Explore the vast range of titles available.

Multiview clustering (MVC) is a proven, effective approach to boosting the various downstream tasks given by unlabeled data. In contemporary society, domain-specific multiview data, such as multiphase postoperative liver tumor contrast-enhanced computed tomography (CECT) images, may be vulnerable to exploitation by illicit organizations or may not be comprehensively collected due to patient privacy concerns. Thus, these can be modeled as incomplete multiview clustering (IMVC) problems. Most existing IMVC methods have three issues: (1) most methods rely on paired views, which are often unavailable in clinical practice; (2) directly predicting the features of missing views may omit key features; and (3) recovered views still have subtle differences from the originals. To overcome these challenges, we proposed a novel framework named fuzzy clustering combined with information theory arithmetic based on feature reconstruction (FCITAFR). Specifically, we propose a method for reconstructing the characteristics of prevailing perspectives for each sample. Based on this, we utilized the reconstructed features to predict the missing views. Then, based on the predicted features, we used variational fuzzy c-means clustering (FCM) combined with information theory to learn the mutual information among views. The experimental results indicate the advantages of FCITAFR in comparison to state-of-the-art methods, on both in-house and external datasets, in terms of accuracy (ACC) (77.5%), normalized mutual information (NMI) (37.9%), and adjusted rand index (ARI) (29.5%).

Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm.

In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the accuracy of grasping are both low when facing the scene of stacking and occlusion for the reason that there is information missing by single-view 2D camera-based methods while acquiring scene information, and the methods of grasping only can’t change the difficult-to-grasp scene which is stack and occluded. Regarding the issue above, a pushing-grasping collaborative method based on the deep Q-network in dual viewpoints is proposed in this paper. This method in this paper adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, which solved the problem of lack of information missing. What’s more, it combines the pushing and grasping actions with the deep Q-network, which make it have the ability of active exploration, so that the trained manipulator can make the scenes less stacking and occlusion, and with the help of that, it can perform well in more complicated grasping scenes. In addition, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before.

This study aims to highlight the effectiveness of computer vision (CV) techniques in classifying brain tumors using a comprehensive dataset consisting of computed tomography (CT) scans. The proposed framework comprises six types of brain tumors, including benign tumors (Meningioma, Schwannoma, and Neurofibromatosis) and malignant tumors (Glioma, Chondrosarcoma, and Chordoma). The acquired images underwent pre-processing steps to enhance the dataset’s quality, including noise reduction through median and Gaussian filters and region of interest (ROIs) extraction using an automated binary threshold-based fuzzy c-means segmentation (ABTFCS) approach. A total of 900 CT-scan images were utilized, 150 images per tumor class, each with a size of 512 × 512 pixels, and 4 ROIs taken per image, so the total dataset size is 3600 (900 × 4) attributes. After pre-processing, the dataset was further analysed to extract 135 statistical multi-features for each ROI. An optimized set of 12 statistical multi-features was selected to identify the most relevant features using a feature selection technique based on correlation. For the classification stage, the optimized statistical multi-feature dataset was evaluated using five computer vision classifiers: multilayer perceptron (MLP), BayesNet, PART, random tree, and randomizable filtered classifier, employing a 10-fold cross-validation method. Among these classifiers, MLP with fine-tuned hyperparameters achieved a promising accuracy rate of 97.83%.

Background Patients with gout frequently have low urinary pH, which is associated with the nephrolithiasis. However, the specific distribution of urinary pH and potential relationship of acidic urine pH to broader manifestations of kidney disease in gout are still poorly understood. Methods A 2016–2020 population-based cross-sectional study was conducted among 3565 gout patients in the dedicated gout clinic of the Affiliated Hospital of Qingdao University to investigate the association between low urinary pH and kidney disease. We studied patients that we defined to have “primary gout”, based on the absence of > stage 2 CKD. All subjects underwent 14 days of medication washout and 3-day standardized metabolic diet. We obtained general medical information, blood and urine biochemistries, and renal ultrasound examination on the day of the visit. The primary readouts were urine pH, eGFR, nephrolithiasis, renal cysts, microhematuria, and proteinuria. Patients were assigned into 5 subgroups (urine pH ≤5.0, 5.0 6.9), aligning with the clinical significance of urine pH. Results Overall, the median urine pH and eGFR of all patients was 5.63 (IQR 5.37~6.09), and 98.32 (IQR 86.03~110.6), with acidic urine in 46.5% of patients. The prevalence of nephrolithiasis, microhematuria, and proteinuria were 16.9%, 49.5%, and 6.9%, respectively. By univariate analysis, eGFR was significantly associated with age, sex, duration of gout, tophus, body mass index, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, serum utare, hypertension, diabetes, and urine pH. On multivariable analysis, eGFR was associated with age, sex, diastolic blood pressure, serum uric acid, hypertension, diabetes, and urine pH. Acidic urine pH, especially urine pH < 5.0, was significantly associated with the prevalence of kidney disease, including > stage 1 CKD, nephrolithiasis, kidney cyst, and microhematuria. Patients with 6.2 ≤ urine pH ≤ 6.9 and SU ≤ 480 μmol/L had the highest eGFR with the lowest prevalence of nephrolithiasis, microhematuria, and proteinuria. Conclusions Approximately half of gout subjects had acidic urine pH. Urine pH < 5.0 was associated with significantly increased nephrolithiasis, renal cyst, microhematuria, and proteinuria. The results support prospective clinical investigation of urinary alkalinization in selected gout patients with acidic urine pH.

The use of renewable energy has increased dramatically over the past couple of decades. Wind farms, consisting of wind turbines, play a vital role in the generation of renewable energy. For monitoring and maintenance purposes, a wind turbine has a variety of sensors to measure the state of the turbine. Sensor measurements are transmitted to a control center, which is located away from the wind farm, for monitoring and maintenance purposes. It is therefore desirable to ensure reliable wireless communication between the wind turbines and the control center while integrating the observations from different sensors. In this paper, we propose an IoT based communication framework for the purpose of reliable communication between wind turbines and control center. The communication framework is based on repeat-accumulate coded communication to enhance reliability. A fusion algorithm is proposed to exploit the observations from multiple sensors while taking into consideration the unpredictable nature of the wireless channel. The numerical results show that the proposed scheme can closely predict the state of a wind turbine. We also show that the proposed scheme significantly outperforms traditional estimation schemes.

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.

Background While xanthine oxidase inhibitors target uric acid production, renal urate underexcretion is the predominant subtypes in gout. This study was to compare treatment response to the XOI febuxostat in a gout cohort according to clinical subtypes of hyperuricemia. Methods A prospective cohort study was conducted to compare the efficacy and safety of febuxostat (initially 20 mg daily, escalating to 40 mg daily if not at target) in 644 gout patients with the three major clinical subtypes for 12 weeks. Hyperuricemia was defined as the renal overload subtype, the renal underexcretion subtype, or the combined subtype based on UUE > or ≤ 600 mg/d/1.73 m 2 and FE UA  < or ≥ 5.5%. The primary endpoint was the rate of achieving serum urate (SU) < 6 mg/dL at week 12. Results Fewer participants with combined subtype achieved the SU target, 45.5% compared with 64.8% with overload subtype ( P  = 0.007), and 56.6% with underexcretion subtype ( P  = 0.022). More participants with combined subtype (82%) had febuxostat escalated to 40 mg than those with overload (62%, P  = 0.001) or underexcretion subtype (68%, P  = 0.001). In all participants, combined subtype hyperuricemia (OR = 0.64, 95%CI 0.41–0.99, P  = 0.048) and baseline SU (OR = 0.74, 95%CI 0.62–0.89, P  = 0.001) were independently associated with lower rates of achieving SU target. Conclusions People with combined subtype have a lower response to febuxostat, compared to those with either overload or underexcretion subtype. Assessment of hyperuricemia subtype may provide useful clinical data in predicting febuxostat response. Key Message • Combined subtype of hyperuricemia had lower urate-lowering response to febuxostat compared with other subtypes. • Typing as combined subtype and baseline serum urate were independent risk factors for targeting. • Assessment of hyperuricemia clinical subtype may provide useful data in predicting febuxostat response.

Background Low urine pH, which may be mediated by metabolic syndrome (MetS), is common in gout. Tart cherries are shown to improve MetS symptoms and possess anti-inflammatory properties. However, the efficacy of tart cherry supplements on urine pH has yet to be studied. Objectives This study aimed to investigate the efficacy and safety of tart cherry supplementary citrate (TaCCi) mixture on urine pH, serum urate (sUA), C-reactive protein (CRP), and gout flares in gout patients initiating urate-lowering therapy (ULT), in comparison to citrate mixture and sodium bicarbonate. Methods A prospective, randomized (1:1:1), open-label, parallel-controlled trial was conducted among 282 men with gout and fasting urine pH ≤ 6, who were initiating ULT with febuxostat (initially 20 mg daily, escalating to 40 mg daily if serum urate ≥ 360 μmol/L). Participants were randomized to groups taking either sodium bicarbonate, citrate mixture, or TaCCi mixture. All participants were followed every 4 weeks until week 12. Urine pH and sUA were co-primary outcomes, with various biochemical and clinical secondary endpoints. Results Urine pH increased to a similar extent in all three groups. SUA levels declined in all three groups as well, with no significant differences observed between the groups. At week 12, the TaCCi mixture group exhibited a greater reduction in the urine albumin/creatinine ratio (UACR) compared to the other two groups ( p  < 0.05). Participants taking TaCCi mixture or citrate mixture experienced fewer gout flares than those in the sodium bicarbonate group over the study period ( p  < 0.05). Additionally, the TaCCi mixture group had a lower CRP level at week 12 relative to the other two groups ( p  < 0.01). Adverse events were similar across all three groups. Conclusion The TaCCi mixture had similar efficacy and safety on urine alkalization and sUA-lowering as the citrate mixture and sodium bicarbonate in patients with gout. However, the TaCCi mixture resulted in greater improvements in UACR and CRP, which suggests that tart cherry supplements may provide additional benefits for renal protection and reduce inflammation in gout, particularly when starting ULT. Trial registration This project was registered in ChiCTR ( www.chictr.org.cn ), with the registration number: ChiCTR2100050749.

Background People with gout and combined-type hyperuricemia, defined as renal urate overload and renal urate under-excretion, have diminished responsiveness to urate-lowering therapy. Emerging observational data suggest that urine alkalization might improve responsiveness to febuxostat. Hence, this prospective study evaluated the urate-lowering efficacy of citrate mixture added to febuxostat in people with gout and combined-type hyperuricemia. Methods Patients with combined-type hyperuricemia and low urine pH (< 6.2) were prospectively enrolled from a gout clinic. All were treated with febuxostat (initially 20 mg daily, escalated to 40 mg daily if serum urate (SU) ≥360µmol/L). Citrate mixture (3.5 g twice daily, open label) was added according to shared decision of both physician and patient (alkalization vs. non-alkalization). Participants were followed for 12 weeks, with primary endpoint being achievement of SU < 360 µmol/L at final assessment. Results We enrolled 234 eligible patients, with 194 completing 12 weeks follow-up (98 non-alkalization and 96 with alkalization). At week 12, more patients in the alkalization group achieved SU < 360µmol/L (57.3% vs. 39.8%, P  < 0.05), with significantly increased renal urate excretion, and lower febuxostat doses (mean ± S.D, 31.9 ± 9.9 vs. 34.7 ± 8.9 mg, P  < 0.05). Additionally, metabolic measures including the triglyceride concentration and triglyceride-glucose index were lower, and high-density lipoprotein cholesterol concentration as well as insulin sensitivity were higher in the alkalization group. The incidence of adverse events was similar between groups. Conclusions Adjunctive urine alkalization with febuxostat demonstrated a superior urate-lowering response and improved metabolic abnormalities in male with gout and combined-type hyperuricemia. Trial registration ChiCTR, http://www.chictr.org.cn , ChiCTR2100043573.