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Pancreatic adenocarcinoma (PAAD) as one of the most aggressive and lethal malignant tumors is correlated with increased morbidity and mortality. Tumorigenesis, growth, and metastasis are affected by various cytokines. Among them, CC chemokines can modulate the infiltration of immune cells and recruit cancer-associated immune cells, which play an important role in the inhibition of tumor immunity and affect the clinical outcome of cancer patients. However, the therapeutic potential and prognostic value of CC chemokines in PAAD have not yet been elucidated. To do this, we comprehensively explore and analyze large amounts of data on the basis of ONCOMINE database, GEPIA, LinkedOmics, cBioPortal, GeneMANIA, UALCAN, jvenn, DAVID 6.8, TRRUST, TIMER, and TISIDB. We found the transcriptional levels of CCL5/7/13/15/18/19/20 in PAAD tissues were remarkably increased, whereas the transcriptional level of CCL17 was decreased. CCL20 expression had significantly been correlated with the tumor stage of PAAD patients. High expressions of CCL5, CCL7, CCL13, CCL18, and CCL20 were notably correlated with the prognosis of patients. Moreover, patients with CCL18 and CCL19 alterations showed a poor prognosis in both overall survival (OS) and disease-specific survival (DSS), and patients with CCL5 and CCL15 alterations also presented a poor prognosis in OS. The functions of the aberrantly expressed CC chemokines were mainly correlated with the cytokine-cytokine receptor interaction, chemokine signaling pathway, inflammatory response, and immune response. Our study shows that the key transcription factors for CC chemokines are RELA and NF-κB1. We also discovered significant associations between the expression levels of CC chemokines and six infiltrating immune cells including CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, and dendritic cells. Taken together, our study indicated the interaction between CC chemokines and PAAD and clarified the value of CC chemokines as potential therapeutic targets as well as prognostic markers for PAAD.

In recent years, mobile edge computing (MEC), as a key technology that facilitates the 5G mobile network, has raised a number of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an online approach for solving dynamic EUA problem in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated against a baseline approach and two state-of-the-art approaches on a widely-used real-world dataset.

Poor sleep and different patterns of marital status among Hispanics/Latinos have been documented, yet the extent to which marital status is associated with sleep health and the moderating role of gender in this association among Hispanics/Latinos is poorly understood.Demographic and sleep data were obtained from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL: n= 16,415), an epidemiological cohort study, and the Sueno Study (n= 2,252) that is an ancillary to HCHS/SOL. Sleep duration, insomnia symptoms, daytime sleepiness, napping, and snoring were self-reported and drawn from HCHS/SOL. Sleep efficiency, sleep fragmentation, and inter-day stability were objectively assessed in the Sueno Study.Complex sample analyses indicated that being married or cohabiting was associated with better sleep health in general, including having normal sleep duration, fewer insomnia symptoms, and higher sleep efficiency (F> 2.804, p< .044). These associations were more prominent in objectively measured sleep indices and among females.Findings suggest being in a committed relationship associated with better sleep health in Hispanics/Latinos in the US, a diverse and under-represented population. Findings may have implications for tailoring sleep health interventions to at-risk populations who may less likely to be in a committed relationship.

Edge computing, as a new computing paradigm that extends cloud computing, allows a service vendor to deploy its services on distributed edge servers to serve its service users in close geographic proximity to those edge servers. Caching edge data on edge servers can minimize service users data retrieval latency. However, such edge data are subject to intentional and accidental corruption in the highly distributed and dynamic edge computing environment. Verifying the integrity of service vendors edge data accurately and efficiently is a critical security problem. Given a potentially large number of edge servers and their limited computing capacities, verifying the integrity of the edge data on each edge server individually is computation expensive. In this paper, we make the first attempt to tackle this Edge Data Integrity (EDI) problem from the service vendors perspective by proposing an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows a service vendor to inspect its edge data and localize corrupted edge data on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Advances in the development of anti-tumour drugs and related technologies have resulted in a significant increase in the number of cancer survivors. However, the incidence of chemotherapy-induced cardiotoxicity (CIC) has been rising continuously, threatening their long-term survival. The integration of nanotechnology and biomedicine has brought about an unprecedented technological revolution and has promoted the progress of anti-tumour therapy. In this review, we summarised the possible mechanisms of CIC, evaluated the role of nanoparticles (including liposomes, polymeric micelles, dendrimers, and hydrogels) as drug carriers in preventing cardiotoxicity and proposed five advantages of nanotechnology in reducing cardiotoxicity: Liposomes cannot easily penetrate the heart's endothelial barrier; optimized delivery strategies reduce distribution in important organs, such as the heart; targeting the tumour microenvironment and niche; stimulus-responsive polymer nano-drug carriers rapidly iterate; better economic benefits were obtained. Nanoparticles can effectively deliver chemotherapeutic drugs to tumour tissues, while reducing the toxicity to heart tissues, and break through the dilemma of existing chemotherapy to a certain extent. It is important to explore the interactions between the physicochemical properties of nanoparticles and optimize the highly specific tumour targeting strategy in the future.

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode.In the cloud services market, service providers attempt to make more profits from their services,while users hope to choose low-cost services with high-quality.The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This paper characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties.By analyzing the behaviors of users and service providers,we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions.In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method.The results are applicable to win-win service applications between service providers and users.

Edge computing has emerged as a promising paradigm for powering a huge number of applications requiring low latency, e.g., real-time navigation, interactive gaming, virtual or augmented reality, etc. Edge computing offers highly accessible computing and storage resources, including CPU, storage and bandwidth, by deploying edge servers at base stations. With more users accessing edge apps, a huge number of data will be transmitted via edge servers between users’ devices and remote cloud servers. A service provider can cache its app data on edge servers to ensure the low data retrieval latency of its users. Due to the physical size limits of edge servers, the optimal data caching strategy must minimize overall user latency with consideration of constrained cache spaces on edge servers. From the service provider’s perspective, we formulate this Constrained Edge Data Caching (CEDC) problem and prove the NP-hardness of this CEDC problem. After that, we propose CEDC-IP, an optimal approach solved by the Integer Programming technique, to find optimal solutions to this CEDC problem. We also propose CEDC-A, an approximation algorithm with theoretical guarantee, to find approximate solutions to large-scale CEDC problems efficiently. Finally, we conduct extensive experiments on a widely-used real-world data set, and the experimental results demonstrate that both CEDC-IP and CEDC-A significantly outperform the other four representative approaches.

Vehicle-to-everything communication is an indispensable component of 6G networks that could help to facilitate future transportation systems. However, massive vehicles and unstable vehicle-to-vehicle (V2V) links may become bottlenecks for the low-latency delivery of contents, such as safety-critical emergency messages and multimedia. Instead of resolving the problem in a centralized way, we propose a massive vehicular Internet of Things (IoT) system and investigate the approach that would enable each vehicle to decide the transmission mode from three modes, i.e., vehicle-to-network (V2N), vehicle-to-infrastructure (V2I) and V2V sidelinks, and wireless resources. Specifically, a multi-agent deep reinforcement learning framework is formulated by combining the multi-agent reinforcement learning approach, WoLF-PHC, with the techniques from deep Q-learning (DQN) to gain the formulated framework the capability of capturing the effects of interaction between learning agents and states of complex environment. The framework is set to maximize the throughput of vehicles while maintaining the latency and reliability constraints of the vehicle communication links. However, it could be easily extended to other objectives. The simulation results demonstrate that the proposed approach outperforms the compared ones in total traffic capacity and satisfaction rate of the vehicles in communication.

Background Whether the anti-gp210 antibody can be used as a biomarker in patients with primary biliary cholangitis (PBC) remains controversial. Aims We aimed to investigate the association between anti-gp210 antibodies and prognosis in ursodeoxycholic acid (UDCA)-treated PBC patients. Methods We conducted a retrospective cohort study of 180 UDCA-treated PBC patients to assess the prognostic value of anti-gp210 antibodies using the Kaplan–Meier method and Cox proportional hazard regression analysis. Results Of the patients included in our analysis, 50 (27.8%) were anti-gp210 positive, and 130 (72.2%) were anti-gp210 negative. The incidence of liver-related death or transplantation was more common in the anti-gp210 + group (22.0 vs. 9.2%, P=0.022). The five-year transplant-free survival rates of anti-gp210-positive patients vs. anti-gp210-negative patients were 77.0% and 90.3%, respectively. We found that the probability of transplant-free survival was significantly lower in the anti-gp210-positive patients than in the anti-gp210-negative patients (log-rank P=0.004). After adjusting for potential confounders using multivariable Cox regression model, positivity for anti-gp210 antibody (hazard ratio: 4.619, 95% confidence interval: 1.895–11.261, P=0.001) was found to be independently associated with an increase in liver-related mortality or transplantation. Conclusion In this cohort of UDCA-treated PBC patients, positivity for anti-gp210 antibody was independently associated with a higher risk of liver-related death or transplantation.

As the mobile era matures, it is increasingly competitive to market mobile apps, forcing companies to invest heavily on mobile user acquisition campaigns. This has unfortunately given birth to a new form of Internet fraud, which we refer to as ''app distribution fraud''. This new fraud involves collusion between ISPs and fraudulent app distributors where app download is hijacked/redirected. In this paper, we have the unique opportunity to cooperate with a major e-commerce company (with about 0.2 billion active users per month) to take a first peek at this problem. Through the nationwide measurement results, we find that app distribution fraud is ubiquitous yet stealthy --- about 1.55% app downloads are hijacked/redirected, affecting more than 75% of the cities we tested and causing an estimated 7.46 billion U.S. dollars financial loss per year. We follow up with additional measurements on the technical mechanism of the fraud and the scope of the fraud (i.e., what other apps are also affected). Surprisingly, we find that sometimes the original app a user intends to download can be replaced with a completely different app, rendering the user's device at risks.

Spontaneous subarachnoid hemorrhage (SAH) accounts for 5-10% of all strokes and is a subtype of hemorrhagic stroke that places a heavy burden on health care. Despite great progress in surgical clipping and endovascular treatment for ruptured aneurysms, cerebral vasospasm (CVS) and delayed cerebral ischemia (DCI) threaten the long-term outcomes of patients with SAH. Moreover, there are limited drugs available to reduce the risk of DCI and adverse outcomes in SAH patients. New insight suggests that early brain injury (EBI), which occurs within 72 h after the onset of SAH, may lay the foundation for further DCI development and poor outcomes. The mechanisms of EBI mainly include excitotoxicity, oxidative stress, neuroinflammation, blood-brain barrier (BBB) destruction, and cellular death. Mitochondria are a double-membrane organelle, and they play an important role in energy production, cell growth, differentiation, apoptosis, and survival. Mitochondrial dysfunction, which can lead to mitochondrial membrane potential (ΔΨm) collapse, overproduction of reactive oxygen species (ROS), release of apoptogenic proteins, disorders of mitochondrial dynamics, and activation of mitochondria-related inflammation, is considered a novel mechanism of EBI related to DCI as well as post-SAH outcomes. In addition, mitophagy is activated after SAH. In this review, we discuss the latest perspectives on the role of mitochondria in EBI and DCI after SAH. We emphasize the potential of mitochondria as therapeutic targets and summarize the promising therapeutic strategies targeting mitochondria for SAH.

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor's perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users data rates. In this paper, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

Background Hepatocellular carcinoma (HCC) is recognized as a leading cause of cancer-associated fatality worldwide. Our study here aimed to probe the mechanism by which exosomes secreted by CSQT-2, an HCC cell line, affected the progression of HCC. Methods Exosomes were extracted from CSQT-2 cells. Colony formation, Transwell, sphere formation and flow cytometric analyses were applied to assess cell biological activities. Microarray analysis detected the change of microRNA (miRNA) expression after exosome treatment, followed by RT-qPCR validation. Luciferase reporter was applied to detect the binding between SIK1 and miR-25. Xenograft studies in nude mice manifested tumor growth and metastatic ability of miR-25 and SIK1. Results The exosome treatment enhanced cell malignant phenotype in vitro and tumor growth and liver and lung metastases in vivo. The exosomes elevated miR-25 expression in HCC cells. miR-25 targeted SIK1 which was decreased in the exosomes-treated cells. miR-25 inhibitor reduced cell malignant phenotype and attenuated tumorigenesis and metastasis in vivo. SIK1 silencing reversed the effect of miR-25 inhibitor. The exosome treatment potentiated the Wnt/β-catenin pathway in cells, whereas miR-25 inhibitor blunted the pathway activity. Conclusion MiR-25 shuttled through CSQT-2-derived exosomes promoted the development of HCC by reducing SIK1 expression and potentiating the Wnt/β-catenin pathway.

A novel biomass-driven heat and power cogeneration system comprising biomass gasification, a gas turbine, a Stirling engine, and a supercritical carbon dioxide cycle integrated with a domestic water heater was proposed in this work. Different biomass feedstocks (paper, wood, paddy husk, and municipal solid waste) were used in the gasifier as the input fuel. The devised system was analyzed from energy, exergy, exergoeconomic, and environmental viewpoints. Moreover, the effect of integrating the Stirling engine with the stand-alone CHP system is studied. Moreover, a detailed parametric analysis was performed to assess the effect of varying operating parameters on system efficiency. Finally, multi-objective optimization using genetic algorithm in MATLAB software was performed to obtain the optimum operating points. According to the results, using municipal solid waste as the input biomass resulted in the highest exergy efficiency by 41.36 % and the lowest C O 2 emission by 0.9021 t / M W h . Also, the system with the Stirling engine had a higher exergy efficiency and lower C O 2 emission than the system without the Stirling engine. According to the optimization results, the maximum obtainable exergy efficiency was 42.03 % , which was related to MSW. Also, the minimum achievable c p , t o t was 10.94 $ / G J , attributable to the respective paddy husk.

Driven by the Internet of Things (IoT), many device-free crowd density estimation techniques can roughly estimate the crowd density based on the relationship between the dynamic crowd and the variation of wireless signals. However, they cannot distinguish the path information of different persons in a fine-grained manner. In this paper, we propose Wisual, a Channel State Information (CSI) based device-free crowd density estimation framework and can visualize the distribution of people. The major challenge of Wisual is how to extract proper quantifiable indexes to distinguish the path information of multiple targets and maximize the resolution of crowd density estimation. To address this challenge, Wisual first presents a method for estimating the frequency of the CSI propagation path (FoC) for the moving persons and constructs a joint multi-feature parameter (JMFP) spectrum matrix with the other two parameters. Then the multi-target spectrum matrix is put into a proposed deep-learning model called CSI stream 3D convolutional neural networks (CS-3DCNN) for implementing crowd density estimation and the target path information differentiation. Finally, Wi-Fi imaging is implemented based on the 2D-MUSIC algorithm, which shows the approximate distribution situation of indoor persons through the spectrograms. The experimental results in typical real-world scenes demonstrate that Wisual can forecast the crowd density with 98% precision and accurately display the frequency spectra of moving persons. Besides, the results also prove the superior effectiveness, scalability, and generalizability of the proposed framework.

Harnessing energy from the environment promotes the rapid development of micro-power generators and relevant power management modules of alternating current (AC) line-filtering to obtain a stabilized direct current (DC) output for storage and use. Micro-supercapacitors (MSCs) with miniaturized volume and high-frequency response are regarded as a critical component in filtering circuits for microscale power conversion. Here, we reported the fabrication of the wafer-sized planar MSCs (M-MSCs) based on 2D Ti3C2Tx MXene using a photolithography technique. The M-MSCs exhibited an areal capacitance of 153 μF cm−2 and a frequency characteristic (f0) of 5.6 kHz in aqueous electrolyte. Moreover, by employing suitable ionic liquid as electrolyte, the voltage window was expanded to 2 V and the f0 could be pushed to 6.6 kHz relying on the electrical double-layer mechanism and lower adsorption energy while maintaining quasi-rectangular cyclic voltammogram curves at 5000 V s−1. Furthermore, the integrated MSCs pack was constructed and exhibited excellent rectifying ability by filtering various high-frequency 5000 Hz AC signals with different waveforms into stable DC outputs. Such ultrahigh-rate and high-voltage M-MSCs module for kHz AC line-filtering would be potentially integrated with customizable electronics to realize on-chip rectifiers in high-density integrated circuit.

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of renal carcinoma and has a high recurrence rate and poor outcome. Accurate patient risk stratification based on genetic markers can help to identify the high-risk patient for early and further treatments and would promote patient survival. Long non-coding RNAs (lncRNAs) have attracted widespread attention as biomarkers for early diagnosis, treatment, and prognosis because of their high specificity and sensitivity. Here, we performed a systematic search in NCBI PubMed and found 44 lncRNAs as oncogenes, 18 lncRNAs as tumor suppressors, 199 lncRNAs as diagnostic biomarkers, 62 lncRNAs as prognostic biomarkers, and 3 lncRNAs as predictive biomarkers for ccRCC. We also comprehensively discuss the biological functions and molecular regulatory mechanisms of lncRNAs in ccRCC. Overall, the present study is a systemic analysis to assess the expression and clinical value of lncRNAs in ccRCC, and lncRNAs hold promise to be diagnostic, prognostic, and predictive biomarkers., Graphical abstract, lncRNAs play important roles in ccRCC. Here, Chao et al. write a systematic review to summarize and discuss the mechanisms, abnormal expressions, and clinical applications of lncRNAs in ccRCC, which provides a comprehensive insight into the research field of molecular regulatory mechanisms of lncRNAs in ccRCC.

In the multi-access edge computing (MEC) environment, app vendors’ data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors but also raises unprecedented challenges. To ensure fast data retrieval for users in the MEC environment, edge data caching must take into account the allocations of data, users, and transmit power jointly. We make the first attempt to study the Data, User, and Power Allocation (DUPA $^3$ 3 ) problem, aiming to serve the most users and maximize their overall data rate. First, we formulate the DUPA $^3$ 3 problem and prove its $\mathcal {NP}$ NP -completeness. Then, we model the DUPA $^3$ 3 problem as a potential DUPA $^3$ 3 game admitting at least one Nash equilibrium and propose a two-phase game-theoretic decentralized algorithm named DUPA $^3$ 3 Game to achieve the Nash equilibrium as the solution to the DUPA $^3$ 3 problem. To evaluate DUPA $^3$ 3 Game, we analyze its theoretical performance and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Edge computing (EC) can overcome several limitations of cloud computing. In the EC environment, a service provider can deploy its application instances on edge servers to serve users with low latency. Given a limited budget K for deploying applications in a particular geographical area, some approaches have been proposed to achieves various optimization objectives, e.g., to maximize the servers' coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider's applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile EC environment. We make the first attempt to tackle this challenge. Specifically, we formulate this Robustness-oriented Edge Application Deployment(READ) problem as a constrained optimization problem and prove its NP-hardness. Then, we provide an integer programming based approach READ-O for solving it precisely, and an approximation algorithm READ-A for efficiently finding near-optimal solutions to large-scale problems. READ-A's approximation ratio is not worse than K/2, which is constant regardless of the total number of edge servers. Evaluation of the widely-used real-world dataset against five representative approaches demonstrates that our approaches can solve the READ problem effectively and efficiently.

Background The aim of this study was to explore expression levels and clinical values of miR-21 and miR-210 in patients with sepsis after burns. Methods One hundred and twenty-eight burn patients who were treated in Binzhou Medical University Hospital were selected as research objects, among which 69 complicated with sepsis were in an observation group and 59 complicated with infection were in a control group. MiR-21 and miR-210 expression in the patients’ serum was detected by RT-PCR. Serum inflammatory cytokines were detected by an enzyme-linked immunosorbent assay (ELISA). Correlation analysis was used for the correlations of the miR-21 and miR-210 with the cytokines. Receiver operating characteristic (ROC) curves were plotted to analyze the diagnostic values of the miR-21 and miR-210 for sepsis after burns and their predictive values for the poor prognosis of sepsis patients. Results MiR-21 expression reduced remarkably and miR-210 expression rose remarkably in the serum of patients with sepsis after burns. According to the analysis of the ROC curves, both of the miR-21 and miR-210 had relatively high diagnostic sensitivity for the disease, but the diagnostic value of their combined detection was higher. Contents of hs-CRP, TNF-α, IL-6, and ICAM-1 in serum were remarkably higher in the observation group than those in the control group. According to the correlation analysis, miR-21 was negatively correlated with the expression of the four cytokines, while miR-210 was positively correlated with that. The predictive value of miR-21 for the prognosis of sepsis patients was not high, but miR-210 had a certain predictive value, and their combined detection had a higher value. Conclusion In serum of patients with sepsis after burns, miR-21 expression reduces remarkably and miR-210 expression rises. The miR-21 and miR-210 are related to the degree of inflammatory responses in septic patients, and their combined detection has a certain value for diagnosing the disease and predicting its prognosis.

Data-driven models have been widely used in industry helping big data analytics due to its convenience and flexibility. However, adversarial attack has the ability to add small perturbation to actual samples, which makes the data-driven models make incorrect predictions. With the high integration of industrial control systems and information technology, the reliability and safety of data-driven models in industry have been seriously threatened. In the paper, fault diagnosis models and quality variable prediction models are verified to be vulnerable facing adversarial attack. To this end, the concept of information fingerprint containing identity information is introduced to distinguish actual samples from adversarial samples with small perturbation. With fault diagnosis models and quality variable prediction models as the background, information fingerprint exaction networks (IfeNet) is developed to extract the information fingerprint for further analysis. IfeNet utilizes supervised contrastive pre-training and unsupervised training to realize parameter learning, with the structure of Siamese deep neural network and autoencoder. Finally, the effectiveness and feasibility of the proposed approach for fault diagnosis models and quality variable prediction models are verified in two industrial benchmark cases.

In industrial internet-of-things (IIoT), conventional event-triggered control method requires sensors continuously monitoring control states in periodical time. This would consume a huge amount of energy resource, which impedes the application of such conventional methods since the sensors are powered by batteries in most cases. To deal with this issue, this paper proposes a new state-to-noise-ratio (SNR) based transmission scheduling method from wireless communication perspective. Specifically, we first adopt an Additive White Gaussian Noise (AWGN) channel model to represent the event-triggered control model, by which we can convert the original event-triggered control problem into a wireless transmission scheduling problem from the sensor to the controller. Based on that, the mutual information is proposed to measure the value of the control states. More importantly, a new control SNR is defined based on mutual information. Using control SNR as the transmission activating criterion, perfect state observation assumption and sensor’s monitoring with periodical time in traditional event-triggered control are no longer needed. Furthermore, we provide an analytical expression of dynamic SNR thresholds to determine whether the transmission should be activated. Finally, we prove the mean-square stability of our proposed method. Numerical comparisons are provided to demonstrate the effectiveness of our proposed method.

In order to fully replace the traditional fossil energy supply system, the efficiency of electrochemical energy conversion and storage of new energy technology needs to be continuously improved to enhance its market competitiveness. The structural design of energy devices can achieve satisfactory energy conversion and storage performance. To achieve lightweight design, improve mechanical support, enhance electrochemical performance, and adapt to the special shape of the device, the structural energy devices develop very quickly. To help researchers analyze the development and get clear on developing trend, this review is prepared. This review summarizes the latest developments in structural energy devices, including special attention to fuel cells, lithium-ion batteries, lithium metal batteries, and supercapacitors. Finally, the existing problems of structural energy devices are discussed, and the current challenges and future opportunities are summarized and prospected. Structural energy devices can undoubtedly overcome the performance bottlenecks of traditional energy devices, break the limitations of existing materials and structures, and provide a guidance for the development of equipment with high performance, light weight and low cost in the future.

Background and aims Observational studies showed that coronavirus disease 2019 (COVID-19) attacks universally and its most menacing progression uniquely endangers the elderly with cardiovascular disease (CVD). The causal association between COVID-19 infection or its severity and susceptibility of atrial fibrillation (AF) remains unknown. Methods and results The bidirectional causal relations of COVID-19 (including COVID-19, hospitalized COVID-19 compared with not hospitalized COVID-19, hospitalized COVID-19 compared with population, and severe COVID-19) and AF are determined by using two-sample Mendelian randomization (MR) analysis. Genetically predicted severe COVID-19 was not significantly associated with risk of AF [odds ratio (OR), 1.037; 95% confidence interval (CI), 1.005-1.071; P = 0. 023, q = 0.115]. In addition, genetically predicted AF was also not causally associated with severe COVID-19 (OR, 0.993; 95% CI, 0.888-1.111; P = 0.905, q = 0.905). There was no evidence to support association between of genetically determined COVID-19 and risk of AF (OR, 1.111; 95% CI, 0.971-1.272; P = 0.127, q = 0.318), and vice versa (OR, 1.016; 95% CI, 0.976-1.058; P = 0.430, q = 0.851). Besides, no significant association was observed for hospitalized COVID-19 with AF. MR-Egger indicated no evidence of directional pleiotropy. Conclusion Overall, this MR study provides no clear support that COVID-19 is causally associated with the risk of AF.

The phenomenon of no-reflow seriously limits the therapeutic value of coronary recanalization and leads to poor prognosis. Recent studies have demonstrated the potential role of pigment epithelium-derived factor (PEDF) in stabilizing endothelial cell junction, reducing vascular permeability and maintaining a quiescent vasculature. In this study, intramyocardial gene delivery was performed 5 days before the acute myocardial infarction/recanalization experiment in male rats. Positron emission tomography perfusion imaging with

Osteonecrosis is a common orthopedic disease in clinic, resulting in joint collapse if no appropriate treatment is performed in time. Core decompression is a general treatment modality for early osteonecrosis. However, effective bone regeneration in the necrotic area is still a significant challenge. This study developed a biofunctionalized composite scaffold (PLGA/nHA30VEGF) for osteonecrosis therapy through potentiation of osteoconduction, angiogenesis, and a favorable metabolic microenvironment. The composite scaffold had a porosity of 87.7% and compressive strength of 8.9 MPa. PLGA/nHA30VEGF had an average pore size of 227.6 μm and a water contact angle of 56.5° with a sustained release profile of vascular endothelial growth factor (VEGF). After the implantation of PLGA/nHA30VEGF, various osteogenic and angiogenic biomarkers were upregulated by 2–9 fold compared with no treatment. Additionally, the metabolomic and lipidomic profiling studies demonstrated that PLGA/nHA30VEGF effectively regulated the multiple metabolites and more than 20 inordinate metabolic pathways in osteonecrosis. The excellent performances reveal that the biofunctionalized composite scaffold provides an advanced adjuvant therapy modality for osteonecrosis., Graphical abstract Image 1, Highlights • A biofunctionalized organic−inorganic composite scaffold is developed for osteonecrosis therapy. • The biofunctionalized composite scaffold potentiates osteoconduction and angiogenesis in osteonecrosis. • The biofunctionalized composite scaffold reverses the adverse microenvironments of osteonecrosis. • The biofunctionalized composite scaffold provides a promising clinical modality for treatment of early osteonecrosis.

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

The complexity of the tumor microenvironment and the diversity of tumors seriously affect the therapeutic effect, the focus, therefore, has gradually been shifted from monotherapy to combination therapy in clinical research in order to improve the curative effect. The synergistic enhancement interactions among multiple monotherapies majorly contribute to the birth of the multi-mode cooperative therapy, whose effect of the treatment is clearly stronger than that of any single therapy. In addition, the accurate diagnosis of the tumour location is also crucial to the treatment. Bismuth-based nanomaterials (NMs) hold great properties as promising theranostic platforms based on their many unique features that include low toxicity, excellent photothermal conversion efficiency as well as the high ability of X-ray computed tomography imaging and photoacoustic imaging. In this review, we will introduce briefly the main features of the tumor microenvironment first and its effect on the mechanism of nanomedicine actions and present the recent advances of bismuth-based NMs for diagnosis and photothermal therapy-based combined therapies using bismuth-based NMs are presented, which may provide a new way for overcoming drug resistance and hypoxia. In the end, further challenges and outlooks regarding this promising field are discussed accompanied with some design tips for bismuth- based NMs, hoping to provide researchers some inspiration to design safe and effective nanotherapeutic agents for clinical treatments of cancers.

Preoperative chemoradiotherapy (CRT) is the standard treatment for locally advanced rectal cancer (LARC). However, CRT failed to impact metastatic recurrence and the risk of side effects on bowel and genitourinary remained a concern. Neoadjuvant chemotherapy alone with mFOLFOX6 or FOLFOXIRI had been investigated in LARC. Here, we tried to compare the efficacy of mFOLFOXIRI with mFOLFOX6 as neoadjuvant chemotherapy in LARC.Between January 2014 and December 2019, patients with LARC receiving neoadjuvant chemotherapy with mFOLFOXIRI or mFOLFOX6 were retrospective analyzed, including data from a prospective trial (NCT02217020). All patients underwent total mesorectal excision (TME). The propensity-score matching was preformed to adjust baseline potential confounders and to estimate differences in outcomes between patients receiving mFOLFOXIRI and mFOLFOX6. Survival analysis was done using Kaplan-Meier analysis and Cox proportional regression analysis.The median follow-up time was 31.1 months. After propensity score matching, 156 patients were available for comparison in each group. The pathological complete response (pCR) rate was 17.9% vs. 5.1% (P.001), the incidence rate of anastomotic fistula was 3.2% vs. 9% (P = .03), the 3 year disease-free survival (DFS) rate was 75% vs. 66.7% (P = .047) and the distant metastasis rate was 16.4% versus 26.6% (P = .013) for mFOLFOXIRI and mFOLFOX6 group, respectively. Patients receiving mFOLFOXIRI had higher incidence of grade III and/or IV nausea and/or vomiting (7.6% vs. 2.5%, P = .04).Neoadjuvant mFOLFOXIRI regimens improved pCR rate and survival outcome, reduced the rate of distant metastasis and anastomotic fistula when comparing with propensity-score matched controls of mFOLFOX6 neoadjuvant chemotherapy.This trial assessed the short-term and long-term effects of neoadjuvant chemotherapy with mFOLFOXIRI and mFOLFOX6 in patients with locally advanced rectal cancer. Comparing with propensity-score matched historical control of chemoradiotherapy, neoadjuvant mFOLFOXIRI chemotherapy was well tolerated and led to higher rates of 3 year disease-free survival in patients with locally advanced rectal cancer.

Electrochemical water splitting is one of the most sustainable approaches for generating hydrogen. Because of the inherent constraints associated with the architecture and materials, the conventional alkaline water electrolyzer and the emerging proton exchange membrane electrolyzer are suffering from low efficiency and high materials/operation costs, respectively. Herein, we design a membrane-free flow electrolyzer, featuring a sandwich-like architecture and a cyclic operation mode, for decoupled overall water splitting. Comprised of two physically-separated compartments with flowing H2-rich catholyte and O2-rich anolyte, the cell delivers H2 with a purity >99.1%. Its low internal ohmic resistance, highly active yet affordable bifunctional catalysts and efficient mass transport enable the water splitting at current density of 750 mA cm−2 biased at 2.1 V. The eletrolyzer works equally well both in deionized water and in regular tap water. This work demonstrates the opportunity of combining the advantages of different electrolyzer concepts for water splitting via cell architecture and materials design, opening pathways for sustainable hydrogen generation., Seawater electrolysis is promising for grid-scale H2 production without freshwater reliance, but high energy costs and detrimental Cl chemistry reduce its practical potential. Here, authors developed an energy-saving hybrid seawater electrolyzer for chlorine-free H2 production and N2H4 degradation.

Peer reviewed: True, Ankle fractures are common injuries that can result in substantial morbidity in the population. This review discusses the management of closed ankle fractures and outlines the recent evidence and guidelines on perioperative management. In general, a detailed history should be undertaken, followed by examination and imaging of the affected limb. Fixation is based on the AO principles of fracture management that aims towards restoring stability of the joint and reducing the risk of long-term complications. A multidisciplinary approach towards perioperative management is recommended in view of the increasing proportion of aging patients with significant comorbidities.

China began connecting farmers directly with supermarkets 10 years ago, when they were at a disadvantage and forced to sell products at low prices, as unstable cooperation among supply chain participants led to inequitable distribution of revenue. Revenue-sharing contracts offer a risk-sharing approach to ensure supply chain coordination and optimize profit for all. Research on short life cycle products with revenue-sharing contracts assume stable prices or investigate the effects of revenue-sharing contracts on supply chain coordination. This study introduced a revenue-sharing contract model into a ‘farmer-supermarket direct-purchase’ supply chain, considering price fluctuation and retail promotional efforts, stochastic market demand, among other factors. Revenue-sharing contracts achieved long-term stability in supply chain coordination, all participants obtained more profits, and the size of revenue-sharing parameter depends on the position and bargaining power of all participants. A case study on Tianhong supermarket and Nanxia farmer cooperative verified these findings, eliciting practical implications for professionals and policymakers.

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space. However, existing solutions rely on the association between the holistic language features and visual features, while neglect the nature of compositional reasoning implied in the language. In this paper, we propose a natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along the tree in a bottom-up fashion. We call our model RVG-TREE: Recursive Grounding Tree, which is inspired by the intuition that any language expression can be recursively decomposed into two constituent parts, and the grounding confidence score can be recursively accumulated by calculating their grounding scores returned by sub-trees. RVG-TREE can be trained end-to-end by using the Straight-Through Gumbel-Softmax estimator that allows the gradients from the continuous score functions passing through the discrete tree construction. Experiments on several benchmarks show that our model achieves the state-of-the-art performance with more explainable reasoning., Comment: Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)

To evaluate the safety and efficacy of transepithelial accelerated corneal crosslinking (CXL) for advanced keratoconus eyes with maximum keratometry (Kmax) values58 diopters (D).Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.Prospective parallel control study.41 keratoconus eyes from 41 patients (mean age, 21.93 ± 5.48 years) who underwent transepithelial accelerated CXL were included prospectively. The enrolled eyes were divided into 2 groups according to their Kmax values (Group A, Kmax ≥58.0 D; Group B, Kmax58.0 D). The examinations including assessment of uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), corneal topography, and corneal endothelial cell density count were conducted preoperatively, at 1 day, 1 month, 3 months, 6 months, and 1 year postoperatively.All 41 eyes finished 1 year follow-up. No statistical difference was noted between the mean UDVA and CDVA in both groups throughout the follow-up duration. At 1-year postoperative follow-up, the CDVA increased by ≥2 lines in 45% (9/20) and 28.6% (6/21) eyes in Groups A and B, respectively. The mean preoperative Kmax in Groups A and B were 62.51 ± 3.34 D and 49.98 ± 4.32 D, respectively, and that at postoperative 1-year follow-up were 61.94 ± 4.11 D and 50.24 ± 4.72 D, respectively. The Kmax values of 30% (6/20) eyes in Group A and 4.8% (1/21) eyes in Group B decreased by more than 1 D. Deduction of flat K, steep K, mean K, and Kmax showed no significant difference between the 2 groups at 1-year postoperative follow-up. Moreover, 20% (4/20) and 23.8% (5/21) of eyes in Groups A and B, respectively, showed progress at postoperative 1-year follow-up.Transepithelial accelerated CXL can safely treat advanced keratoconus eyes with Kmax values ≥58.0 D with some extent of efficacy and has similar progressive rate as Kmax values58.0 D.

Purpose: With the emergence of multidrug-resistant and pan-resistant strains, Klebsiella pneumoniae (K. pneumoniae) shows higher treatment failure rates and mortality in clinics. It is more important to develop an effective method for treating K. pneumonia infections. The main objectives of this study were to determine the minimal inhibitory concentration (MIC) and the mutant prevention concentration (MPC) for eight antimicrobial agents against K. pneumoniae isolated from different hosts and compare the emergence of resistant mutants between animal strains and human strains. Materials and Methods: A total of 72 nonduplicate K. pneumoniae isolates and 8 antimicrobial agents (amikacin, azithromycin, levofloxacin, doxycycline, nitrofurantoin, colistin, tigecycline, and imipenem) were used. The MIC and MPC values were determined using agar plate assays. The values of the selection index (SI) were calculated with MPC90/MIC90. Pharmacodynamic parameters were calculated using published plasma pharmacokinetic variables. Results: For human isolate strains, the MPC50/90 (μg/mL) values were as follows: amikacin, 32/128; azithromycin, 64/128; levofloxacin, 4/16; doxycycline, 32/32; nitrofurantoin, 128/512; colistin, 4/8; tigecycline, 8/16; and imipenem, 4/8. The value of SI was 8 for azithromycin, doxycycline, and tigecycline; 16 for amikacin, levofloxacin, and nitrofurantoin; 4 for imipenem; and 2 for colistin. For animal isolate strains, the MPC90 values were 128 μg/mL for azithromycin and doxycycline, 64 μg/mL for amikacin, 32 μg/mL for levofloxacin, 512 μg/mL for nitrofurantoin, 8 μg/mL for colistin and tigecycline, 4 μg/mL for imipenem. The value of SI was 2 for colistin and imipenem, 8 for tigecycline, 16 for amikacin, and 32 for the other four agents. In combination with pharmacokinetic parameters, these findings indicated that the plasma concentrations of the seven antibiotics except imipenem were below the MPC for the entire dosing interval. Conclusion: The ability of eight antibiotics to prevent resistant mutants of K. pneumoniae was different between animal strains and human strains. Higher doses than those currently approved should be required to prevent the enrichment of mutants of drug-resistant bacteria in the clinics.

This paper aims to solve sample inefficiency in Asynchronous Advantage Actor-Critic (A3C). First, we design a new off-policy actor-critic algorithm, which combines actor-critic with experience replay to improve sample efficiency. Next, we study the sampling method of experience replay for trajectory experience and propose a familiarity-based replay mechanism which uses the number of replay times of experience as the probability weight of sampling. Finally, we use the GAE-V method to correct the bias caused by off-policy learning. We also achieve better performance by adopting a mechanism that combines off-policy learning and on-policy learning to update the network. Our results on Atari and MuJoCo benchmarks show that each of these innovations contributes to improvements in both data efficiency and final performance. Furthermore, our approach keeps a fast coverage speed and the same parallel feature as A3C, and also has better performance on exploration.

Research Question Does blastocyst biopsy for preimplantation genetic testing (PGT) increase the risk of adverse maternal and neonatal outcomes? Study design A retrospective cohort study was conducted. A total of 5097 single frozen-thawed blastocyst transfer cycles, including 2061 cycles in the biopsied group and 3036 cycles in the unbiopsied group, from January 2016 to December 2018 were enrolled in our analyses. Maternal and neonatal outcomes were compared between the two groups. Results The live birth rate in the biopsied group (41.1%) was significantly higher than that in the unbiopsied group (35.6%, adjusted OR=1.34 [95% CI: 1.10, 1.63], p=0.003) after adjusting for maternal age, maternal body mass index, gravidity, parity, infertility diagnosis, timing of blastocyst transfer, blastocyst quality, regimen of endometrial preparation, endometrial thickness before transfer, and treatment year. The rates of biochemical pregnancy loss (12.6% vs. 14.6%, adjusted OR=0.64 [0.43, 0.95], p=0.026) and early miscarriage (12.1% vs. 17.3%, adjusted OR=0.49 [0.33, 0.75, p=0.001) were significantly lower in the biopsied group than in the unbiopsied group. No significant differences were found in sex ratio or the risks of hypertensive disorders in pregnancy, diabetes in pregnancy, placenta previa, preterm premature rupture of membranes, low birth weight, very low birth weight, macrosomia, small for gestational age, large for gestational age or birth defects between the two groups. When the subgroup analyses were conducted based on different types of PGT, Similar patterns were found for all types. Conclusion Blastocyst biopsy might not increase the risks of adverse maternal and neonatal outcomes in the short term.

INTRODUCTION Neoadjuvant chemotherapy is standard treatment for locally advanced (LABC) or inflammatory breast cancer (IBC). We hypothesized adding sunitinib, a tyrosine kinase inhibitor with anti-tumor and anti-angiogenic activity, to an anthracycline and taxane regimen would improve pathologic complete response (pCR) rates to a prespecified endpoint of 45% in patients with HER2 negative LABC or IBC. PATIENTS AND METHODS We conducted a multicenter, phase II trial of neoadjuvant sunitinib with paclitaxel (S+T) followed by doxorubicin and cyclophosphamide plus G-CSF for patients with HER2 negative LABC or IBC. Patients received sunitinib 25 mg PO daily with paclitaxel 80 mg/m2 IV weekly x12 followed by doxorubicin 24 mg/m2 IV weekly + cyclophosphamide 60 mg/m2 PO daily with G-CSF support. Response was evaluated using pCR in the breast and the Clinical-pathologic scoring + estrogen receptor (ER) and grade (CPS+EG) score. RESULTS Seventy patients enrolled and 66 were evaluable for efficacy. Eighteen patients (27%) had pCR in the breast (10 had ER+ disease and 8 had triple negative disease). When defining response as pCR and/or CPS+EG score ≤ 2, 47% were responders. In ER+ patients, 23 (64%) were responders. The most common toxicities were cytopenias and fatigue. CONCLUSIONS Neoadjuvant S+T followed by AC+G-CSF was safe and tolerable in LABC and IBC. The study did not meet the prespecified endpoint for pCR. However, 47% were responders using pCR and/or CPS+EG score ≤2. ER+ patients had the highest response rate (64%). The addition of sunitinib to neoadjuvant chemotherapy may provide promising incremental benefit for ER+ patients.

China's natural gas market is not only growing rapidly but is also in the process of liberalization, indicating the potential of oil-gas price decoupling. In this situation, this study re-examines whether oil-gas price decoupling is happening in China, in order to analyze the achievements of market reform. First, a multi-scale decomposition approach is used to find oil-gas price patterns in the time-frequency domain. Then, a quantile-on-quantile analysis is used to acquire the entire dependence structure between oil and gas prices. The findings indicate that the association between oil and gas prices increases over scales but varies for quantiles. Specifically, clear evidence is provided that China's liquefied natural gas (LNG) price is independent of the oil prices selected for this study in the short term, whereas no additional evidence exists that proves oil and gas prices have already been decoupled. This finding indicates that the oil-indexation pricing mechanism still remains dominant. Based on the empirical results, the authors suggest that the establishment of trading hubs, the removal of restrictive clauses in contracts, and the diversification of pricing mechanisms should be simultaneously promoted in China.

Although lithium-ion batteries (LIBs) have many advantages, they cannot satisfy the demands of numerous large energy storage industries owing to their high cost, low security, and low resource richness. Aqueous zinc-ion batteries (ZIBs) with low cost, high safety, and high synergistic efficiency have attracted an increasing amount of attention and are considered a promising choice to replace LIBs. However, the existing cathode materials for ZIBs have many shortcomings, such as poor electron and zinc ion conductivity and complex energy storage mechanisms. Thus, it is crucial to identify a cathode material with a stable structure, substantial limit, and suitability for ZIBs. In this review, several typical cathode materials for ZIBs employed in recent years and their detailed energy storage mechanisms are summarized, and various methods to enhance the electrochemical properties of ZIBs are briefly introduced. Finally, the existing problems and expected development directions of ZIBs are discussed.

To effectively detect anomalies and eliminate the influence of noise on anomaly detection (AD), we propose a hyperspectral AD method based on low-rank decomposition and morphological filtering (called LRDMF). On the one hand, given the different ways in which anomalies and noise occur in the spectral bands, a low-rank decomposition model is proposed to decompose the original hyperspectral image (HSI) into background, anomaly, and noise components, where a superpixel segmentation method and the sparse representation (SR) model are used to construct a robust background dictionary. On the other hand, considering that the anomalies in HSI possess small area characteristics, a morphological filtering method is applied to preserve the small connected components. Finally, anomalies are detected by jointly considering the low-rank decomposition and morphological filtering results. The experimental results conducted on two real hyperspectral datasets demonstrate that the proposed method outperforms some of the state-of-the-art methods.

This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimization. To address the problem, PQ-curve inaccuracy caused by expression simplification is analyzed through the bridge of rotor current frame, rotor overspeeding control prioritized operation is recommended, and a surrogate-assisted distributed optimization (SADO) scheme is proposed from the WF perspective. The proposed method iteratively uses measured operating data to prompt a surrogate model to fit the accurate model, and then the optimal control action is guaranteed by online exploitation-and-exploration process with demonstrated availability through convergence analysis. Further, coordination with offline pretraining ensures that convergence can be obtained within shortened iteration steps. Case studies on 150-MW DFIG WF demonstrate the effectiveness of the proposed SADO scheme regarding shortening the iteration number, a full extraction on reactive power capacity and the better performance for voltage support.

The changing energy mix under the integration of wind and solar power widens the load peak-valley difference of the power grid and poses great challenges in power grid operation, especially for peak shaving. Hydropower has a crucial role to play in enabling the integration of more variable renewables. To explore a reasonable approach for the changing roles of hydropower and the development of wind and solar power in response to energy transition, a long-term typical daily aggregate model is developed for hydro-wind-solar power with peak shaving operation. Scenarios of integrated wind and solar power proportions under increasing penetrations are simulated by incrementally increasing the installed wind and solar power capacity. The model is converted to a mixed integer linear programming formulation. Groups of numerical experiments divided by influencing factors are used to analyze the complementary influence and optimization mechanisms. The results demonstrate that (1) the proposed model decouples hydro-wind-solar sources at temporal scales and conducts peak shaving effectively. (2) Different scales of wind and solar power have notable impacts on both long-term and short-term hydropower operation. (3) Developing wind and solar power in reasonable proportions could effectively utilize the complementarity and reduce hydropower output fluctuations on different time scales.

Recently, crowdsourced geographic data have provided a cost-effective approach to learn deep convolutional neural networks (DCNNs) for road extraction from remote sensing images. However, crowdsourced datasets often suffer from label noise and include error labels that can affect the performance of DCNN-based road extraction methods. Thus, we propose a novel sequence deep learning (SDL) framework that introduces label probability sequence to correct error labels and robustly learn DCNNs for road extraction. The label probability sequence is obtained by front-end developed DCNNs, which can provide valuable true label information to alleviate the adverse effect of label noise. Specifically, to pursue high effectiveness of the label correction process, an adaptive label correction method is proposed to exploit the distance between the true label distribution hidden in label probability sequence and the possible true label distribution. In addition, based on the uncertainty of label probability sequence, a noise correction loss function is introduced to robustly learn DCNNs on the improved dataset produced by the adaptive label correction. By repeating these two steps in each epoch, we can learn efficiently DCNN-based road extraction methods under noisy datasets. Experimental studies on two real-world noisy road datasets show that the proposed method can help to overcome label noise issue for DCNNs and achieve excellent performance in road extraction.