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Geothermal energy with abundance and large quantity can partially cover building heating/cooling loads and promote the carbon-neutrality transitions. Shallow geothermal ventilation (SGV) system, with a little initial investment cost, is one of promising technologies to partly replace the conventional air-conditioning system for air pre-cooling/pre-heating. This paper reviews applications of SGV system for improving thermal performance over latest two decades, which mainly includes the reclassification of SGV system, coupling with other advanced energy-saving technologies, application potentials for building cooling/heating under various weather conditions. Heat transfer mechanism and mathematical modelling techniques have been reviewed, together with in-depth analysis on current research trends, existing limitations, and recommendations of SGV system. Phase change materials, with considerable latent energy density, can stabilize the thermal performance with high reliability. The review identifies that optimization designs and advanced approaches need to be investigated to address the existing urgent issues of SGV system (e.g., large land occupation, difficulty in centralized collection of condensate water timely for horizontal buried pipe, bacteria growth, polluted supply air, and high construction cost for vertical buried pipe). A plenty of studies show that the SGV system could greatly expand the application scope and improve system energy efficiency by combining with other energy-saving technologies. This paper will provide some guidelines for the scientific researchers and engineers to keep track on recent advancements and research trends of SGV system for the building thermal performance enhancement and pave path for future research works.

Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

Recently, reconfigurable intelligent surface (RIS) has attracted continuous attention in wireless communications. Because the normal incidence condition of electromagnetic waves cannot always be satisfied in practical applications, the angular insensitivity in RISs has become an important issue. The angular sensitivity in RISs may lead to the failure of RIS-assisted wireless communication networks that rely on the reciprocity of wireless channels. In this work, an angle-insensitive 3-bit RIS meta-atom is proposed. By introducing metallic vias in the substrate of programmable meta-atoms, the interference induced by multiple reflections in the multilayered structure is restrained, and the decoupling between adjacent meta-atoms makes the metasurface insensitive to the incident angle. Hence the angular insensitivity can be achieved. An RIS is fabricated using the proposed angleinsensitive programmable meta-atom with metallic vias, which is capable of maintaining stable phase and amplitude responses at oblique incidences. Both simulated and measured results show stable angle performance. A maximum of 3150 phase range and eight digital coding states with 450 stable interval are obtained for wide incidence angles from 00 to 600. Finally, numerical simulations and experimental results of anomalous reflections are performed to verify the angular reciprocity of the proposed RIS, which is important in wireless communications.

International audience; Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15%, when the number of subjects is 50.

Background: The ubiquitin-proteasome system (UPS) is critical in cellular protein degradation and widely involved in the regulations of cancer hallmarks. Targeting the UPS pathway has emerged as a promising novel treatment in hematological malignancies and solid tumors. Objective: This review mainly focuses on the preclinical results of proteasome inhibitors in solid tumors. Methods: We analyzed the published articles associated with the anticancer results of proteasome inhibitors alone or combination chemotherapy in solid tumors. Important data presented in abstract form were also discussed in this review. Results/Conclusion: Proteasome inhibitors, such as bortezomib and carfilzomib, are highly effective in treating solid tumors. The anticancer efficacy is not limited to affect the proteasomal inhibition- associated signaling pathways but also widely involves the signaling pathways related to cell cycle, apoptosis, and epithelial-mesenchymal transition (EMT). In addition, proteasome inhibitors overcome the conventional chemo-resistance of standard chemotherapeutics by inhibiting signaling pathways, such as NF-κB or PI3K/Akt. Combination chemotherapy of proteasome inhibitors and standard chemotherapeutics are widely investigated in multiple relapsed or chemo-resistant solid tumor types, such as breast cancer and pancreatic cancer. The proteasome inhibitors re-sensitize the standard chemotherapeutic regimens and induce synergistic anticancer effects. The development of novel proteasome inhibitors and delivery systems also improves the proteasome inhibitors’ anticancer efficacy in solid tumors. This review summarizes the current preclinical results of proteasome inhibitors in solid tumors and reveals the potential anticancer mechanisms.

Accurate vehicle sideslip angle estimation is crucial for vehicle stability control. In this paper, an enabling event-triggered sideslip angle estimator is proposed by using the kinematic information from a low-cost Global Positioning System (GPS) and an on-board Inertial Measurement Unit (IMU). First, a preliminary vehicle sideslip angle is derived using the heading angle of GPS and the yaw rate of IMU, and an event-triggered mechanism is proposed to eliminate the accumulative estimation error. The algorithm convergence is guaranteed through theoretical deduction. Second, a longitudinal and a lateral vehicle velocity are obtained using the preliminary vehicle sideslip angle and the measured GPS velocity and their kinematic relationship, based on which a multi-sensor fusion and a multi-step Kalman filter scheme are respectively presented to realize longitudinal and lateral vehicle velocity estimation. By doing this, the update frequency and estimation accuracy of the vehicle sideslip angle estimate can be further improved to meet the requirement of online implementation. Finally, the effectiveness and reliability of the proposed scheme are verified under comprehensive driving conditions through both hardware-in-loop (HIL) and field tests. The results show that the proposed event-triggered sideslip angle estimator has a mean estimation error of 0.029 deg and of 0.14 deg in the HIL and field tests, exhibiting better estimation accuracy, reliability and real-time performance compared with other typical estimators.

In cloud computing, resources are usually in cloud service provider's network and typically accessed remotely by the cloud users via public channels. Key agreement enables secure channel establishment over a public channel for the secure communications between a cloud user and a cloud service provider. Existing key agreement protocols for cloud computing suffer from some challenges, e.g., realizing low connection delay, eliminating certificate management problem, enhancing user privacy and avoiding bad randomness. To tackle these challenges, we propose a certificateless 0-RTT anonymous AKA protocol against bad randomness for secure channel establishment in cloud computing. As a 0-RTT protocol, it significantly speeds up the efficiency of the secure channel establishment process. Further, our protocol does not need for the certificates to bind a public key with an entity's identity and hence solves the certificate management problem. Finally, concrete security analysis of the protocol is also proposed. The protocol not only satisfies the traditional security attributes (e.g., known-key security, unknown key-share), but also strong security guarantees, i.e., user privacy and bad randomness resistance.

RGB-T salient object detection (SOD) aims at utilizing the complementary cues of RGB and Thermal (T) modalities to detect and segment the common objects. However, on one hand, existing methods simply fuse the features of two modalities without fully considering the characters of RGB and T. On the other hand, the high computational cost of existing methods prevents them from real-world applications (e.g., automatic driving, abnormal detection, person re-ID). To this end, we proposed an efficient encoder-decoder network named Context-guided Stacked Refinement Network (CSRNet). Specifically, we utilize a lightweight backbone and design efficient decoder parts, which greatly reduce the computational cost. To fuse RGB and T modalities, we proposed an efficient Context-guided Cross Modality Fusion (CCMF) module to filter the noise and explore the complementation of two modalities. Besides, Stacked Refinement Network (SRN) progressively refines the features from top to down via the interaction of semantic and spatial information. Extensive experiments show that our method performs favorably against state-of-the-art algorithms on RGB-T SOD task while with small model size (4.6M), few FLOPs (4.2G), and real-time speed (38fps). Our codes is available at: https://github.com/huofushuo/CSRNet.

3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy, delineation of the clinical target volume (CTV) is an essential step in the establishment of treatment plans. However, manual delineation is subjective and time consuming. In this study, we propose an automated segmentation model based on deep neural networks for the breast cancer CTV in planning computed tomography (CT). Our model is composed of three stages that work in a cascade manner, making it applicable to real-world scenarios. The first stage determines which slices contain CTVs, as not all CT slices include breast lesions. The second stage detects the region of the human body in an entire CT slice, eliminating boundary areas, which may have side effects for the segmentation of the CTV. The third stage delineates the CTV. To permit the network to focus on the breast mass in the slice, a novel dynamically strided convolution operation, which shows better performance than standard convolution, is proposed. To train and evaluate the model, a large dataset containing 455 cases and 50 425 CT slices is constructed. The proposed model achieves an average dice similarity coefficient (DSC) of 0.802 and 0.801 for right-0 and left-sided breast, respectively. Our method shows superior performance to that of previous state-of-the-art approaches.

Deoxynivalenol (DON) is a mycotoxin that commonly contaminates cereals worldwide. Dietary exposure to DON is a subject of great public health concern, but studies on the health effects of chronic exposure to DON are not available. In this study, we investigated the connection between DNA methylation levels and DON exposure in children. The DNA methylation status of white blood cells from 32 children aged 2~15 years old in Henan, China, was profiled. A total of 378 differentially methylated CpGs were identified between the high and low DON exposure groups, and 8 KEGG pathways were found to be significantly enriched among the differentially methylated genes. In addition, the quantitative methylation of EIF2AK4, EMID2 and GNASAS was analysed using the Sequenom MassARRAY platform. The results showed that the methylation level of EIF2AK4 was significantly different between the two groups, and the methylation levels were associated with exposure to DON. Conclusively, our study found that chronic exposure to DON during childhood could affect DNA methylation levels.

Leptomeningeal metastasis (LM) is a disastrous complication in lung cancer. LM patients with oncogene-addicted non-small cell lung cancer (NSCLC) have a relatively better prognosis than those with the wild-type counterpart; however, overall post-LM survival is short. Additionally, the high heterogenicity of the LM entity creates a treatment challenge, and to date, no standard strategy has been established. This article describes a female lung adenocarcinoma patient with a resistant epidermal growth factor receptor (EGFR) exon20ins mutation who developed LM only 11 months after radical surgery IIIA (pT1bN2). Intrathecal chemotherapy (ITC), whole-brain radiotherapy (WBRT) with a simultaneous integrated boost (SIB) followed by Osimertinib was initiated. The cerebrospinal fluid (CSF) cytology turned negative. The first remission lasted 6 months, then bone metastases occurred, and the LM progressed. An Ommaya reservoir was implanted. ITC with pemetrexed and anlotinib was administered. A CSF next-generation sequencing (NGS) examination revealed EGFR exon20ins (p. A767_V769 dup 1.5%), which was different from that of the primary tumor (p. V769_D770 ins ASV 17.48%). The CSF cytology then turned negative again; however, the patient succumbed to the disease in December 2020. The patient's post-LM overall survival (OS) time was 13.5 months. This case is novel and of great value. Clinicians should pay special attention to populations at high risk of developing LM. Early detection followed by active intervention, including ITC, RT, and systemic treatment, will result in a better prognosis. The NGS of CSF is fundamental to understanding the genetic profiles of LM and providing effective and precise treatment.

Acute myocardial infarction (AMI) remains a dominant origin of morbidity, mortality and disability worldwide. Increases in reactive oxygen species (ROS) are key contributor to excessive cardiac injury after AMI. Here we developed an immobilized enzyme with Superoxide Dismutase (SOD) activity cross-link with Zr-based metal-organic framework (ZrMOF) (SOD-ZrMOF) for mitigate ROS-caused injury. In vitro and in vivo evidence indicates that SOD-ZrMOF exhibits excellent biocompatibility. By efficiently scavenging ROS and suppressing oxidative stress, SOD-ZrMOF can protect the function of mitochondria, reduce cell death and alleviate inflammation. More excitingly, long-term study using an animal model of AMI demonstrated that SOD-ZrMOF can reduce the infarct area, protect cardiac function, promote angiogenesis and inhibit pathological myocardial remodeling. Therefore, SOD-ZrMOF holds great potential as an efficacious and safe nanomaterial treatment for AMI., Graphical abstract Image 1, Highlights • We synthesized a superoxide dismutase immobilized nanocomposite with enhanced stability. • We tested the biocompatibility of SOD-ZrMOF and found it could promote the cell viability and proliferation of H9c2 cells. • SOD-ZrMOF shows excellent performance in protecting mitochondrial function of CMs by eliminating excessive ROS under hypoxia. • SOD-ZrMOF exhibited therapeutic effects on MI heart through inhibiting oxidative stress injury by NF-κB/HIF-1α pathway.

Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations.In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns.As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs.We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

By violating semantic constraints that the control process impose, the semantic attack leads the Industry Control Systems (ICS) into an undesirable state or critical state. The spread of semantic attack has caused huge economic losses and casualties to critical infrastructure. Therefore, detecting semantic attack is referred to an urgent and critical task. However, few existing detecting techniques can achieve satisfactory effects in detecting semantic attack of ICS, due to the high requirements of complete critical state-based semantic behavior features description, joint detection on multivariate type state variables, and validity of field states datasets under semantic attacks. In an effort to deal with above challenges, We label device states databases with temporal characteristics and divide impacts on states of field devices under semantic attacks into three categories, including absent in states set, confused sequences, irregular frequency. On this basis, we establish a behavioral model based on secondary labeling of states-duration evolution graph (BMSLS), then implement an adaptive secure state-based semantic attack detection framework furtherly. Compared with the traditional Auto Regression (AR) algorithm, the newer time series correlation graph model, and other five deep learning algorithms, our proposed framework demonstrates the superior effect on the detection of semantic attack.

Mobile edge computing (MEC) is a promising solution to enable resource-limited mobile devices to offload computation-intensive tasks to nearby edge servers. In this paper, dynamic computation offloading in a non-orthogonal multiple access (NOMA) based multi-user network is investigated by considering stochastic task arrivals and wireless channels. Specifically, a cooperative multi-agent deep reinforcement learning (CMADRL) based framework is proposed to learn decentralized offloading policies to allocate task offloading and local execution powers at each user, which minimizes long-term average network computation cost in terms of power consumption and buffering delay. By leveraging a centralized training and distributed execution strategy, the proposed framework can not only learn efficient decentralized policies, but also relieves users’ computational pressure and works well in coordinating the interference of NOMA-based networks. To reduce the training complexity, it is further improved to train one parameter shared policy network that can be exploited by all users with slightly sacrificed performance. Numerical simulations demonstrate that the proposed CMADRL based framework can learn efficient dynamic offloading policies at each user, which significantly outperforms the conventional independent Q-learning based framework as well as some greedy strategies with lower network computation cost.

Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.

Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available at: \url{https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping-Pytorch}., Extension of a CVPR 2019 paper. Dataset and PyTorch Code are released. arXiv admin note: substantial text overlap with arXiv:1904.04441

The objective of this study was to investigate the clinical manifestations and pathologic appearances of the submandibular gland (SMG) in Stevens-Johnson syndrome (SJS).Patients with autologous transplantation of SMG for treatment of severe dry eye between March 1998 and May 2018 were divided into the SJS group (70 cases) and non-SJS group (50 cases) according to the history of SJS. The SMG weight and computed tomography volume and salivary flow rate were measured. The concentration index and secretion index were estimated using scintigraphy with technetium-99m-pertechnetate. Histopathology studies of SMG tissues were conducted, and the acini parameters were measured using a digital image analyzer.A decreased computed tomography volume and weight was observed in 48.57% the SJS group and 2% in the non-SJS group (P.01). The rest whole, acid-stimulated whole, and SMG rest salivary flow rates decreased in the SJS group (P.05). The normal SMG concentration index (37.5% vs 96.67%, P.001) and secretion index (35% vs 96.67%, P.001) rates were lower in the SJS group than in the non-SJS group. The glandular parenchyma was reduced, the acinar space was widened, and the fat content was increased in the SJS group.SMG atrophic and degenerative changes occurred in the SJS group, with a decrease in salivary secretion function in more than half of the patients.

Enabling charging capacity abnormality diagnosis is essential for ensuring battery operation safety in electric vehicle (EV) applications. In this paper, a data-driven method is proposed for battery charging capacity diagnosis based on massive real-world EV operating data. Using the charging rate, temperature, State-of-Charge and accumulated driving mileage as the inputs, a tree-based prediction model is developed with a polynomial feature combination used for model training. A statistics-based method is then used to diagnose battery charging capacity abnormity by analyzing the error distribution of large sets of data. The proposed tree-based prediction model is compared with other state-of-the-art methods and is shown to have the highest prediction accuracy. The holistic diagnosis scheme is verified using unseen data.

The volatile combustion and NO release characteristics of pulverized fuel (PF) flames in an optical flat-flame entrained-flow reactor were investigated by CH*/NO* chemiluminescence imaging. Pyrolyzed bituminous (PB) char with an ultralow volatile content (Vdaf≈10%) and a comparative coal sample of Shenhua (SH) bituminous coal with a high volatile content (Vdaf = 34.2%) were studied under different ambient O2 mole fractions (10%∼30%). The initiation of the combustion region of PB char occurs later than SH bituminous coal, owing to its low volatile contents, a slow devolatilization rate, and homogeneous-heterogeneous ignition/combustion mode. By measuring probed solid and gas samples, the conversion ratios of fuel-N to NO and the releasing ratios of volatile-N/char-N were determined in ignition process. Semi-quantitative NO release integral intensity and volatile combustion intensity results were obtained by integrating the NO* or CH* chemiluminescence intensity in the corresponding reaction region. The volatile combustion and NO release show good synchronous for Shenhua bituminous coal, which means that the NO releasing originated predominantly and owned higher conversion ratio from the volatile-N in the ignition region. The NO release integral intensity and NO emission of SH bituminous coal decreased with O2 concentration increased from 20% to 30%, which indicates that the enriched-O2 environment is beneficial for high volatiles bituminous to decrease NOx emission in ignition stage. For PB char, NO releasing originated predominantly from the char heterogeneous reactions of char-N in its ignition and combustion late stage.

Trimethylamine-N-oxide (TMAO), a gut microbiota-produced metabolite, is accumulated in chronic kidney disease (CKD) patients. It is well known to contribute to CKD-related cardiovascular complications. However, the effect of TMAO on peritoneal dialysis (PD)-related peritonitis remains largely unknown. Here, we demonstrate that serum concentrations of TMAO were positively correlated with C-reactive protein levels, and the appearance rate of dialysate IL-6 and PAI-1, in PD patients. During the follow-up period of 28.3 ± 8.0 months, patients with higher TMAO levels (≥50 μM) had a higher risk of new-onset peritonitis (HR, 3.60; 95%CI, 1.18-10.99; P=0.025) after adjusting for sex, age, diabetes, PD duration, BUN, rGFR, C-reactive protein, BMI and β2-M. In CKD rat models, TMAO significantly promoted peritoneal dialysate-induced inflammatory cell infiltration, inflammatory cytokines production in the peritoneum. In vitro study revealed that TMAO directly induced primary peritoneal mesothelial cell necrosis, together with increased production of pro-inflammatory cytokines including CCL2, TNF-α, IL-6, and IL-1β. In addition, TMAO significantly increased TNF-α-induced P-selectin production in mesothelial cells, as well as high glucose-induced TNF-α and CCL2 expression in endothelial cells. In conclusion, our data demonstrate that higher levels of TMAO exacerbate peritoneal inflammation and might be a risk factor of incidence of peritonitis in PD patients.

This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

Objectives: The exact characteristics of a coronavirus disease 2019 (COVID-19) outbreak that trigger public health interventions are poorly defined. The aim of this study was to assess the critical timing and extent of public health interventions to contain COVID-19 outbreaks in Australia. Methods: A practical model was developed using existing epidemic data in Australia. The effective combinations of public health interventions and the critical number of daily cases for intervention commencement under various scenarios of changes in transmissibility of new variants and vaccination coverage were quantified. Results: In the past COVID-19 outbreaks in four Australian states, the number of reported cases on the day that interventions commenced strongly predicted the size and duration of the outbreaks. In the early phase of an outbreak, containing a wildtype-dominant epidemic to a low level (≤10 cases/day) would require effective combinations of social distancing and face mask use interventions to be commenced before the number of daily reported cases reaches six. Containing an Alpha-dominant epidemic would require more stringent interventions that commence earlier. For the Delta variant, public health interventions alone would not contain the epidemic unless the vaccination coverage was ≥70%. Conclusions: This study highlights the importance of early and decisive action in the initial phase of an outbreak. Vaccination is essential for containing variants.

BACKGROUND: Axillary imaging has been earmarked to forecast high nodal burden [≥3 metastatic axillary lymph nodes (ALN)] instead of lymph node metastasis since the Z0011 trial period. We aimed to ascertain the possibility of utilising quantitative shear wave elastography (SWE) to forecast high nodal burden in invasive breast cancer (IBC). METHODS: In our hospital, 324 patients with clinical T1-T2N0 IBC who underwent surgery from June 2020 to October 2020 were analyzed retrospectively. A total of 273 patients (84.3%) were categorized as having a limited nodal burden, while 51 patients (15.7%) had a high nodal burden. The two groups were compared in terms of clinicopathological traits, ultrasonic features, and SWE values. The diagnostic performance for prediction of high nodal burden with the optimal cutoff values was drawn by SWE value. RESULTS: The optimal cutoff values for forecasting high nodal burden were as demonstrated: 119.52 kPa for tumor Emax, 97.31 kPa for tumor Emean, 19.38 for tumor Esd, 26.22 kPa for ALN Emax, 19.79 kPa for ALN Emean, 2.32 for ALN Eratio, 3.34 for ALN Esd. Combined with the ratings of sensitivity and specificity, ALN Emax could be chosen as the optimal index if the best diagnostic achievement was contemplated (AUC: 0.856; 95% CI: 0.802–0.909). CONCLUSIONS: An Emax cutoff 26.22 kPa of ALN, 72% of women with a high nodal burden of axillary disease would be detected, but if used for clinical decision making, 13% of women with a limited nodal burden disease would be potentially over treated. This data can allow us to appropriately ascertain this subgroup and can be used as one of the therapeutic implementation resources for patient decision support.

Ultrasonography of the breast mass is an important imaging technology for diagnosing breast cancer. In China, ultrasound equipment is widely used in medical institutions. Patients obtain a report with highlighted ultrasonography images through an initial clinical screening. However, analyzing these images manually is highly subjective for the variation in the clinical competence of doctors, resulting in poor consistency and low sensitivity. In this study, an automated breast cancer diagnosis system is developed to increase diagnostic accuracy. The system is deployed on mobile phones, takes a photo of the ultrasound report as input and performs diagnosis on each image. The developed system consists of three subsystems. The first subsystem is to reduce noise in the taken photos, reconstructing high-quality images. We develop the first subsystem based on the frameworks of stacked denoising autoencoders and generative adversarial networks. The second subsystem is to classify images into malignant and non-malignant; to extract high-level features from the images, deep convolutional neural networks are employed. The third subsystem is to detect anomalies in model performances, reducing false negative rates. Generative adversarial networks are utilized to distinguish false negative samples from true negative samples. 18225 breast ultrasonography images and 2416 ultrasound reports are collected to train and evaluate the system. Experimental results show that the performance of our system is comparable to that of human experts. It is believed that this is the first system for breast cancer diagnosis deployed on mobile phones. The developed system is integrated with a cloud computing platform and accessible online to aid in the initial screening and diagnosis of breast cancer, thereby promoting earlier treatment, reducing the morbidity and mortality.

Epigenetics, including histone modifications, play a significant role in central nervous system diseases, but the underlying mechanism remains to be elucidated. The aim of this study was to evaluate the role of H3K27me3 in regulating transcriptomic and pathogenic mechanisms following global ischemic stroke. Here, we found that in vivo ischemic/reperfusion (I/R) injury induced marked upregulation of H3K27me3 in the hippocampus. The administration of GSK-126 to rat brains decreased the levels of H3K27me3 in the hippocampus and reduced neuronal apoptosis after experimental stroke. Furthermore, ChIP-seq data demonstrated that the primary role of GSK-126 in the ischemic brain is to reduce H3K27me3 enrichment, mediating negative regulation of the execution phase of apoptosis and the MAPK signaling pathway. Further study suggested that the protective role of GSK-126 in ischemic rats was antagonized by U0126, an inhibitor of ERK1/2. Collectively, we demonstrated the potential of H3K27me3 as a novel stroke therapeutic target, and GSK-126 exerted a neuroprotective function in ischemic brain injury, which might be associated with activation of the MAPK/ERK pathway. Graphical Abstract

Currently, in the spaceborne synthetic aperture radar (SAR) community, the sliding spotlight mode has been widely applied to achieve high-resolution imaging. However, limited by the system pulse repetition frequency (PRF), it is usually impossible to realize a large-scale coverage at the same time. The technique of azimuth multichannel alleviates the PRF restriction by receiving additional spatial samples proportional to the number of Rx channels. A system combining the technique of azimuth multichannel with sliding spotlight SAR is able to achieve ultrahigh-resolution and wide-swath imaging simultaneously. In the advanced multichannel sliding spotlight mode, some problems may occur. First, azimuth beam progressive steering leads to an enlarged Doppler bandwidth over N· PRF, and conventional azimuth multichannel reconstruction algorithms developed for the stripmap mode are, thus, not directly suitable for the sliding spotlight mode. Second, the phase characteristics of multichannel sliding spotlight SAR is different from that of multichannel stripmap SAR due to the time variation of the system Doppler centroid. Third, processing of spaceborne multichannel sliding spotlight SAR experimental data shows that the convolution function used in the full-aperture reconstruction method is closely related to the antenna phase configuration strategy of the phased array antenna system. Therefore, an improved processing frame is required for the multichannel sliding spotlight SAR, reexamining the validity of assumptions, such as the stop-go-stop approximation and that of a noncurved trajectory during the relatively long aperture time. Recently, as an exploratory study, a dual-channel sliding spotlight experiment was conducted with the Gaofen-3 (GF-3) SAR. Two experimental data sets were acquired with different PRFs. In this article, the GF-3 payload and the experiment design are introduced, and a complete multichannel sliding spotlight SAR data processing chain is provided. The first imaging results of spaceborne multichannel sliding spotlight SAR are demonstrated.

The deep learning-based method has shown promising competence in image classification. Its success can be attributed to the ability to learn discriminative feature representation given plenty of labeled data. However, in real-hyperspectral image (HSI) classification applications, since pixel labeling is difficult and costly, the labels we can obtain within an HSI are always limited and noisy (i.e., inaccurate), which consequently causes overfitting of the deep learning-based method. To address this problem, we propose a novel unified deep learning network to employ both labeled and unlabeled data for training, with which the unsupervised structure knowledge, e.g., intracluster similarity and intercluster dissimilarity, inherently contained in those unlabeled data can be exploited to boost the conventional supervised classification. Specifically, we first explore the unsupervised structure knowledge in unlabeled data via a clustering method and formulate a supervised clustering task on those data with the obtained cluster labels. Then, we propose a multitask network to jointly address both the conventional classification task and the formulated supervised clustering task. With a shared feature extraction module and a high-level feature fusion module, the unsupervised structure knowledge contained in unlabeled data can be effectively introduced into the classification task, which is beneficial to learn a more discriminative feature representation and, thus, well mitigates the overfitting problem and improves the classification results. Experimental results on three data sets demonstrate the proposed method can effectively label the unlabeled data within an HSI, especially when the training labels are limited and noisy.

Automatic vessel segmentation in the fundus images plays an important role in the screening, diagnosis, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. However, due to the limited well-annotated data, varying size of vessels, and intricate vessel structures, retinal vessel segmentation has become a long-standing challenge. In this paper, a novel deep learning model called AACA-MLA-D-UNet is proposed to fully utilize the low-level detailed information and the complementary information encoded in different layers to accurately distinguish the vessels from the background with low model complexity. The architecture of the proposed model is based on U-Net, and the dropout dense block is proposed to preserve maximum vessel information between convolution layers and mitigate the over-fitting problem. The adaptive atrous channel attention module is embedded in the contracting path to sort the importance of each feature channel automatically. After that, the multi-level attention module is proposed to integrate the multi-level features extracted from the expanding path, and use them to refine the features at each individual layer via attention mechanism. The proposed method has been validated on the three publicly available databases, i.e. the DRIVE, STARE, and CHASE _ DB1. The experimental results demonstrate that the proposed method can achieve better or comparable performance on retinal vessel segmentation with lower model complexity. Furthermore, the proposed method can also deal with some challenging cases and has strong generalization ability.

We combined our generalized energy-based fragmentation (GEBF) approach and machine learning (ML) technique to construct quantum mechanics (QM) quality force fields for proteins. In our scheme, the training sets for a protein are only constructed from its small subsystems, which capture all short-range interactions in the target system. The energy of a given protein is expressed as the summation of atomic contributions from QM calculations of various subsystems, corrected by long-range Coulomb and van der Waals interactions. With the Gaussian approximation potential (GAP) method, our protocol can automatically generate training sets with high efficiency. To facilitate the construction of training sets for proteins, we store all trained subsystem data in a library. If subsystems in the library are detected in a new protein, corresponding datasets can be directly reused as a part of the training set on this new protein. With two polypeptides, 4ZNN and 1XQ8 segment, as examples, the energies and forces predicted by GEBF-GAP are in good agreement with those from conventional QM calculations, and dihedral angle distributions from GEBF-GAP molecular dynamics (MD) simulations can also well reproduce those from ab initio MD simulations. In addition, with the training set generated from GEBF-GAP, we also demonstrate that GEBF-ML force fields constructed by neural network (NN) methods can also show QM quality. Therefore, the present work provides an efficient and systematic way to build QM quality force fields for biological systems.

The higher bandwidth inverse synthetic aperture radar (ISAR) can obtain the higher resolution radar images, which can provide more target information and help improve radar target detection and recognition. It is essential to study how to achieve a precise high-resolution (HR) ISAR image utilizing limited measurement echoes. The existing neural-network-based ISAR imaging methods extract features only from limited measurement echoes, and the common features in HR ISAR images are not utilized sufficiently, which limits the imaging performance improvement. Moreover, in their loss functions, there are no explicit constraints on the correct recovery of strong scattering points, which are important in reflecting the target characteristics. In this letter, we propose a conditional probabilistic generative model to achieve the HR ISAR imaging. By optimizing the well-designed Kullback-Leibler (KL) divergence between conditional prior and approximate posterior probability distribution in the loss function, the common features contained in training HR radar images can be learned, and a suitable prior probability distribution for the latent variable can be obtained. To accurately recover the positions and relative amplitudes of strong scattering points, we blend a similarity measurement that is sensitive to the large values' locations in a vector with the adversarial loss. Both visual and numerical results of extensive experiments prove that the proposed model can obtain enhanced effectiveness and efficiency compared with some counterparts.

Digital beamforming (DBF) synthetic aperture radar (DBSAR) is a promising candidate to overcome the constraint of minimum antenna area in the traditional single-channel synthetic aperture radar (SAR) system for achieving high-resolution and wide-swath (HRWS) image. On reception, DBF technology in elevation can significantly improve the system performance such as its sensitivity, ambiguity level, and the output signal-to-noise ratio (SNR) by Scan-On-Receive (SCORE). However, the inevitable channel mismatch will result in a beam-pointing error and further deteriorate the output SNR in the final SAR images. In this letter, the configuration of the 16-channel DBF-SAR system is described. A scheme of semi-physical simulation testbed for 1-D point target imaging is designed. Based on this, a detailed internal calibration technique combined with DBSAR mode is proposed to calibrate the channel mismatch and thus improve the SAR image quality. The real 1-D raw data experiment is performed to validate the proposed scheme. Finally, the imaging results from the practical flight experiment are shown and analyzed.

Extracting the flying airplane from the satellite video has received a great deal of attention in remote sensing applications. Nevertheless, the challenge arises in two aspects: the limited spatial resolution of space-borne imaging devices, and the small size of the airplane with low contrast to the background. This paper presents a novel spatially accurate and temporally consistent model for the foreground airplane extraction. Specifically, each frame is decomposed as a linear combination of the airplane and background with a kind of mixture ratios. This enables more accurate spatial representation of the small airplane of low resolution. Then, the airplanes from different frames obey the common mixture ratios based on the sub-pixel motion estimation. This enables the temporally consistent extraction through frames. The effectiveness of our method is demonstrated by the qualitative and quantitative evaluation on both synthetic and real data.

Laparoscopic-assisted radical vaginal hysterectomy (LARVH) and abdominal radical hysterectomy (ARH) are commonly used for cervical cancer treatment. However, the clinical application of LARVH versus ARH in treating cervical cancer remains controversial.To investigate the efficacy of LARVH versus ARH in treating cervical cancer via comparing several inductors by pooling related studies.Eligible articles from PubMed, Embase, and the Cochrane library were screened using established search terms. Consecutive variables were pooled using weighted mean difference (WMD) and 95% confidence interval (CI). Categorical variables were pooled using odds ratio (OR) and 95% CI.A total of 13 articles were included in this meta-analysis, comprising 579 patients who underwent LARVH and 810 who underwent ARH. LARVH required a longer operation time (WMD = 50.97, 95% CI: 38.34, 63.59, p0.001) than ARH. However, compared to patients who underwent ARH, those who underwent LARVH had less bleeding volume (WMD =Owing to these reasons, LARVH has more application prospects than ARH in treating cervical cancer.

Automatic skin lesion segmentation is one of the most important tasks for computer-aided diagnosis of skin cancer. Although many deep learning-based methods have been proposed, most of them do not pay attention to the boundary information that is critical for achieving more accurate segmentation. In this work, an end-to-end deep edge convolutional neural network (DE-Net) based on the encoder-decoder structure is proposed to pay more attention to the skin lesion boundaries. In the decoder process of the proposed DE-Net, an edge information guided module (EIGM) is designed to introduce the boundary information of the original image and fuse it with different levels of contextual information. Furthermore, to generate more accurate skin lesion boundaries without losing the entire performance and guide the module to highlight more boundary information, we propose an entirety-center-edge (ECE) loss function that can further optimize the boundary details on the basis of the necessary segmentation results. The proposed loss function is backbone-independent and has better performance than other commonly used loss functions in segmentation tasks. In the experiment, the ISIC-2017 dataset is employed to evaluate the effectiveness of the proposed method. It achieves the performance of 0.8792 and 0.8053 on the metrics Dice coefficients and Jaccard index, respectively. Furthermore, we also evaluate the proposed method on ISIC-2016 and PH2 datasets to demonstrate its generalizability. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods on all these three datasets and has strong generalizability.

The main difficulty of synthetic aperture radar (SAR)-optical image matching or registration lies in the significant heterogeneous characteristics introduced by the different imaging mechanisms between SAR and optical images. Instead of directly using the raw image pair, transforming the pair into a feature domain, where they have homogeneous feature representation, is believed more effective. Inspired by image segmentation, we develop an end-to-end deep learning model for the SAR-optical matching, based on a siamese U-net with a fast Fourier transform (FFT) correlation layer. First, the siamese U-net with sharing weights extracts the feature maps of the SAR and optical images and projects the heterogeneous images into a homogeneous space. Then, the two feature maps are cross-correlated or normalized cross-correlated by the FFT layer and a similarity heatmap is obtained. Finally, the heatmap is send into a softmax2d classifier to determine the best matching, and thus matching is converted into classification. The nonlinear mapping capability of deep learning can well tackle the intensity variation across the different imaging modals; the encoder-decoder architecture with skip connections in the U-net can take full advantage of the global information and simultaneously preserve the local resolution and position information and thus guarantees high accuracy and robustness; besides, the FFT correlation is helpful for the efficiency improvement and training with large image pairs. Experiments show that the proposed method can achieve a pixel-level matching error.

Hyperspectral image (HSI) contains an abundant spatial structure that can be embedded into feature extraction (FE) or classifier (CL) components for pixelwise classification enhancement. Although some existing works have exploited some simple spatial structures (e.g., local similarity) to enhance either the FE or CL component, few of them consider the latent manifold structure and how to simultaneously embed the manifold structure into both components seamlessly. Thus, their performance is still limited, especially in cases with limited or noisy training samples. To solve both problems with one stone, we present a novel dual-level deep spatial manifold representation (SMR) network for HSI classification, which consists of two kinds of blocks: an SMR-based FE block and an SMR-based CL block. In both blocks, graph convolution is utilized to adaptively model the latent manifold structure lying in each local spatial area. The difference is that the former block condenses the SMR in deep feature space to form the representation for each center pixel, while the later block leverages the SMR to propagate the label information of other pixels within the local area to the center one. To train the network well, we impose an unsupervised information loss on unlabeled samples and a supervised cross-entropy loss on the labeled samples for joint learning, which empowers the network to utilize sufficient samples for SMR learning. Extensive experiments on two benchmark HSI data set demonstrate the efficacy of the proposed method in terms of pixelwise classification, especially in the cases with limited or noisy training samples.

Pulse position modulation (PPM) is a key technology for deep space optical communication (DSOC) system to improve the data transmission rate. To reach optimum rate, the modulation parameters of PPM are required to be analyzed. The rate can be improved by choosing appropriate modulation orders and the initial pulse width. In this paper, the meteorological statistical data of our experimental location are analyzed. Then, the analysis formula of the cloud scattering and atmospheric turbulence influence on PPM communication rate are given. The cloud particles are measured through aircraft detector, simulation analysis is carried out by using the measured data of aircraft detector. Finally, the influence on the communication rate of different cloud physical thickness are analyzed.

Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone-cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone-cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone-cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone-cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone-cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone-cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, ``GKT-JSVAAS-GKT.'' This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

In this article, a novel design method of dual-band and dual-state (DBDS) Doherty power amplifier (DPA) is proposed. Considering the actual parasitic parameters, the DPA can be designed by using the impedance transformation of saturation and back-off points at dual frequency bands of DBDS as well as second harmonic matching. The DBDS DPA for dual-band (DB) 5G application, i.e., 3.4 and 4.9 GHz band are designed and implemented, for the first time, by using metal-integrated and substrate-integrated suspended line (MI-SISL) technology, which has advantages of high performance and self-packaging. The fabricated DBDS DPA achieves the measured 44 and 43.2 dBm saturated power at 3.4 and 4.9 GHz bands, respectively. For the 3.4 and 4.9 GHz bands, the measured drain efficiency (DE) is 70.7% and 70.4% at peak power levels and 38% and 42% at a 6-dB output back-off, respectively. When driven by a 20- and 100-MHz modulated signal with 6-dB peak-to-average power ratio (PAPR), the adjacent channel leakage ratio is better than -27 dBc at operating frequencies. The average output power of 38 and 37 dBm with an average efficiency of 37.9% and 40.9% is achieved at 3.4 and 4.9 GHz, respectively.

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms baseline methods by 10 percent on Success Rate weighted by Path Length (SPL) and achieves the state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore and adapt to unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7 to 11.7 percent).

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems. In this paper, we propose a unified framework for service provisioning in RAN slicing with aim of maximizing resource utilization while guaranteeing QoS of users. The framework consists of two steps. The first step is to identify a set of slice users whose QoS can be satisfied simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve significant performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.

Background: Cardiovascular disease is the leading cause of death in both developed and developing countries. Di'ao Xinxuekang (DAXXK) is a pure Chinese medicine herbal preparation refined from dioscin extracted from the roots of Dioscorea panthaica Prain et Burk and Diosorea nipponica Makino. Objective: To evaluate the application of DAXXK in Cardiovascular disease. Methods: : We searched and summarized all the studies on DAXXK and Cardiovascular disease in pumend, Google, and CNKI. Results: Modern pharmacological studies have shown that DAXXK has pharmacological effects such as dilating blood vessels, lowering blood pressure and cardiac load, improving hemodynamics, lowering blood lipids and anti-platelet aggregation, and is widely used for the therapy of various kinds of cardiovascular diseases, including hypertension, atherosclerosis, coronary heart disease (CHD), angina pectoris (AP) and myocardial infarction. We provide an overview of the clinical efficacy, molecular mechanisms, safety and therapeutic potential of DAXXK in the treatment of cardiovascular disease, aiming to provide clues and evidence for clinical decision-making. Conclusion: DAXXK exerts cardiovascular protection by regulating a variety of cardiovascular disease-related signaling pathways.

The outstand flanges of aluminium alloy angles are sensitive to local buckling due to the low elastic modulus of material. To improve the sectional buckling stress, lips could be introduced to improve the stability behaviour of the outstand flanges. This paper presents an equilibrium approach and a simplified energy method for the local and distortional buckling of extruded aluminium alloy lipped angle columns. Closed form solutions for local buckling and distortional buckling are proposed. The equivalent flexural rigidity and torsional rigidity of the lip are two main factors affecting the sectional buckling behaviour. As distinct from double-sided symmetrical edge stiffeners, the bending of a single-sided edge stiffener can lead to in-plane compressive deformation of the adjacent flange. Based on the edge-stiffened flange model, the equivalent flexural rigidity of the lip is obtained from finite element (FE) analysis. To validate the proposed analytical methods, the elastic buckling coefficients of lipped angles with varying dimensions are obtained from eigenvalue buckling analysis. Lips of different thicknesses are considered to study the effect of torsional rigidity. Comparing with FE results, the proposed equilibrium approach shows excellent accuracy for both local and distortional buckling stresses. The closed form solution given by energy method can also well predict the sectional buckling stress. Finally, parametric studies are carried out to further investigate the effects of flexural and torsional rigidities of lips and the slenderness ratios of flanges on the elastic buckling behaviour. Some design remarks are concluded to provide valuable reference for the application of extruded aluminium alloy lipped angles in practice.