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This paper systematically describes the technical principles, evaluation indicators, system forms and research progress of air-side evaporative cooling air conditioning systems, water-side evaporative cooling air conditioning systems and freon-side evaporative cold coagulation heat air conditioning systems of Data center. In order to reduce the energy consumption of the refrigeration and air-conditioning system in the Data center, the application conditions and scenarios of the different forms of evaporative cooling air-conditioning systems should be considered comprehensively. Therefore, it is very important that the renewable energy-dry air can be used to the greatest extent. These efforts would contribute to China's 2030 "Carbon Peak" and 2060 "Carbon Neutral."

Objective To assess the concordance of tumour grade in specimens obtained from diagnostic cystoscopic biopsy and transurethral resection of bladder tumour (TURBT) and explore the risk factors of upgrading. Methods The medical records of 205 outpatients who underwent diagnostic cystoscopic biopsy before initial TURBT were retrospectively reviewed. Comparative analysis of the tumour grade of biopsy and operation specimens was performed. Tumour grade changing from low-grade (LG) to high-grade (HG) with or without variant histology was defined as upgrading. Univariate and multivariate logistic regression analyses were performed to identify the risk factors of upgrading. Results For the 205 patients, the concordance of tumour grade between biopsy and operation was 0.639. The concordance for patients who were preoperatively diagnosed with LG and HG was 0.504 and 0.912, respectively. Univariate and multivariate logistic regression analyses showed that older age, tumour multifocality, high neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte-to-monocyte ratio (LMR) were significantly associated with upgrading (odds ratio [OR] ranging from 0.412 to 4.364). The area under the curve of the different multivariate models was improved from 0.752 to 0.821, and decision curve analysis demonstrated a high net benefit when NLR, LMR, and PLR were added. Moreover, the nomogram was well calibrated. Conclusions Diagnostic cystoscopic biopsy may not accurately represent the true grade of new bladder cancer, especially for outpatients with LG. Moreover, older age, tumour multifocality, high NLR, PLR, and low LMR are risk factors of upgrading, and systemic inflammatory markers improve the predictive ability.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This paper presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

Lung cancer is the top reason for cancer-related deaths worldwide. The 5-year overall survival rate of lung cancer is approximately 20 % due to the delayed diagnosis and low response rate to regular treatments. Microbiota, both host-microbiota and alien pathogenic microbiota, have been investigated to be involved in a complicated and contradictory relationship with lung cancer initiation, treatments, and prognosis. Disorders of certain host-microbiota and pathogen infection are associated with the risk of lung cancers based on epidemiological evidence, and antibiotics (ATBs) could dramatically impair anti-cancer treatment efficacy, including chemotherapy and immunotherapy. Moreover, probiotics and microbe-mediated drugs are potential approaches to enhance regular anti-tumor treatments. Therefore, the knowledge of the complex dual effect of microbes on lung cancer is beneficial to take their essence and remove their dross. This review offers insight into the current trends and advancements in microbiota or microbial components related to lung cancer.

The present study aimed to explore the efficacy and safety of roxadustat in patients with renal anemia and erythropoietin (EPO) hyporesponsive who are receiving continuous ambulatory peritoneal dialysis (CAPD).This is a single center, before and after treatment, self-controlled study; 55 CAPD patients with renal anemia and EPO hyporesponsiveness were enrolled. The main follow-up parameters included routine blood, liver and kidney function, electrolyte, blood lipid, high-sensitivity C-reactive protein, and iron tests. Serum samples were used to determine interleukin-6 and tumor necrosis factor-α levels via enzyme linked immunosorbent assay. The Modified Quantitative Subjective Global Assessment Score and Malnutrition-Inflammation Score before and after treatment, and adverse events during treatment were recorded. The follow-up observation time was 12 weeks. Preliminary data 12-24 weeks before the enrollment as well as post-follow-up data at 36 weeks were also collected.Fifty patients completed the 12-week follow-up. The hemoglobin levels were 8.0 ± 1.2 g/dL at baseline and 11.2 ± 2.0 g/dL after 12 weeks of roxadustat treatment. The hemoglobin increases at all measured time points and was statistically significant compared with the baseline value (P .05). The overall hemoglobin response rate (hemoglobin increase ≥ 1.0 g/dL) was 80%, and 50% of the patients reached the hemoglobin target (hemoglobin ≥ 11.0 g/dL) at 12 weeks. Transferrin was higher at 12 weeks (2.2 ± 0.5 g/L) than at baseline (1.7 ± 0.5 g/L) (P .05), while serum ferritin levels slightly decreased compared with the baseline value (P .05). The median high-sensitivity C-reactive protein level and other inflammation-related indicators, such as white blood cell counts, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, interleukin-6, and tumor necrosis factor-α, were not significantly different from their baseline values. Nutrition-related biochemical indices such as albumin, creatinine, and blood lipids were also not significantly changed. The Modified Quantitative Subjective Global Assessment Score and Malnutrition-Inflammation Score were slightly lower at 12 weeks than at baseline. No serious adverse events were observed during the follow-up period. Post-follow-up data revealed a maintained hemoglobin level in patients who remained on roxadustat treatment while those switched back to EPO treatment after 12 weeks resulted in a decreased hemoglobin at 36 weeks.In patients with EPO hyporesponsiveness on CAPD, roxadustat can efficiently and safely improve anemia and nutritional status without promoting inflammation.

Crowdsourced software testing (short for crowdtesting) is a special type of crowdsourcing. It requires that crowdworkers master appropriate skill-sets and commit significant effort for completing a task. Abundant uncertainty may arise during a crowdtesting process due to imperfect information between the task requester and crowdworkers. For example, a worker frequently chooses tasks in an ad hoc manner in crowdtesting context, and an inappropriate task selection may lead to the worker's failing to detect any bugs, and significant testing effort unpaid and wasted. Recent studies have explored methods for supporting task requesters to make informed decisions on task pricing, worker recommendation, and so on. Unfortunately, very few study offers decision making support from the crowdworkers' perspectives. We motivate this study through a pilot study, revealing the large portion (74\%) of unpaid crowdworkers' effort due to the inappropriate task choice. Drawn from our previous work on context-aware crowdworker recommendations, we advocate a more effective alternative to manual task selection would be to provide contextualized and personalized task recommendation considering the diverse distribution of worker preference and expertise, with objectives to increase their winning chances and to potentially reduce the frequency of unpaid crowd work. This paper proposes a context-aware personalized task recommendation approach PTRec, consisting of a testing context model and a learning-based task recommendation model to aid dynamic worker decision in selecting crowdtesting tasks. The testing context model is constructed in two perspectives, i.e., process context and resource context, to capture the in-process progress-oriented information and crowdworkers' characteristics respectively. Built on top of this context model, the learning-based task recommendation model extracts 60 features automatically, and employs random forest learner to generate dynamic and personalized task recommendation which matches workers' expertise and interest. The evaluation is conducted on 636 crowdtesting tasks involving 2,404 crowdworkers from one of the largest crowdtesting platforms, and results show the potential in recommending proper tasks to workers so as to improve bug detection efficiency and increase their monetary earnings.

This article reviews advanced process and electron device technology of integrated circuits, including recent featuring progress and potential solutions for future development. In 5 years, for pushing the performance of fin field-effect transistors (FinFET) to its limitations, several processes and device boosters are provided. Then, the three-dimensional (3D) integration schemes with alternative materials and device architectures will pave paths for future technology evolution. Finally, it could be concluded that Moore's law will undoubtedly continue in the next 15 years.

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

In recent years, the adaptive cruise control (ACC) system has become widely adopted. The vehicle-positioning system plays an important role in automotive platoon driving and can provide vehicle-position information through vehicle-to-vehicle communication. However, the acquired vehicle-position information often presents uncertainties given the existence of variations in the traffic environment and of noise in the vehicle-positioning system. These uncertainties, in turn, influence the car-following performance of a vehicle platoon. In this study, we employ the desired safety margin (DSM) model, an ACC control strategy, to investigate the influence of the uncertainty level of vehicle-position information (ULVPI) on string stability and car-following safety. The stability criterion of the DSM model with ULVPI is derived through linear stability theory. Theoretical analysis results are verified by numerical simulations. Analytical results indicate that a negative ULVPI value can expand the stable region and improve string stability and that a positive ULVPI value can reduce delay time. Moreover, a negative ULVPI value can improve car-following safety during the stopping and evolution processes, whereas a positive ULVPI values can increase the safety margins of vehicles during the starting process. In the car-following process, a negative ULVPI value can improve car-following safety and reduce rear-end collision risk. The variation in ULVPI values can improve string stability and reduce the risk of rear-end collisions when mean and standard deviation are reasonable. Therefore, the improvement of a vehicle's dynamic performance, string stability, and safety relies on the effect of ULVPI in different traffic scenarios. These results are useful in designing different control strategies that stabilize traffic flow and improve traffic safety for vehicles with ACC systems.

Recognizing traffic command gestures with high accuracy and quick response at a low computational cost is a requisite for driver assistance or autonomous driving. However, it has been understudied for a long time. Existing research takes advantage of increasing development in human action recognition but pays little attention to onboard conditions. In this article, we propose a simple but effective recognition model based on human upper-body geometric features and a long short-term memory (LSTM) network. The handcrafted geometric features can easily be calculated with estimated 2-D human keypoints at a low computational cost but are discriminative and sufficient in classification. Offline and online inferences are implemented to comprehensively evaluate the proposed model. For the sake of robustness required in the automotive domain, dual voting is designed to filter the output in online inference. On the recently published Chinese traffic police gesture (CTPG) dataset, the presented approach is the best with a remarkable improvement of approximately 8% compared to previous LSTM-based methods with handcrafted spatial features and is competitive with advanced GCN-based deep learning methods. The tradeoff pattern is explored to demonstrate how accuracy and response time alter with different training and inference strategies so that a balanced setup can be manually chosen under various application scenarios. Field tests are also carried out with an experimental vehicle, and the results uncover the present gap between research and practical application to some extent, moving a step closer to real-life traffic command gesture recognition.

With the increase of mining depth of coal seam, the impact pressure has severely restricted the coal mine safe and efficient mining, drilling pressure is a kind of through the construction of borehole in the coal seam, achieve the purpose of partial discharge, pressure relief effect is very obvious, the larger the aperture effect of pressure, the better, by comparing the 150 mm in diameter and 300 mm diameter CT detection construction surrounding rock displacement change. Compare the pressure relief effect of 150 mm drilling and 300 mm drilling.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80% while guaranteeing content freshness in heavily loaded ICVNs.

An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input-output sequence characteristics. Then,an E-Seq2Seq approach with a multiple-encoder-decoder architecture and a fully connected extension layer is proposed. On this basis,the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct the deep learning model,and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.

In view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this paper, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of method consists of three core components. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster. We implement the experiments and the results show that our method achieves 92.3%, 91.2%, and 3679.2 kWh respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

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.

The association between the estimated glomerular filtration rate (eGFR) and atherosclerotic cardiovascular disease (ASCVD) risk is unknown. We aimed to evaluate whether eGFR can be used as a predictor in ASCVD risk assessment.Using baseline data from 28,187 participants from Shanghai Suburban Adult Cohort and Biobank study, we adopted Pooled Cohort Equations (PCEs) and Prediction for ASCVD Risk in China (China-PAR) to estimate 10-year ASCVD risk. Multivariate logistic regression was used to analyze the relationship between 10-year ASCVD risk and eGFR. The receiver operating characteristic (ROC) curve was used to evaluate predictive value of eGFR for 10-year high ASCVD risk. Compared with normal eGFR, both men and women with reduced eGFR had a higher prevalence of ASCVD risk factors. With the decrease of eGFR level, the median of 10-year ASCVD risk gradually increased. For men, the adjusted odds ratios (95% confidence interval (CI)) of 10-year high ASCVD risk by PCEs associated with eGFR (60-74 and60 mL/min/1.73 meGFR may be an important risk factor in predicting and stratifying ASCVD risk. Consideration should be given to integrating eGFR into existing risk assessment tools to improve predictive performance.

Keloid scarring is one of the most common types of pathological scarring. Keloid scars that fail to heal can affect a person's physical and psychological function by causing pain, pruritus, contractures, and cosmetic disfigurement. Silicone gel sheeting (SGS) is made from medical-grade silicone reinforced with a silicone membrane backing and is one of the most commonly used treatments for keloid scars. However, there is no up-to-date systematic review assessing the effectiveness of SGS for keloid scars. A clear and rigorous review of current evidence is required to guide clinicians, healthcare managers and people with keloid scarring.To assess the effectiveness of silicone gel sheeting for the treatment of keloid scars compared with standard care or other therapies.We used standard, extensive Cochrane search methods. The latest search date was December 2021.We included randomised controlled trials (RCTs) that recruited people with any keloid scars and assessed the effectiveness of SGS.Two review authors independently performed study selection, risk of bias assessment, data extraction and GRADE assessment of the certainty of evidence. We resolved initial disagreements by discussion, or by consulting a third review author when necessary.Two studies met the inclusion criteria. Study sample sizes were 16 and 20 participants. The trials were clinically heterogeneous with differences in causes for scarring (e.g. surgery, infected wounds, and trauma), site (e.g. chest and back), and ages of scars. The duration of follow-up was three and four and a half months. The included studies reported three comparisons; SGS compared with no treatment, SGS compared with non-silicone gel sheeting (a dressing similar to SGS but which does not contain silicone), and SGS compared with intralesional injections of triamcinolone acetonide. One trial had a split-body design and one trial had an unclear design (resulting in a mix of paired and clustered data). The included studies reported limited outcome data for the primary review outcome of scar severity measured by health professionals and no data were reported for severity of scar measured by patients or adverse events. For secondary outcomes some data on pain were reported, but health-related quality of life and cost-effectiveness were not reported. Both trials had suboptimal outcome reporting, thus many domains in the risk of bias were assessed as unclear. All evidence was rated as being very low-certainty, mainly due to risk of bias, indirectness, and imprecision. SGS compared with no treatment Two studies with 33 participants (76 scars) reported the severity of scar assessed by health professionals, and we are uncertain about the effect of SGS on scar severity compared with no treatment (very low-certainty evidence, downgraded once for risk of bias, once for inconsistency, once for indirectness, and once for imprecision). We are uncertain about the effect of SGS on pain compared with no treatment (21 participants with 40 scars; very low-certainty evidence, downgraded once for risk of bias, once for inconsistency, once for indirectness, and once for imprecision). No data were reported for other outcomes including scar severity assessed by patients, adverse events, adherence to treatment, health-related quality of life and cost-effectiveness. SGS compared with non-SGS One study with 16 participants (25 scars) was included in this comparison. We are uncertain about the effect of SGS on scar severity assessed by health professionals compared with non-SGS (very low-certainty evidence, downgraded once for risk of bias, once for indirectness, and once for imprecision). We are also uncertain about the effect of SGS on pain compared with non-SGS (very low-certainty evidence, downgraded once for risk of bias, once for indirectness, and once for imprecision). No data were reported for other outcomes including scar severity assessed by patients, adverse events, adherence to treatment, health-related quality of life and cost-effectiveness. SGS compared with intralesional injections of triamcinolone acetonide One study with 17 participants (51 scars) reported scar severity assessed by health professionals, and we are uncertain about the effect of SGS on scar severity compared with intralesional injections of triamcinolone acetonide (very low-certainty evidence, downgraded once for risk of bias, once for indirectness, and once for imprecision). This study also reported pain assessed by health professionals among 5 participants (15 scars) and we are uncertain about the effect of SGS on pain compared with intralesional injections of triamcinolone acetonide (very low-certainty evidence, downgraded once for risk of bias, once for indirectness, and twice for imprecision). No data were reported for other outcomes including scar severity assessed by patients, adverse events, adherence to treatment, health-related quality of life and cost-effectiveness.There is currently a lack of RCT evidence about the clinical effectiveness of SGS in the treatment of keloid scars. From the two studies identified, there is insufficient evidence to demonstrate whether the use of SGS compared with no treatment, non-SGS, or intralesional injections of triamcinolone acetonide makes any difference in the treatment of keloid scars. Evidence from the included studies is of very low certainty, mainly driven by the risk of bias, indirectness, and imprecision due to small sample size. Further well-designed studies that have good reporting methodologies and address important clinical, quality of life and economic outcomes are required to reduce uncertainty around decision-making in the use of SGS to treat keloid scars.

Pose estimation is an essential technology for product grasping and assembly in intelligent manufacturing. Finding local correspondences between the 2D image and 3D model is the key step to estimate the 6D pose of an object. However, when the objects are textureless, it is difficult to identify distinguishable point features. In this paper, we propose a novel deep learning framework called the pseudo-Siamese graph matching network to tackle the problem of feature matching of textureless objects, and estimate accurate object poses with a single RGB-only image. We utilize a pseudo-Siamese network structure to learn the similarity between the 2D image features and the 3D mesh model of the object. A fully convolutional network and a graph convolutional network are used to extract high-dimensional deep features of 2D image and 3D model, respectively. Dense 2D-3D correspondences are inferred using the pseudo-Siamese matching network. Then, the pose of the object is calculated by the PnP and RANSAC methods. Experiments on the LINEMOD dataset and a grasping task for metal part show the accuracy and robustness of our proposed method. Code is available at https://github.com/Ray0089/PSGMN.git.

Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering.

Energy harvesting (EH) aided communications hold a great potential in the design of green communication systems for their high energy efficiency. However, the random power supply due to EH may cause an intolerable delay in data transmission. To overcome this, a Reliable Energy Source (RES) is desired to provide transmission power when the large delay is induced. In this paper, we study the delay-optimal scheduling policy for EH aided communications with the constraint of average power provided by RES. More specifically, the delay-minimal scheduling is obtained through the two-dimensional Markov chain modeling and linear programming (LP) formulation. To further reduce the computational complexity, we present a value iteration algorithm, based on which we not only reveal a threshold-based structure of the delay-optimal scheduling policy for EH-aided communications with large-capacity batteries, but also conceive a low complexity policy that is asymptotically optimal. For EH-aided communications with finite-capacity batteries, we present a unified framework based on large deviation theory. The non-asymptotic framework demonstrates that the delay-power tradeoff curve of the low complexity scheduling policy is capable of converging to that of the delay-optimal policy exponentially as the capacity of the battery increases.

Background Immune checkpoint inhibitors (ICIs) have shown numerous clinical benefits in multiple cancer types, but good predictive biomarkers are severely lacking. Although increasing evidence has linked Hedgehog (Hh) signaling pathway with tumor development, a systematic investigation for its potential as a biomarker remains elusive. Methods We collected and analyzed the transcriptional data and clinical outcomes of diverse cancers from the Cancer Genome Atlas and four published ICI datasets. Hh activity was estimated by conducting a single‐sample gene‐set enrichment analysis (ssGSEA) for the Hh‐related genes and calculating the ssGSEA score in each tumor sample. Results Our findings suggest that tumors with high Hh activity displayed multiple immunosuppressive characteristics, including lack of anti‐tumor response pathways, downregulation of immune effectors, enrichment of immunosuppressive cells and chemokines, and activation of immunosuppressive signaling. Notably, patients in the non‐response group had enriched Hh activity and showed worse overall survival (OS; pooled HR = 1.50, 95% CI = 1.02–2.21, p = 0.039). In the subgroup of high programmed cell death ligand 1 (PD‐L1) expression, patients who harbored high Hh activity displayed a dramatically lower response rate to ICIs and a strikingly worse OS (pooled HR = 2.89, 95% CI = 1.53–5.49, p

Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.

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.

This paper studies on a division method of the whole aeroengine loading spectrum flight mission segment and rotor speed mission segment, which is based on the actual flight actions and related to the flight operations of aeroengine and is suitable for the variable-speed aeroengines such as turbojet and turbofan. Through the research, the aeroengine loading spectrum operation-related mission segments can be divided, which can provide important data basis for the life research on the structures which are sensitive to flight maneuver (such as the main shaft, large gearbox and installation section), lay a foundation for the simulation, compilation and prediction of the whole aeroengine loading spectrum. Firstly, based on the summary of basic flight actions in actual flight, the division of flight mission segment was realized by programming. Moreover, the aeroengine rotor speed mission segments, associated with flight actions and missions, were divided based on the flight mission segment division results. Besides, the efficiency and accuracy of the mission segment division results were verified by adopting measured loading spectrum data. Finally, the characteristics of speed mission segment division results were compared and analyzed in tables and figures. The comparison results show that the characteristics of similar speed mission segments are similar, while the characteristics of different speed mission segments are different. And the shapes of similar mission segments vary to the change of flight actions and operations, which can reflect the operation-related feature of the segments.

The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS). High-order features are usually developed to uncover subtle differences between sub-categories in FGFS, but they are less effective in handling the high intra-class variance. In this paper, we propose a Target-Oriented Alignment Network (TOAN) to investigate the fine-grained relation between the target query image and support classes. The feature of each support image is transformed to match the query ones in the embedding feature space, which reduces the disparity explicitly within each category. Moreover, different from existing FGFS approaches devise the high-order features over the global image with less explicit consideration of discriminative parts, we generate discriminative fine-grained features by integrating compositional concept representations to global second-order pooling. Extensive experiments are conducted on four fine-grained benchmarks to demonstrate the effectiveness of TOAN compared with the state-of-the-art models., T-CSVT Accepted

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp, respectively. Moreover, the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segment swap and tandem dispersed duplication. Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs. Mako is publicly available at https://github.com/xjtu-omics/Mako.

This article presents a control scheme for an omnidirectional wireless power transfer system. A system architecture is proposed to implement the current amplitude control. To focus power transfer towards targeted loads, a smart detection algorithm for identifying the positioning and orientation of receiver devices based on the input power information is presented. The system efficiency is further improved by a maximum efficiency point tracking function. A novel power flow control with a load combination strategy to charge multiple loads simultaneously is explained. Finally, the proposed control scheme is successfully applied to an example 6.78 MHz omnidirectional wireless charging bowl system. The experimental results confirm the accuracy of the load detection algorithm and validate the power flow control strategy.

Background: Adherence-to-colonoscopy in cascade screening of colorectal cancer (CRC) is crucial to the effectiveness of screening, but it has not been systematically evaluated. We aimed to evaluate the colonoscopy adherence among average-risk populations globally. Methods: We searched Pubmed, Embase, Web of Science, and Cochrane Library for observational and experimental studies published in English up to December 31, 2018 and reporting the colonoscopy adherence following positive initial screening. We calculated pooled adherence with its 95% confidence intervals (CIs) using a random-effects meta-analysis. Subgroup and mixed-effects meta-regression analysis were performed to evaluate heterogeneous factors for adherence. Findings: Of 9,772 articles identified, 191 (313 studies) were included in the systematic review. The pooled adherence-to-colonoscopy was 81·6% (95% CI: 79·6-83·4), with the estimates varying substantially by continents, CRC incidence, GDP per capita, recruitment method, screening round and type of initial test. Type of initial test was the most modifiable heterogeneous factor for adherence across studies (Q=53·00, P

The bilateral filter (BF) is a nonlinear filtering method, which can remove noise and retain better edge information. It has been widely used in the field of hyperspectral images (HSIs) filtering. In this letter, we propose a novel spectral-spatial information integration method based on the BF with multispatial domain (MBF). The proposed method includes three steps. First, principal component analysis (PCA) is used for the original HSI to obtain multiple components containing almost all information; second, multiple principal components are used as both spatial domain and range domain information for BF; finally, the extreme learning machine (ELM) is used for classification. To verify the effectiveness of the proposed approach, we evaluate performance on three benchmark data sets. Our method will improve the existing filtering methods by constructing multiple spatial domains for filtering, which will make more effective use of spatial features and solve the problem of lack of spatial information in HSIs. This method is compared with other filtering algorithms. Comparative experiments show that our proposed method can improve the classification accuracy. And the MBF information is more effective than the BF with single spatial domain information and other filtering methods.

This paper proposes a novel image classification architecture named ensemble cross‐stage partial attention network based on the backbone network DarkNet53 of Yolov3 to improve the feature extraction capability and the interpretability of image classification. This network has multiple advantages, including light model parameters, fast classification speed, and high classification accuracy for small objects and complex images. Local network architectures of different cross‐phases are added in the proposed network structure to reduce the calculation. Furthermore, channel and hybrid domain attention modules, which, respectively, fuse the branch feature with the extracted channel and spatial attention features, are designed for feature extraction of images. Experimental results confirm the improved performance of the proposed approach on the CIFAR‐100, ImageNet, and UCMerced datasets. In addition, experiments on the MSCOCO dataset suggest the application of the proposed method to object detection with satisfactory accuracy.

OBJECTIVES This study aimed to evaluate the most updated worldwide distribution, risk factors, and temporal trends of cervical cancer for different countries and age groups. METHODS The Global Cancer Observatory database was retrieved for the age-standardized rates (ASRs, per 100,000 persons) for incidence and mortality of cervical cancer in 2018. The associations with risk factors were examined by multivariable regression analysis, adjusting for human development index (HDI) and gross domestic products (GDP) per capita. Joinpoint regression analysis was used to calculate the 10-year annual average percent change (AAPC) for incidence and mortality. RESULTS A total of 568,847 new cases (ASR, 13.1) and 311,365 deaths (ASR, 6.9) of cervical cancer were reported globally in 2018. The highest incidence and mortality were observed in Southern Africa (ASRs, 43.1 and 20.0) and countries with low HDI (ASRs, 29.8 and 23.0). Countries with higher incidence and mortality had lower HDI (β = -8.19, 95% CI -11.32 to -5.06, p

Ferroelectric vertical gate-all-around field-effect-transistor (Fe-VGAAFET) suits a memory cell with a 5 nm technology node and beyond since it is less constrained by gate length, thereby providing sufficient space for the ferroelectric film compared with ferroelectric FinFET (Fe-FinFET) and ferroelectric lateral gate-all-around field-effect-transistors (Fe-LGAAFET). Also, Fe-VGAAFET achieves multilayer vertical stacking, which further increases the integrated density of devices. Here, we develop ferroelectric vertical sandwich gate-all-around field-effect-transistors (Fe-VSAFETs) with large memory windows (the maximum 2.3 V), high program/erase speeds (100 ns), and excellent retention properties using a self-aligned high-k metal gate process. Furthermore, vertical nanosheet devices with two channel thicknesses of approximately 16 and 42 nm and nanowire devices with a channel diameter of 30 nm were successfully fabricated, and excellent device characteristics were obtained.

This paper proposes the concept of “sub-wet bulb temperature” for the application of evaporative cooling technology, discusses the principle and implementation approach of the indirect-direct evaporative chiller for producing sub-wet bulb temperature cold water, and puts forward a way to describe the cooling performance of the pre-cooling section of indirect evaporative cooling through “sub-wet bulb efficiency”, using the “cooling efficiency of the water drenching filler on the air side and the water side” to show the cooling performance of the direct evaporative cooling section of the Water drenching filler, and utilizing the “unit's chilled water efficiency” to describe the cooling performance of the whole unit, and then explicit equation for predicting the unit's water supply temperature is derived out. The cooling performance of evaporative cooling chillers under different ambient air pre-cooling types was tested in Xinjiang region. The results showed that the chilled water efficiency of evaporative chillers reached a maximum value of 45.9 under the coil and vertical tube type of indirect evaporative pre-cooling form. At this time, the sub-wet bulb efficiency of the pre-cooling section of indirect evaporative cooling is 52.1%, the cooling efficiency of the water drenching filler on the air side has reached to 59.8%, and the water supply temperature of the unit is 13.7 °C, which is 2.3 °C lower than the wet bulb temperature of ambient air.

Recently, optical remote-sensing images have been steadily growing in size, as they contain massive data and complex backgrounds. This trend presents several problems for object detection, for example, increased computation time and memory consumption and more false positives due to the complex backgrounds of large-scale images. Inspired by deep neural networks combined with time-frequency analysis, we propose a time-frequency analysis-based object detection method for large-scale remote-sensing images with complex backgrounds. We utilize wavelet decomposition to carry out a time-frequency transform and then integrate it with deep learning in feature optimization. To effectively capture the time-frequency features, we propose a feature optimization method based on deep reinforcement learning to select the dominant time-frequency channels. Furthermore, we design a discrete wavelet multiscale attention mechanism (DW-MAM), enabling the detector to concentrate on the object area rather than the background. Extensive experiments show that the proposed method of learning from time-frequency channels not only solves the challenges of large-scale and complex backgrounds, but also improves the performance compared to the original state-of-the-art object detection methods. In addition, the proposed method can be used with almost all object detection neural networks, regardless of whether they are anchor-based or anchor-free detectors, horizontal or rotation detectors.

The emergence of adversarial examples has had a great impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for the detection of adversarial examples. A feature statistical layer is constructed to collect statistical data of feature map output from each convolutional layer in SmsNet by combining manual features with a neural network. The entire model is an end-to-end detection model, so the feature statistical layer is not independent of the network, and its output is directly transmitted to the fully connected layer by a short-cut connection called the SmsConnection. Additionally, a dynamic pruning strategy is proposed to simplify the model structure for better performance. The experiments demonstrate the effectiveness of the network structure and pruning strategy, and the proposed model achieves high detection rates against state-of-the-art adversarial attacks.

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or r2, and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; r2 = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner (P = .15), sex (P = .051), and section thickness (P = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

In this letter, a junction-temperature and current extraction method is presented based on the electroluminescence mechanism of the SiC mosfet body diode. Starting from the observation of two characteristic peaks in the emitted light spectrum, we proved that the junction temperature and the drain current can be simultaneously measured. This novel method consists of decoupling the relationship between the intensity of the electroluminescence peaks, the current, and the temperature. Through this optical method with inherent electrical isolation, the junction temperature and current in the SiC chip can be simultaneously measured with high precision. The total error of the junction temperature estimation is within ±3 °C, and the error of the current estimation is about ±0.2 A.

Today, remote sensing (RS) data are already regarded as ``big data.'' Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

Multistatic SAR is able to observe targets from different angles simultaneously, which enhances the information acquiring capability. However, multistatic SAR can still be affected by electromagnetic jamming, resulting in the misinterpretation of multistatic SAR images. This article proposes a method to locate multiple range-deception jammers and suppress jamming signals. First, the echo model of multistatic SAR under a multijammer environment is established. Second, the detection of interested targets in multistatic SAR images can be achieved through visual saliency detection methods based on spectral residual. Third, location distribution features of false targets in multistatic SAR images are analyzed, and the Euclidean distance criteria are used to effectively distinguish false targets. Accurate localization is then achieved by combing multistatic SAR configuration information. Finally, using a linear constrained minimum variance beamforming algorithm to suppress jamming signals, multistatic SAR images without jamming signals can be obtained. Simulation results validate the effectiveness of the proposed method in this article.