3 K27M mutation (47%). Some clinical characteristics were significantly different between H3 K27M mutant and H3 wild-type tumors. The main risk factors for DMGSCs were male sex, glioblastomas, and ≤ 2 spinal cord segments. The median survival period of patients with H3 K27M mutant tumors was significantly shorter than those with H3 wild-type tumors (17.0 ± 3.7 months vs censored, P 0.0001). In the DMGSC subgroup, patients with thoracic cord tumors had a significantly better prognosis than those with cervical cord tumors (31.0 ± 6.0 vs 10.0 ± 4.8 months). Patients 45 years of age survived significantly longer than patients 19 years (P = 0.001). In conclusion, H3 K27M mutation significantly predicts a worse outcome of spinal cord gliomas. Anatomical location and age are the main risk factors for DMGSCs.

Introduction and objectives: CASC9 and miR-424-5p are closely related with hepatocellular carcinoma (HCC) progression. This study aimed to evaluate the effect of CASC9 involved with miR-424-5p on the development of HCC. Materials and methods: qRT-PCR was performed to determine the mRNA expressions of CASC9 and miR-424-5p in HCC tissues/cells and adjacent normal tissues/human hepatic epithelial cells, and to analyze the relationship of CASC9 with the clinico-pathological characteristics and prognosis of HCC patients. Then, cell proliferation was measured by CCK-8 and11 LncRNAs, long non-coding RNAs; HCC, hepatocellular carcinoma; AFP, alpha-fetoprotein; CASC9, cancer sensitivity 9; ESCC, esophageal squamous cell carcinoma; qRT-PCR, quantitative reverse transcription polymerase chain reaction; ATCC, American Type Culture Collection; DMEM, Dulbecco's Modified Eagle Medium; CCK-8, cell counting kit-8; RIPA, radio-immunoprecipitation assay; SDS-PAGE, sodium dodecylsulphate polyacrylamide gel electrophoresis; PVDF, polyvinylidene fluoride; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; siNC, small interfering RNA of negative control; M: miR-424-5p mimic; IC, inhibitor control; I, miR-424-5p inhibitor. clone formation assays. Apoptosis of HCC cells was measured by flow cytometry. Besides, cell migration and invasion were determined by scratch wound-healing and Transwell assays, respectively. DIANA-LncBase V2 and dual luciferase reporter gene assay were used to verify the targeted relationship between CASC9 and miR-424-5p. Bcl-2, Bax and cleaved caspase-3 expressions were detected by Western blot. Results: Higher expression of CASC9 was observed in HCC tissues/ cells than in adjacent normal tissues/ human hepatic epithelial cells, and was closely linked to poor prognosis of HCC, tumor size, TNM stage, differentiation degree, lymph node metastasis and alpha-fetoprotein (AFP). Down-regulation of CASC9 decreased the proliferation, invasion and migration of HCC cells while enhancing apoptosis. Besides, CASC9 was negatively correlated with miR-424-5p. MiR-424-5p inhibitor enhanced cell proliferation, invasion and migration while decreasing apoptosis. Interestingly, siRNA-CASC9 partially offset the effects of miR-424-5p inhibitor on HCC cells. Conclusion: CASC9 promoted proliferation, invasion and migration and inhibited apoptosis in HCC cells by inhibiting miR-424-5p.

Internet of Drones (IoD) employs drones as the internet of things (IoT) devices to provision applications such as traffic surveillance and object tracking. Data collection service is a typical application where multiple drones are deployed to collect information from the ground and send them to the IoT gateway for further processing. The performance of IoD networks is constrained by drones' battery capacities, and hence we utilize both energy harvesting technologies and power control to address this limitation. Specifically, we optimize drones' wireless transmission power at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost. We then formulate a Markov Decision Process (MDP) model to characterize the power control process in dynamic IoD networks, which is then solved by our proposed model-free deep actor-critic reinforcement learning algorithm. The performance of our algorithm is demonstrated via extensive simulations.

Internet of drones (IoD), employing drones as the internet of things (IoT) devices, brings flexibility to IoT networks and has been used to provision several applications (e.g., object tracking and traffic surveillance). The explosive growth of users and IoD applications injects massive traffic into IoD networks, hence causing congestions and reducing the quality of service (QoS). In order to improve the QoS, caching at IoD gateways is a promising solution which stores popular IoD data and sends them directly to the users instead of activating drones to transmit the data; this reduces the traffic in IoD networks. In order to fully utilize the storage-limited caches, appropriate content placement decisions should be made to determine which data should be cached. On the other hand, appropriate drone association strategies, which determine the serving IoD gateway for each drone, help distribute the network traffic properly and hence improve the QoS. In our work, we consider a joint optimization of drone association and content placement problem aimed at maximizing the average data transfer rate. This problem is formulated as an integer linear programming (ILP) problem. We then design the Drone Association and Content Placement (DACP) algorithm to solve this problem with low computational complexity. Extensive simulations demonstrate the performance of DACP.

In the past, China, like other developing countries in the world, pursued the goal of rapid economic development at the expense of ecology and ignored the issue of environmental protection. But in recent decades, as environmental problems have become increasingly prominent, developing countries have begun to explore ways to coordinate economy and ecological environment. As the largest developing country, China has been actively exploring ecological governance plans, putting forward the concept of green development, setting the goal of building a "beautiful China" and placing the construction of ecological civilization in the ontological status of social systems and national goals. In order to accelerate the green development process of enterprises in developing countries, based on the actual situation in China, this paper constructs a tripartite evolutionary game model of environmental accounting information disclosure with enterprises, investors and media as the research objects, and analyzes the internal mechanism of environmental accounting information disclosure. The model finds that the equilibrium of the three parties is affected by multiple factors. Therefore, this study further uses system dynamics to explore the dynamic process of evolutionary games and the strategic choices among multiple agents, and explore the mechanism of three types of agents to promote environmental accounting information disclosure. The simulation results reveal that government incentives have a greater impact on guiding enterprises and the media to evolve in the direction of legal disclosure and participation in exposure strategies. In addition, the continuous reduction of the cumbersome degree of disclosure procedures and the difficulty of improving environmental performance can fundamentally promote companies to actively choose legal disclosure strategies, which will help improve the quality of environmental accounting information disclosure, boost investor confidence, and enhance government supervision.

PurposeThe purpose of this paper is to explore the determinants of family business owners’ intrafamily succession intention based on the theory of planned behavior and neo-institutional theory.Design/methodology/approachNational survey data were collected from Chinese private firms in 2010, and a sample of 804 family firms was used to test the hypotheses.FindingsAt the micro level, familism, intrafamily succession regulation and family control have positive effects on owners’ intrafamily succession intention. At the macro level, district succession orientation, which is the district prevalence of intrafamily succession practice, has a positive effect on owners’ intrafamily succession intention. Additionally, the district succession orientation weakens the positive effects of intrafamily succession regulation and family control.Originality/valueThe paper contributes to the understanding of family business owners’ intrafamily succession intention from both micro and macro perspectives. Besides, it also contributes to the integration of micro and macro research by examining the interaction effects.

Background Community-based diabetes management is known to be an important strategy for global diabetes control. In China, community-based diabetes management care, including regular blood glucose tests and guidance on medicine use, dietary control, and physical exercise provided by primary health institutions (PHIs), as one of the key contents of the national essential public health services (EPHS), was implemented since 2009 when the new round of health system reform was initiated. This study aimed to investigate the utilization of community-based diabetes management care services, and explore the factors influencing utilization from both patients’ and providers’ points of view. Methods In total, 2520 type-2 diabetes mellitus (DM) patients registered for EPHS were selected from 63 PHIs in eight counties of Shandong province, China, using multi-stage stratified sampling. Of those, 2166 patients (response rate: 85.4%) completed face-to-face structured questionnaires on their utilization of community-based diabetes management care services. Further, 63 PHIs were surveyed on diabetes care delivery, and 444 primary healthcare providers were purposively sampled from those PHIs to measure their knowledge of diabetes management care delivery, using a self-developed questionnaire. Descriptive statistics were used to analyze the delivery and utilization of diabetes management care services. Multilevel logistic regression models were used to analyze the factors associated with patients’ utilization of diabetes management services. Results All 63 PHIs reported that all the required four diabetes management services were provided through EPHS. However, only 49.6% of the patients reported they fully used these services, with no statistically significant difference between urban and rural patients. Patients who had higher knowledge of diabetes and better self-efficacy in controlling the condition, were more likely to fully utilize diabetes management care. A larger number of PHI health staff per 1000 population was associated with better utilization of care. Conclusion Although community-based diabetes management services are well available to Chinese DM patients under the framework of EPHS, the actual utilization of diabetes management services among the patients was poor. The size of the PHI workforce, patients’ knowledge and self-efficacy in controlling diabetes, were important predictors of utilization, and could be enhanced to improve control of diabetes.

In recent years, with the advent of the third wave of artificial intelligence, a large number of new technologies have been applied in social software and information services, such as big data collection and analysis, pattern recognition and user behavior prediction. The subsequent impact of mobile new media on a new generation of young people and the entire society has become one of the most promising and research issues. This paper obtains relevant data through online and offline surveys and authoritative organizations, uses Cronbach alpha coefficient to verify the validity of the survey results. The Spearman Rank correlation coefficient is calculated to analyze the relationship between the grade level of college students, the degree of influence on mobile new media and the duration of using mobile new media. Based on BP neural network and Elman neural network, the two models are combined by the optimal weighting method to predict the arrival rate of mobile new media information, and the conclusion based on quantitative analysis of mathematical model is given. The results show that the arrival rate of new media information on different platforms is on the rise in the future.

It is meaningful to use data mining technology to predict the type of lawsuit which a company may receive so that enterprises can avoid lawsuit risks. So we propose a corporation lawsuit prediction algorithm based on guiding learning and collaborative filtering recommendation. Firstly, we use the adaptive synthetic sampling approach (ADASYN) to generate more synthetic data for different minority classes according to their different level of difficulty in learning, so that the training would focus on these minority classes that are difficulty to learn and reduce the learning bias introduced by the imbalance of data distribution. Secondly, for the sake of solving the problem that the insufficient samples make it difficult for the model to learn enough knowledge resulting in a large fluctuation of final scores during the training and poor model stability, we use guiding learning to integrate the basic knowledge of all types of lawsuit a company may receive in the future obtained by the multi-label classification model into the training process of TOP-1 and TOP-2 predictive models. Finally, in order to further improve the prediction accuracy, we use the collaborative filtering recommendation algorithm (CFRA) to select the most similar sample with each test sample from the training set, and the lawsuit type of the selected sample is directly used as the predicted lawsuit type of the corresponding test sample, thereby improving the total prediction accuracy. The experimental results show that the proposed algorithm can effectively predict the most probable lawsuit types of the Top2 for corporations.

Fog-aided Internet of Things (IoT) networks provide low latency IoT services by offloading computational intensive and delay sensitive tasks to the fog nodes, which are deployed close to the IoT devices. Mobile IoT relies on battery limited mobile IoT devices (e.g., wearable devices and smartphones) to provision networks with enhanced flexibility. Mobile IoT faces the challenges of varying wireless channel conditions and hence may degrade the quality of service (QoS). We investigate the task allocation, which intelligently distributes tasks to different fog nodes and adapts to IoT varying mobile environment, such that the average task completion latency, constrained by QoS requirements and mobile IoT device battery capacity, is minimized. An integer linear programming (ILP) problem is then formulated to solve this problem. However, it is difficult to obtain the user mobility patterns (i.e., future locations where tasks are offloaded) and user side information (i.e., task length and computing intensity). Therefore, we propose an online learning algorithm to engineer task allocation decisions and then demonstrate its performances by extensive simulations.

Immunotherapy might be an effective treatment in extrahepatic cholangiocarcinoma (eCCA), a tumor with extremely limited therapeutic options. Our study is to characterize the programmed death ligand-1 (PD-L1) protein expression and cancer microenvironment profiles in surgically resected eCCA samples. PD-L1 positivity was observed on tumor cells (32.3%) as well as on tumor-associated macrophages (74.2%). PD-L1 expression by eCCA correlated significantly with immune parameters such as intra-tumoral CD3+ tumor infiltrating lymphocytes (TILs) density (P = 0.002), intra-tumoral CD8+ TILs density (P < 0.001), and the expression pattern of human leukocyte antigen (HLA) class I (P < 0.001). Immunofluorescence showed that PD-L1 positive tumor cells were adjacent to PD-1 positive cells and the stroma covered with interferon-γ. Correlation with clinicopathological parameters and survival analyses revealed that PD-L1 positivity in eCCA was related to the absence of venous invasion (P = 0.030), improved overall survival (P = 0.020) and progressionfree survival (P = 0.011). HLA class I molecules defect, which is an important mechanism of immune evasion, was frequently observed in eCCA (50.0%) and was associated with a decreased number of intra-tumoral CD8+ TIL density (P = 0.028). Additionally, the presence of unusually high numbers of tumor-associated macrophages (TAMs) subsets M2 in most of eCCA (74.2%) was noted. Our study indicated that PD-L1 expression in association with intra-tumoral TILs infiltration and HLA class I expression in 32.3% of the eCCA reflects an active immune microenvironment potentially responsive to PD-1/PD-L1 inhibitors. In addition, the combination of macrophage-targeting agents may provide therapeutic synergy for future immunotherapy.

Rapid growth of Internet of Things (IoT) services with different service level agreement (SLA) requirements has called for offloading computing tasks from the resource constrained IoT devices to the remote cloud. The growing number of IoT devices is exacerbating the links between IoT devices and the cloud. Furthermore, many services (e.g., health and disaster response) require fast service response and immediate data analytics, thus posing a great challenge to the remote cloud. Fog computing provides the solution by bringing computing resources to the edge of the network to assume substantial computing tasks. However, the resource failures during service processing should not be overlooked because they can greatly degrade service performance. In our work, we investigate the fog resource provisioning problem for the deadline-driven IoT services to minimize the system cost considering the probability of resource failures. We formulate the problem as an integer linear programming (ILP) model, and then design a Weighted Best Fit Decreasing (WBFD) algorithm with low computational complexity. Simulation results validate that our proposed algorithm performs close to the optimal solutions of ILP.

Summary What affects the way that trust develops in negotiations? In two studies, we used an actor–partner interdependence model to investigate how both negotiators' trust propensity prior to the negotiation and two types of behavior during the negotiation affect negotiators' trust development. Study 1 demonstrated that both focal negotiators' (actors') and their counterparts' (partners') trust propensity were positively associated with negotiators' trust development. Study 2 showed that actors' and partners' trust propensity had an indirect effect on trust development via both actors' and partners' negotiation behaviors. Negotiators' trust propensity positively affected their use of Q&A (questions and answers about interests) and negatively affected their use of S&O (substantiation about positions and single-issue offers). Actors and partners' negotiation behaviors in turn affected their own and their partners' trust development. Our studies propose and test a model to understand how negotiators' trust propensity and negotiation behaviors affect the development of trust in negotiation. Copyright © 2016 John Wiley & Sons, Ltd.

In order to demonstrate the influence of structural fine-tuning on bandgap and semiconductor performance of π-conjugated polymer materials, three new diketopyrrolopyrrole (DPP) based copolymers P1, P2 and P3 with monomers, i.e., 2-phenylpyridine, 2-(thiophen-2-yl)pyridine and 2-(thiophen-2-yl)thiazole, respectively, have been synthesized using a typical Suzuki cross-coupling polycondensation. The DFT calculations demonstrate that polymer P3 displays high backbone coplanarity in comparison to polymers P1 and P2. Absorption spectra results reveal that P3 exhibits a remarkable red-shifted absorption profile, narrow band gap and broad absorption. The thin film FET measurements show that P3 possesses the highest hole mobility (μh) up to 1.25 cm2 V−1 s−1 and on/off (Ion/off) ratio as high as 108. Those results demonstrate that the fine-tuning of the π-conjugated backbone can significantly affect the band gaps and coplanarities, and ultimately improve the semiconducting performances.

ObjectiveThere is a high prevalence of poor glycaemic control among patients with type 2 diabetes (T2DM) in China. This study aimed to explore both organisational and individual characteristics associated with glycaemic control among patients with T2DM.DesignCross-sectional survey.SettingShandong Province, China.ParticipantsThe participants were 2166 patients with T2DM and 337 healthcare providers from 36 urban communities and 36 rural villages in Shandong Province.Primary and secondary outcome measuresMultistage stratified sampling procedures were used to measure demand-side individual demographic, clinical and self-management characteristics, and supply-side organisational characteristics, and the status of glycaemic control. Multilevel logistic regression analysis was performed to assess key determinants of glycaemic control.ResultsOnly 42.8% of the patients with T2DM achieved good glycaemic control. Age, income, hypertension and self-efficacy were significantly positive predictors of optimal glycaemic control, while duration of diabetes, antidiabetic drugs and monitoring of blood glucose were significantly negative predictors of that. Private VCs (OR=0.48, 95% CI 0.29 to 0.82, pConclusionsGlycaemic control was suboptimal among patients with T2DM in China. The determinants of failing to achieve good glycaemic control included both organisational and individual characteristics. Potential interventions that target patients, providers and the healthcare organisations should be taken to improve the glycaemic control and health outcome among patients with T2DM.

Objective The purpose of this study was to demonstrate the usefulness of conventional magnetic resonance imaging (MRI) in stratification of spinal cord non-ependymal gliomas. Methods Forty-one patients (mean age, 29.9 ± 16.8 years; 27 men) with first-diagnosed spinal cord non-ependymal gliomas underwent conventional MRI including T1-weighted, T2-weighted (T2W) and contrast-enhanced T1-weighted imaging sequences. The tumors were removed by surgery and processed by histology 1 week after MRI. Patients were divided into low-grade and high-grade groups according to histological findings. The MRI features of gliomas including signal-to-noise-ratio (SNR), margins, heterogeneity, edema, enhancement, tumor size, longitudinal location, and presence of syrinx were measured and compared between low-grade and high-grade groups using the χ2 test and the Mann-Whitney U test. Results The SNR of low-grade gliomas on T2W images was significantly higher than that of high-grade gliomas (165.0 ± 108.7 vs. 98.6 ± 44.4; P = 0.016 by the Mann-Whitney U test). The margins of low-grade gliomas were found to be clearer compared to that of high-grade gliomas (54.5% vs. 5.3%; P = 0.001). Receiver-operating-characteristic analysis revealed that the area under the curve of 1/SNR, margin and the combination of 1/SNR and margin was 0.722 (95% confidence interval [CI], 0.564–0.880), 0.736 (95% CI, 0.578–0.894), and 0.845 (95% CI, 0.717–0.973), respectively. No significant differences were found in the heterogeneity, edema, enhancement level, enhancement SNR, size, longitudinal location, cyst, hemorrhage, and intramedullary location between low-grade and high-grade non-ependymal gliomas (all P > 0.05). Conclusions The characteristics of signal intensity and margin on T2W imaging are valuable in stratification of spinal cord non-ependymal gliomas.

BackgroundSelf-management is the cornerstone of diabetes care, however, despite the numerous recommendations available for self-management, type-2 diabetes mellitus (T2DM) patients' performance is suboptimal in China. This study aimed to explore the association between self-efficacy and self-management behaviors among Chinese T2DM patients, which might provide evidence to inform effective self-management interventions for these patients.MethodsA cross-sectional survey was conducted using a multi-stage stratified randomized sampling in Shandong Province, China. The Diabetes Empowerment Scale-Short Form (DES-SF) was used to measure patients' self-efficacy to manage diabetes. Latent class analysis (LCA) was used to explore the observed classes of self-management behaviors (dietary control, physical exercise, regular medication and self-monitoring of blood glucose). A two-class solution for self-management behaviors was tested to be the fittest based on LCA; we labelled active and inactive self-management groups. Univariate and multivariate logistic regression analysis were used to examine the associations between self-efficacy and self-management behaviors.ResultsA total of 2166 T2DM patients were included in the analysis. The mean DES-SF score was 31.9 (standard deviation: 5.2). The estimated proportions of T2DM in the active and inactive groups were 54.8% and 45.2%, respectively. The multivariate logistic regression showed that higher DES-SF score was significantly associated with higher possibility of active self-management behaviors (odds ratio = 1.06; 95% confidence interval: 1.04-1.08).ConclusionsSelf-efficacy in managing diabetes is associated with self-management behaviors among Chinese T2DM patients. To improve self-management behaviors, multiple strategies should be conducted to improve patients' self-efficacy.

Background The link between prostate cancer (PCa) development and aberrant expression of genes located on the Y chromosome remains unclear. Objective To identify Y-chromosomal long noncoding RNAs (lncRNAs) with critical roles in PCa and to clarify the corresponding mechanisms. Design, setting, and participants Aberrantly expressed lncRNAs on the Y chromosome were identified using transcriptome analysis of PCa clinical samples and cell lines. Biological functions and molecular mechanisms of the lncRNAs were revealed using in vitro and in vivo experimental methods. Outcome measurements and statistical analysis Experiments and outcome measurements were performed in duplicate or triplicate. Wilcoxon signed-rank test was employed for comparison of RNA levels in clinical cohorts. Analysis of variance was employed for comparisons among multiple groups. Results and limitations In most patients with PCa, TTTY15 was the most elevated lncRNA located on the Y chromosome. Knockout of this lncRNA by two different CRISPR-Cas9 strategies suppressed PCa cell growth both in vitro and in vivo. TTTY15 promoted PCa by sponging the microRNA let-7, consequently increasing CDK6 and FN1 expression. FOXA1 is an upstream regulatory factor of TTTY15 transcription. Conclusions The Y-chromosomal lncRNA TTTY15 was upregulated in most PCa tissues and could promote PCa progression by sponging let-7. Patient summary We found that TTTY15 levels were frequently elevated in prostate cancer (PCa) tissues compared with those in paracancerous normal tissues in a large group of PCa patients, and we observed a tumour suppressive effect after TTTY15 knockout using CRISPR/Cas9. These results may have therapeutic implications for PCa patients.

Family business owners and researchers tend to overwhelmingly focus on the top-level structure of firms but ignore the middle-level practice – involving family members in the middle-management team. Compared to top managers at the strategic apex, middle-level managers are mainly responsible for internal operations and control, and the composition of the middle-management team has an immediate and direct impact on the overall workforce efficiency of family firms. Integrating agency theory and organizational justice perspective, we proposed that family involvement in middle management would have a negative impact on the labor productivity of family firms. We further corroborated this effect by identifying three boundary conditions at the individual (i.e., familial CEO), organizational (i.e., firm size), and regional (i.e., labor mobility) levels. Using a sample of 1,284 privately owned family firms in China, we found that family involvement in middle management, measured as the percentage of familial middle-level managers, was negatively associated with labor productivity. Furthermore, this negative relationship existed only when the CEO is a family member rather than a professional manager, when the size of the firm is large rather than small, or when the firm is located in regions with low rather than high labor mobility. These findings contribute to family business literature and provide practical implications for human resource management in family firms.

Several resting-state neuroimaging studies have indicated abnormal regional homogeneity (ReHo) in chronic schizophrenia; however, little work has been conducted to investigate naive patients with first-episode schizophrenia (FES). Even less investigated is the association between ReHo measures and clinical symptom severity in naive patients with FES. The current study evaluated ReHo alterations in whole brain, and assessed the correlations between ReHo measures and clinical variables in naive patients with FES. Forty-four naive patients with FES and 26 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Group-level analysis was utilized to analyze the ReHo differences between FES and HC in a voxel-by-voxel manner. Severity of symptoms was evaluated using a five-factor model of the Positive and Negative Syndrome Scale (PANSS). The correlation between the severity of symptoms and ReHo map was examined in patients using voxel-wise correlation analyses within brain areas that showed a significant ReHo alteration in patients compared with controls. Compared with the healthy control group, the FES group showed a significant decrease in ReHo values in the left medial frontal gyrus (MFG), right precentral gyrus, left superior temporal gyrus (STG), left left middle temporal gyrus (MTG), left thalamus, and significant increase in ReHo values in the left MFG, left inferior parietal lobule (IPL), left precuneus, and right lentiform nucleus (LN). In addition, the correlation analysis showed the PANSS total score negatively correlated with ReHo in the right precentral gyrus and positively correlated with ReHo in the left thalamus, the positive factor positively correlated with ReHo in the right thalamus, the disorganized/concrete factor positively correlated with ReHo in left posterior cingulate gyrus (PCG), the excited factor positively correlated with ReHo in the left precuneus, and the depressed factor negatively correlated with ReHo in the right postcentral gyrus and positively correlated with ReHo in the right thalamus. Our results indicate that widespread ReHo abnormalities occurred in an early stage of schizophrenic onset, suggesting a potential neural basis for the pathogenesis and symptomatology of schizophrenia.

Image dehazing has become a significant research area in recent years. However, the traditional dehazing algorithms based on statistics priors cannot adaptive to various conditions of natural hazy images. And those algorithms based on Data-driven learning such as some dehazing networks for estimating transmission almost have the problem that the range of the estimated transmission is too narrow for those haze images where hazy density changes largely. So in this paper, we present a novel Dehazing Network to learn the relationship between the hazy image and its corresponding transmission map. It uses jump connection and the layer of Multi-scale features fusion to obtain more feature related to haze density and use both max pooling and average pooling which in turn remove some details of the transmission map and make the gained transmission map more accurate. Moreover, we also propose a linear stretching algorithm based on dark channel prior to extent the transmission range. The experimental result demonstrate that proposed algorithm achieves favorable result against existing dehazing algorithms on both synthetic images and natural images.

NUT (nuclear protein in testis) midline carcinoma is a rare malignant tumor arising in midline structures, such as head, neck and mediastinum. In the 2015 WHO classification of lung tumours, it was categorized in "Epithelial tumors". However, because of limited clinical data, its biological features have not been fully recognized. We report a case of NUT midline carcinoma presented as a primary lung tumor. Pneumonectomy was performed without any neoadjuvant therapy and it turned out to be a gross total resection. Even so, the prognosis is poor with a disease-free survival of 2 months and an overall survival of 6 months.

Caching popular contents closer to users has been proposed to alleviate wireless network traffic and improve user quality of experience (QoE). Decisions on where and what to cache is of great importance. In this paper, we propose a hierarchical cache-enabled cloud radio access network (C-RAN) architecture where joint caching is considered in both remote radio heads (RRHs) and baseband units (BBUs) with the constraints of backhaul and fronthaul links. We formulate the content placement problem as an integer linear programming (ILP) model with the objective of minimizing the average content download time. A heuristic algorithm is proposed in order to reduce the time complexity. Simulation results of the average download delay are analyzed from different aspects including caching locations, total file lengths, cache sizes and file popularities, and they demonstrate that the performance of the proposed popularity-based algorithm approximates ILP solutions closely but with high time efficiency.

Purpose: Our study was to detect the effect of Decitabine (DAC) combined with arsenic trioxide (AS2O3) on DAPK gene and HL-60 cell proliferation and apoptosis. Methods: DAC and AS2O3 monotherapy, combination treatment and DAC pretreatment were used in this study after incubating with HL-60 cell for 24 h, 48 h, 72 h. CCK8 was used to detect the cell proliferation of HL-60 cell. Flow cytometry was used to detect the cell apoptosis. Then, we used RT-PCR to obtain the gene expression level of DAPK. Results: HL-60 cells were treated with different concentrations of DAC (20 μmol/L, 40 μmol/L, 80 μmol/L), AS2O3 (1 μmol/L, 2.5 μmol/L, 5 μmol/L) monotherapy for 24 h, 48 h, 72 h; along with the extension of the drug concentration and time, proliferation inhibition rate had gradually increased. Monotherapy of DAC, AS2O3 could inhibit the proliferation and induce apoptosis of HL-60 cells, and was time- and dose-dependent. DAC (80 μmol/L) was firstly used for pretreatment, and then, different concentrations of AS2O3 (1 μmol/L, 2.5 μmol/L, 5 μmol/L) were used for 24 h, 48 h, 72 h. It was found that cell proliferation inhibition rate and apoptosis rate had increased significantly. When the two drugs were used together, the increasing proliferation inhibition rate, apoptosis rate and DAPK had become more obvious. Conclusion: DAC and AS2O3 had a synergetic effect for the HL-60 cell proliferation inhibition, apoptosis and expression of DAPK.

Objective To give a comprehensive and basic understanding of diabetes and its risk factors in floating people in China. Method We use "(diabetes or type 2 diabetes or chronic disease) and (floating population or employed floating population)" as the key words to search in the China academic literature database (CNKI), Wan Fang database, PubMed and Web of Science for relevant literature and extract the data about the prevalence of diabetes, relevant risk factors and disease management of the floating population in China. Result Twenty-one literatures are entered into analysis finally, one is English and the rest are Chinese. According to the national survey, the prevalence rate of diabetes in floating population in China was 5. 1% (95%CI, 4.9% - 5.3%), which is lower than that of the general population(11.6%,95%CI, 11.3%–11.8%),and is consistent with the results of the local surveys. The comparison result between the prevalence of floating population and that of local population in each region differs in local surveys. In addition, the prevalence of male floating population is lower than that of the female population. Finally, as the age of the population rises, so does the prevalence of diabetes. As for the risk factors of chronical diseases, the overweight rate in every region is similar but the obesity rate differs in different regions (Ningxia,26.0%;Xiangshan,14.0%), and the obesity rate of the floating population is less than half of that of the general people(4.7%,11.9%). The awareness rate, treatment rate and control rate in the floating patients differ in the regional researches, but they all can’t meet the goals set by the local health departments. Conclusion Compared with the general population, the prevalence of diabetes in the floating population are lower. However, Considering the growing population number and the poor disease management of the floating patients, the potential threat brought by the diabetes in floating population is imponderable. The government should establish the national surveillance system of diabetes for the floating population, strengthen the construction of the primary medical institutions, and optimize the existing funding system.

Dehazed image quality assessment algorithm is aimed to evaluate the quality of dehazed images. However, the existing dehazed image quality evaluation algorithms are overly dependent on the dehazed images database with accurate subjective quality scores, which are inaccurate, biased and time-consuming, and it is difficult to obtain a large dehazed images database, or extent the existing database. To overcome these problems, it is by using the subjective quality preference that we propose a rank learning algorithm to evaluate the dehazed image quality, here, the subjective quality preference stands for the information such as “the quality of Image \(I_a\) is better than that of image \(I_b\)”, and the rank learning is aimed to learn a function that can predict the corresponding rank sequence for a given set of input stimulus. In our algorithm, we transform the problem of dehazed image quality evaluation into the classification problem of quality preference learning and then use and random forest and pairwise comparison in turn to learn the function that can predict the corresponding quality rank sequence for a given set of dehazed images. The experimental results show that our algorithm is highly consistent with the subjective feeling of human eye and is superior to the traditional dehazed image quality evaluation algorithms. Moreover, our algorithm has a strong expansibility.

It is known that food intake was associated with decreased risk of cancer, we prepared a compounded adlay seed oil (C-ASO) containing 0.5% cinnamaldehyde and 1% 6-gingerol and evaluated the preventive effects of the oral C-ASO on DMBA/TPA-induced skin carcinogenesis. In contrast to untreated carcinogenic mice, skin tumor incidence and multiplicity were decreased to some extent in single ASO-, cinnamaldehyde- and gingerol-treated mice, whereas the oral C-ASO almost completely reduced susceptibility to skin carcinogenesis by cooperative ablation of enhanced lipid rafts, drastically suppressed phosphatidylinositol 3-Kinases (PI3K)/protein kinases A (Akt) signaling and decreased levels of nuclear transcription factor-kappa B (NF-κB) and cyclin D1 which reduced skin epithelial proliferation and epithelial-mesenchymal transition (EMT) during tumor genesis. This work supports C-ASO as a candidate functional food to prevent carcinogenesis.