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A cloud computing multi-objective task scheduling optimization based on fuzzy self-defense algorithm is proposed. Select the shortest time, the degree of resource load balance and the cost of multi-objective task completion as the goal of cloud computing multi-objective task scheduling, establish a mathematical model to measure the effect of multi-objective task scheduling, and construct the objective function of cloud computing multi-objective task scheduling. The experimental results show that the cloud computing multi-objective task scheduling optimization method based on fuzzy self-defense algorithm can improve the performance of multi-objective maximum completion time, deadline violation rate and virtual machine resource utilization.

Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

The DC bus voltage of the unified power flow controller (UPFC) is closely related to the active power balance between the series and the shunt sides. In this study, a highly reliable modular multilevel converter (MMC)-UPFC DC bus voltage control strategy is proposed to ensure the continuous and stable operation of the MMC-UPFC. The DC voltage coordinated control strategy of dual converters in the shunt side, which combines the constant DC voltage control, droop control and margin control, is based on the topology configuration of the main converter and the standby converter. The DC voltage coordinated control strategy is adopted for different control targets under conditions of small-range adjustment of active power flow, wide-range adjustment, and the shunt-side converter failure, to achieve reliable and stable operation of UPFC under different operating conditions. Finally, a simulation model was established under the MATLAB/Simulink environment to verify the feasibility of this control strategy.

By Mobile Edge Computing (MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modelling both the topological properties of the road network and anticipating events -- such as rush hours -- that may occur in the future). Hence, it is an ideal target for graph representation learning at scale. Here we present a graph neural network estimator for estimated time of arrival (ETA) which we have deployed in production at Google Maps. While our main architecture consists of standard GNN building blocks, we further detail the usage of training schedule methods such as MetaGradients in order to make our model robust and production-ready. We also provide prescriptive studies: ablating on various architectural decisions and training regimes, and qualitative analyses on real-world situations where our model provides a competitive edge. Our GNN proved powerful when deployed, significantly reducing negative ETA outcomes in several regions compared to the previous production baseline (40+% in cities like Sydney)., To appear at CIKM 2021 (Applied Research Track). 10 pages, 4 figures

Cellular network configuration is critical for network performance. Current practice is mostly based on field experience and manual adjustment. The process is labor-intensive, error-prone, and far from optimal. To automate and optimize cellular network configuration, in this paper, we propose an online-learning-based joint-optimization approach that addresses a few specific challenges: limited data availability, convoluted sample data, highly complex optimization due to interactions among neighboring cells, and the need to adapt to network dynamics. In our approach, to learn an appropriate utility function for a cell, we develop a neural-network-based model that addresses the convoluted sample data issue and achieves good accuracy based on data aggregation. Based on the utility function learned, we formulate a global network configuration optimization problem. To solve this high-dimensional nonconcave maximization problem, we design a Gibbs-sampling-based algorithm that converges to an optimal solution when a technical parameter is small enough. Furthermore, we design an online scheme that updates the learned utility function and solves the corresponding maximization problem efficiently to adapt to network dynamics. To illustrate the idea, we use the case study of pilot power configuration. Numerical results illustrate the effectiveness of the proposed approach.

Making idle servers sleep is considered to be a key approach to reducing energy consumption of various information and communication systems. Optimal sleeping policies for a single server have been derived only for non-bursty traffic in prior work. In this paper, for the first time, we study the optimal sleeping operation for a single server with bursty traffic to answer the question of whether server sleeping can bring extra benefit with bursty traffic or not. Key factors including switchover energy consumption as well as delay performance are considered. We formulate the problem as a partially observable Markov decision process, and show that it can be solved by observing time elapsed since the last arrival. The optimal sleeping policy is shown to be a two-threshold policy with a wait-and-see feature, i.e., the server would wait a period of time to see if there are any extra arrivals before switching modes. Simulation results show that with the optimal sleeping mechanism, traffic burstiness can enhance system performance on energy cost and delay penalty.

Endocrine-disrupting compounds (EDCs) can interfere with endocrine systems and bio-accumulate through the food chain and even decrease biodiversity in contaminated areas. This review discusses a critical overview of recent research progress in the biotransformation of EDCs (including polychlorinated biphenyl and nonylphenol, and suspected EDCs such as heavy metals and sulfonamide antibiotics) by white rot fungi (WRF) based on techniques with an emphasis on summarizing and analyzing fungal molecular, metabolic and genetic mechanisms. Not only intracellular metabolism which seems to perform essential roles in the ability of WRF to transform EDCs, but also advanced applications are deeply discussed. This review mainly reveals the removal pathway of heavy metal and antibiotic pollutants because the single pollution almost did not exist in a real environment while the combined pollution has become more serious and close to people's life. The trends in WRF technology and its related advanced applications which use the combined technology, including biocatalysis of WRF and adsorption of nanomaterials, to degrade EDCs have also been introduced. Furthermore, challenges and future research needs EDCs biotransformation by WRF are also discussed. This research, referring to metabolic mechanisms and the combined technology of WRF with nanomaterials, undoubtedly contributes to the applications of biotechnology. This review will be of great benefit to an understanding of the trends in biotechnology for the removal of EDCs.

Biomass power generation projects involve green and renewable energy. This study regards Laifa Straw Recycling Company of Henan Sheqi as an example. Field survey and economic analysis are employed as the main research methods. The problems of straw collection, storage, and transportation are examined. According to the data obtained from the investigation and the mathematical model, when the straw recycling company uses the artificial model, the average price of each ton of straw is 385.4 yuan; when the straw recycling company uses the mechanical model, the average price of each ton of straw is 264.4 yuan. Comparison of the mechanical and artificial models reveals that the average price of each ton of straw differs by 121 yuan. Under the assumption that the annual purchase amount of straw is 2 × 10 5 tons, the cost of 2.42 × 10 7 yuan per year can be saved. Thus, Laifa Straw Recycling Company of Henan Sheqi should consider the mechanical model. Sensitivity analysis of the two models shows that the collection cost is the most sensitive factor when the straw recycling company utilizes the artificial model because most farmers go out to work, so a shortage in rural labor force occurs; this shortage results in increased cost of artificial collection. When the straw recycling company utilizes the mechanical model, the cost of storage is the most sensitive factor because of the large storage amount. High current land leasing and management costs lead to increased storage cost.

By decoupling control coverage and traffic coverage, dynamic base station (BS) sleeping becomes possible in hyper-cellular networks. Traffic coverage can be adjusted based on real-time demands, to enhance the energy efficiency of the networks. However, sleeping mechanisms may affect the networks delay performance. Thus, it is important to obtain analytical results on the influence of sleeping mechanisms on the systems energy efficiency and delay performance. The optimal sleeping mechanism can be derived based on the energy-delay tradeoff. This work summarizes the relevant contributions of our group: based on the vacation queueing model, closed-form expressions for average power and mean traffic delay were obtained. The conditions for the energy-delay tradeoff were analyzed. Further, the optimal BS sleeping design and the optimal energy-delay tradeoff were obtained, assuming fixed delays. The influence of the mode-changing energy cost was studied. BS sleeping designs for bursty traffic were analyzed, combined with power matching. The optimal BS sleeping mechanism for bursty traffic was obtained with a partially observable Markov decision process (POMDP).

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

Cellular network performance depends heavily on the configuration of its network parameters. Current practice of parameter configuration relies largely on expert experience, which is often suboptimal, time-consuming, and error-prone. Therefore, it is desirable to automate this process to improve the accuracy and efficiency via learning-based approaches. However, such approaches need to address several challenges in real operational networks: the lack of diverse historical data, a limited amount of experiment budget set by network operators, and highly complex and unknown network performance functions. To address those challenges, we propose a collaborative learning approach to leverage data from different cells to boost the learning efficiency and to improve network performance. Specifically, we formulate the problem as a transferable contextual bandit problem, and prove that by transfer learning, one could significantly reduce the regret bound. Based on the theoretical result, we further develop a practical algorithm that decomposes a cell’s policy into a common homogeneous policy learned using all cells’ data and a cell-specific policy that captures each individual cell’s heterogeneous behavior. We evaluate our proposed algorithm via a simulator constructed using real network data and demonstrates faster convergence compared to baselines. More importantly, a live field test is also conducted on a real metropolitan cellular network consisting 1700+ cells to optimize five parameters for two weeks. Our proposed algorithm shows a significant performance improvement of 20%.

A small, novel wheat drying oven was designed, and tested in order to validate the possibility of recovering diesel engine waste heat as thermal energy. Prior to experiments, the theoretical probability was conducted, and it was confirmed that the thermal energy required for drying wheat could be satisfied by reusing the diesel engine waste heat. Three different experiment parameters, including air temperature, baffle angle, and drying time, were controlled to optimize the results. Based on the experiment data, the optimum temperature for drying wheat was 55°C, and the optimum baffle angle was 23.25°. The drying efficiency decreased as the drying temperature and drying time increased. The moisture content of wheat decreased from 26.0% to 20.0% with 55°C hot air when the proposed wheat drying oven was used. The test results demonstrated that the proposed oven could be made to work.

The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm., Comment: 13 pages, 7 figures, accepted by IEEE Transactions on Vehicular Technology

Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrence-awareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance., 7 pages, 4 figures

Base station (BS) sleeping operation is one of the effective ways to save energy consumption of cellular networks, but it may lead to longer delay to the customers. The fundamental question then arises: How much energy can be traded off by a tolerable delay ? In this paper, we characterize the fundamental tradeoffs between total energy consumption and overall delay in a BS with sleep mode operations by queueing models. Here, the BS total energy consumption includes not only the transmitting power but also basic power (for baseband processing, power amplifier, etc.) and switch-over power of the BS working mode, and the overall delay includes not only transmission delay but also queueing delay. Specifically, the BS is modeled as an M/G/1 vacation queue with setup and close-down times, where the BS enters sleep mode if no customers arrive during the close-down (hysteretic) time after the queue becomes empty. When asleep, the BS stays in sleep mode until the queue builds up to $N$ customers during the sleep period ( $N$ -Policy) . Several closed-form formulas are derived to demonstrate the tradeoffs between the energy consumption and the mean delay for different wake-up policies by changing the close-down time, setup time, and the parameter $N$ . It is shown that the relationship between the energy consumption and the mean delay is linear in terms of mean close-down time, but non-linear in terms of $N$ . The explicit relationship between total power consumption and average delay with varying service rate is also analyzed theoretically, indicating that sacrificing delay cannot always be traded off for energy saving. In other words, larger $N$ may lead to lower energy consumption, but there exists an optimal $N^{\ast} $ that minimizes the mean delay and energy consumption at the same time. We also investigate the maximum delay (delay bound) for certain percentage of service and find that the delay bound is nearly linear in mean delay in the cases tested. Therefore, similar tradeoffs exist between energy consumption and the delay bound. In summary, the closed-form energy–delay tradeoffs cast light on designing BS sleeping and wake-up control policies that aim to save energy while maintaining acceptable quality of service.

This paper presents a planar ultra-wideband (UWB) bandpass filter with sharp out-of-band rejection performance. The filter is formed by a folded multiple-mode resonator to realize high performance in an operation band from 3.3 to 10 GHz with a very compact size of 20 mm × 20 mm × 0.5 mm. An extra notched band centered at 5.8 GHz is further accomplished by etching a Hilbert fractal curve slit on the filter without the necessity of readjusting the geometrical parameters. The simulated and measured results are in good agreement.

In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computational services. The offloading of computational tasks and collection of results rely on successful transmissions between vehicles and infrastructures during encounters. In this paper, we investigate how to provide timely computational services in VCC systems. In particular, we seek to minimize the deadline violation probability given a set of tasks to be executed in vehicular clouds. Due to the uncertainty of vehicle movements, the task replication methodology is leveraged which allows one task to be executed by several vehicles, and thus trading computational resources for delay reduction. The optimal task replication policy is of key interest. We first formulate the problem as a finite-horizon sampled-time Markov decision problem and obtain the optimal policy by value iterations. To conquer the complexity issue, we propose the balanced-task-assignment (BETA) policy which is proved optimal and has a clear structure: it always assigns the task with the minimum number of replicas. Moreover, a tight closed-form performance upper bound for the BETA policy is derived, which indicates that the deadline violation probability follows the Rayleigh distribution approximately. Applying the vehicle speed-density relationship in the traffic flow theory, we find that vehicle mobility benefits VCC systems more compared with road traffic systems, by showing that the optimum vehicle speed to minimize the deadline violation probability is larger than the critical vehicle speed in traffic theory which maximizes traffic flow efficiency., accepted by Internet of Things Journal

Background We aimed to determine the risk conferred by metabolic syndrome ( METS ) and diabetes mellitus ( DM ) to recurrent stroke in patients with minor ischemic stroke or transient ischemic attack from the CHANCE (Clopidogrel in High‐risk patients with Acute Non‐disabling Cerebrovascular Events) trial. Methods and Results In total, 3044 patients were included. Patients were stratified into 4 groups: neither, METS only, DM only, or both. METS was defined using the Chinese Diabetes Society ( CDS ) and International Diabetes Foundation ( IDF ) definitions. The primary outcome was new stroke (including ischemic and hemorrhagic) at 90 days. A multivariable Cox regression model was used to assess the relationship of METS and DM status to the risk of recurrent stroke adjusted for potential covariates. Using the CDS criteria of METS , 53.2%, 17.2%, 19.8%, and 9.8% of patients were diagnosed as neither, METS only, DM only, and both, respectively. After 90 days of follow‐up, there were 299 new strokes (293 ischemic, 6 hemorrhagic). Patients with DM only (16.1% versus 6.8%; adjusted hazard ratio 2.50, 95% CI 1.89–3.39) and both (17.1% versus 6.8%; adjusted hazard ratio 2.76, 95% CI 1.98–3.86) had significantly increased rates of recurrent stroke. No interaction effect of antiplatelet therapy by different METS or DM status for the risk of recurrent stroke ( P =0.82 for interaction in the fully adjusted model of CDS ) was observed. Using the METS ( IDF ) criteria demonstrated similar results. Conclusions Concurrent METS and DM was associated with an increased risk of recurrent stroke in patients with minor stroke and transient ischemic attack.

Mobile edge computing is a novel technique in which mobile devices offload computation-intensive tasks with stringent delay requirements to the edge cloud. However, the limited computational resource in the edge cloud may result in the Quality of Service degradation. In this paper, we address this issue by coordinating the heterogeneous cloud which includes the edge cloud and the remote cloud. Considering the offloading of delay-bounded tasks, we study into the scheduling of heterogeneous cloud in order to maximize the probability that tasks can have the delay requirements met. The problem formulation is proved to be concave, and an optimal algorithm is proposed accordingly. The optimal policy with heterogeneous cloud is notably different from the policy merely using the edge cloud. With only the edge cloud, the system serves tasks with loose delay bounds and drops tasks with stringent delay bounds when the traffic load is heavy. However, with the heterogeneous cloud, tasks with stringent delay bounds are offloaded to the edge cloud and tasks with loose delay bounds are offloaded to the remote cloud. In numerical results, the probability that the delay bounds of tasks are satisfied can be improved by about 40% with the assistance of the remote cloud.

Mobile edge computing is a novel technique to offer cloud-based computation offloading services to mobile users with short delay. However, the Cloud Service Providers (CSPs) of the edge cloud are generally different from the CSPs of remote Internet cloud. Considering the competition between the heterogeneous clouds, we study the optimal provisioning of the computational resource in the edge cloud. We firstly analyze the Nash equilibrium prices of the cloud market when the amount of edge computational resource is given. Based on the pricing policy, we further design an algorithm to optimize the edge computational resource capacity so that the profit of the edge cloud is maximized. We also derive the lower and upper bound of the optimal edge computational resource. The numerical results indicate that the profit of the edge cloud is greatly influenced by the price of the remote cloud when users care more about the price than the delay.

Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.

A problem of much current practical interest is the replacement of the wiring infrastructure connecting approximately 200 sensor and actuator nodes in automobiles by an access point. This is motivated by the considerable savings in automobile weight, simplification of manufacturability, and future upgradability. A key issue is how to schedule the nodes on the shared access point so as to provide regular packet delivery. In this and other similar applications, the mean of the inter-delivery times of packets, i.e., throughput, is not sufficient to guarantee service-regularity. The time-averaged variance of the inter-delivery times of packets is also an important metric. So motivated, we consider a wireless network where an Access Point schedules real-time generated packets to nodes over a fading wireless channel. We are interested in designing simple policies which achieve optimal mean-variance tradeoff in interdelivery times of packets by minimizing the sum of time-averaged means and variances over all clients. Our goal is to explore the full range of the Pareto frontier of all weighted linear combinations of mean and variance so that one can fully exploit the design possibilities. We transform this problem into a Markov decision process and show that the problem of choosing which node's packet to transmit in each slot can be formulated as a bandit problem. We establish that this problem is indexable and explicitly derive the Whittle indices. The resulting Index policy is optimal in certain cases. We also provide upper and lower bounds on the cost for any policy. Extensive simulations show that Index policies perform better than previously proposed policies.

With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deployed locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay-sensitive applications are executed ahead of delay-tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system., Comment: 6 pages, 7 figures, accepted by GlobeCom'15 Workshop

Objectives: The prevalence of depression is relatively high in individuals with diabetes. However, screening and monitoring of depressive state in patients with diabetes is still neglected in developing countries and the treatment of diabetes-related depression is rarely performed in these countries. In this study, our aim was to study the role of diabetes education in the improvement of depressive state in newly diagnosed patients with type 2 diabetes. Methods: The Dutch version of the center for epidemiological studies depression scale (CES-D scale) and the problem areas in diabetes (PAID) questionnaire were used to assess depression and diabetes-specific emotional distress in 1200 newly diagnosed male adult patients with type 2 diabetes before and after a two-week diabetes education by professionally trained nurses. Pearson correlation and regression analysis were used to analyze the factors related to depression in patients with type 2 diabetes. Results: The incidence of depression in newly diagnosed patients with type 2 diabetes was 28%, and the rate of diabetes-specific emotional distress was 65.5%. High education levels, low income were correlated to depression in individuals with diabetes. After two weeks of diabetes education, the incidence of depression and diabetes-specific emotional distress decreased significantly to 20.5% (P < 0.05) and 11% (P < 0.001), respectively. Conclusions: The incidence of depression, especially diabetes-specific emotional distress, was relatively high in newly diagnosed patients with type 2 diabetes. The depression state could be improved by diabetes education.