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Based on advanced information and communication infrastructures and enabled with the cutting-edge information processing of cognitive computing, existing smart manufacturing systems have foreseen a prevailing tendency that approaches a higher automation level, i.e., Self-X (e.g., Self-configuration/optimization/adaptation). However, the readiness of ‘Self-X’ levels is still far to reach, encountering the practical challenges of semantics-based networking and human-machine untrust in the manufacturing scenario. To mind these gaps, the authors envision an industrial knowledge graph (IKG) and graph embedding (GE) enabled pathway, to flourish today’s smart manufacturing paradigms towards cognitive mass personalization. To pave it, three promising IKG and GE enabling techniques in the ‘Self-X’ cognitive manufacturing network are described. Potential opportunities and challenges are also pointed out to invite more opinions to refine and innovate the exploitation of IKG and GE for the future of smart manufacturing.

Arnold-Chiari malformations (ACM) is a rare congenital hindbrain maldevelopment, leading to downward herniation of the cerebellar tonsils. Clinical features relates to cerebrospinal fluid disturbances, manifesting as symptoms of headaches, pseudotumour-like episodes, cranial nerve palsies and cerebellar dysfunction. Ocular manifestations includes varying ophthalmoloplegia and accommodation abnormalities. Papilloedema has been rarely implicated and remains an uncommon feature of ACM. We report a case of ACM who developed papilloedema and visual disturbance, that was successfully treated with suboccipital decompression. The presentation of patients with ACM-I and papilloedema unaccompanied by localising signs may resemble that of IIH. Neuroimaging with special attention to the craniocervical junction in saggital and transverse planes is crucial. Surgical decompression of the posterior fossa seems to improve headache symptoms and clinical signs of papilloedema.

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with k connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.

Objective The Chronic Disease Self-Management Program (CDSMP) is designed to enhance patients self-efficacy and skills to manage their chronic illness. There is compelling evidence for the benefits of the CDSMP among patients with systemic lupus erythematosus (SLE); however, little is known about predictors of participation among Black women with SLE. We examined factors associated with CDSMP initiation and completion in this population. Methods We studied 228 Black women with SLE who consented to attend a CDSMP workshop. We used logistic regression to calculate unadjusted and adjusted ORs for being a CDSMP initiator (a participant registered into the CDSMP who attended at least one of the first two weekly classes) and a CDSMP completer (a participant who completed >4 of 6 weekly classes). Results Majority of partipants were CDSMP initiators (74%, n=168). Of them, 126 (75%) were CDSMP completers. Older age [aOR: 1.03, 95% CI:1.00-1.06] and unemployment/disability [aOR: 2.05, 95% CI: 1.05-4.14] increased the odds of being a CDSMP initiator. The odds of initiating CDSMP decreased by 22% for each additional child in the household [OR: 0.78, 95% CI:0.62-0.98] but this association became non-significant in the adjusted model [aOR: 0.89, 95% CI:0.68-1.18]. The only factor that differed significantly between CDSMP completers and non-completers was age, with 4% higher odds of being a completer for each additional year of age (aOR: 1.04, 95% CI: 1.00-1.07). Conclusion Our findings suggest that young Black women with SLE face barriers to attend and complete in-person CDSMP workshops, possibly in relation to work and childcare demands.

With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users' demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40% of cost for users through the brokerage service.

Malaria, Zika virus, West Nile virus, Dengue fever, and Lyme disease are common causes of morbidity and mortality around the world. While arthropod bites may cause local inflammation and discomfort, a greater concern is the potential to develop deadly systemic infection. The use of insect repellents (IR) to prevent systemic infections constitutes a fundamental public health effort. Cost-effectiveness, availability, and high-efficacy against arthropod vectors are key characteristics of an ideal IR. Currently, numerous IRs are available on the market, with DEET (N,N-diethyl-3-methylbenzamide) being the most widely used. DEET has an excellent safety profile and remarkable protection against mosquitoes and various other arthropods. Other EPA-registered IR ingredients (permethrin, picaridin, IR3535, oil of lemon eucalyptus, oil of citronella, catnip oil, and 2-undecanone) are alternative IRs of great interest due to some having efficacies comparable to that of DEET. These alternative IRs possess low toxicity and favorable customer experiences in utilization (e.g., cosmetically pleasant, naturally occurring). This review summarizes currently available EPA-registered IRs: the origins, mechanisms of action, side effect profiles, and available formulations will be discussed. This review will enable the clinician to select the best IR option to meet patients' needs and provide the greatest protection from arthropod bites and sequelae.

Most commercially available optical see-through head-mounted displays (OST-HMDs) utilize optical combiners to simultaneously visualize the physical background and virtual objects. The displayed images perceived by users are a blend of rendered pixels and background colors. Enabling high fidelity color perception in mixed reality (MR) scenarios using OST-HMDs is an important but challenging task. We propose a real-time rendering scheme to enhance the color contrast between virtual objects and the surrounding background for OST-HMDs. Inspired by the discovery of color perception in psychophysics, we first formulate the color contrast enhancement as a constrained optimization problem. We then design an end-to-end algorithm to search the optimal complementary shift in both chromaticity and luminance of the displayed color. This aims at enhancing the contrast between virtual objects and the real background as well as keeping the consistency with the original displayed color. We assess the performance of our approach using a simulated OST-HMD environment and an off-the-shelf OST-HMD. Experimental results from objective evaluations and subjective user studies demonstrate that the proposed approach makes rendered virtual objects more distinguishable from the surrounding background, thereby bringing a better visual experience.

Background Observational studies have shown promising therapeutic effects of long-pulsed neodymium-doped yttrium aluminum garnet (LP-Nd:YAG) laser on warts. Objective To evaluate whether LP-Nd:YAG laser was superior to cryotherapy for cutaneous warts. Methods In this study, 150 adult patients with warts were randomized equally to receive laser or cryotherapy every 3–4 weeks, a maximum of 4 sessions. The primary outcomes were cure rate at 16 weeks and 6 months; secondary outcomes included time to clearance of warts and treatment-related adverse effects. Results There was no difference in the cure rate for laser versus cryotherapy at 16 weeks (54.1% vs. 46.7%) and 6 months (59.5% vs. 57.3%). However, time to clearance of warts, up to 16 weeks and 6 months, tended to be shorter for laser versus cryotherapy ( P = .04 and .08, respectively). Post-hoc analyses showed a significantly higher cure rate for laser versus cryotherapy in 3 subgroups of HPV 2/27/57-induced recalcitrant warts, but not in their counterpart subgroups. Laser had more mild adverse effects. Limitations Single-center. Conclusions The overall therapeutic effects of LP-Nd:YAG laser were similar to cryotherapy, but laser may be more effective to relatively recalcitrant warts and may associate with shorter time to clearance of warts.

Summary Aim of the study Nomograms have been proposed to assess prognosis following curative surgery for gastric cancer. The objective of the current study was to evaluate the performance of the Gastric Cancer Collaborative Group nomograms developed in 2014 by Kim et al., using a cohort of patients from a 10-year single institution experience in gastric cancer management. Patients and methods We retrospectively reviewed patients who underwent curative-intent surgery for histologically confirmed gastric cancer at First Surgical Clinic of Padua University Hospital (Italy) from January 2010 to May 2020. Univariable and multivariable Cox proportional hazard models were employed to assess the effect of the variables of interest on mortality and recurrence. Multivariable analysis was performed by considering the variables included in the Gastric Cancer Collaborative Group nomograms in order to validate them. The performance of the nomograms was evaluated using Harrell's C-index and calibration plots. Results Overall, 168 patients were included, with a median follow-up of 20.1 months. On multivariable analysis, tumor location, lymph node ratio, and pathological T stage were associated with recurrence; age, tumor location, lymph node ratio, and pT stage were associated with OS (overall survival). The nomograms had good discriminatory capability to classify both OS (C-index: 0.75) and DFS (disease-free survival) (C-index 0.72). The corrected C-Index for DFS based on the AJCC staging system revealed better prediction (C-Index 0.75), while the corrected C-Index for OS had worse discrimination ability compared with the current nomogram (C-Index 0.72). Conclusions The Gastric Cancer Collaborative Group nomograms demonstrated good performances in terms of prediction of both OS and DFS on external validation. The two nomograms are easy to apply, and variables included are widely available to most facilities.

Co-location pattern mining refers to discovering neighboring relationships of spatial features distributed in geographic space. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the large number of discovered patterns containing multiple redundancies. To address this problem, in this article, we propose a novel approach for discovering the super participation index-closed (SPI-closed) co-location patterns which are a newly proposed lossless condensed representation of co-location patterns by considering distributions of the spatial instances. In the proposed approach, first, a linear-time method is designed to generate complete and correct neighboring cliques using extended neighboring relationships. Based on these cliques, a hash structure is then constructed to store the distributions of the co-location patterns in a condensed way. Finally, using this hash structure, the SPI-closed co-location patterns (SCPs) are efficiently discovered even if the prevalence threshold is changed, while similar approaches have to restart their mining processes. To confirm the efficiency of the proposed method, we compared its performance with similar approaches in the literature on multiple real and synthetic spatial datasets. The experiments confirm that our new approach is more efficient, effective, and flexible than similar approaches.

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top-k partial loss and convex top-k partial hinge loss. Based on the losses, we present a novel top-k partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches.

This prospective randomized controlled study compared the short- and long-term efficacy and advantages of modified Park's submucosal over Milligan-Morgan hemorrhoidectomy (MMH) in the treatment of grade III and IV circumferential prolapsed hemorrhoids (CPH).A total of 186 consecutive patients with grade III or IV CPH were enrolled. The patients were randomized to MPSH (n = 93) or MMH (n = 93) treatment groups. Patient data included demographics, clinical characteristics, surgical parameters, underlying or arising complications, and CPH recurrence. The patients were closely followed up for at least 24 months.The patient demographic data and clinical parameters were comparable in the two groups (P 0.05). MPSH took significantly longer operative time (P 0.05). Although there was no significant difference in mean postoperative pain between the two groups at days 3 and 14, pain at day 7 was significantly more frequent and severe in the MMH group (P 0.05). There were no significant group differences in postoperative bleeding, intraoperative blood loss, or development of anal stenosis (P 0.05). However, the cumulative relapse-free rate was substantially higher in the MPSH group (P 0.05).MPSH and MMH are both practical and safe techniques for treating grade III and IV CPH disease. However, MPSH is safer, easier, and faster to perform and is associated with fewer recurrences within 24 months of surgery. Additional clinical studies are needed to validate these conclusions.

This paper addresses the problem of reconstructing 3D poses of multiple people from a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. Finally, an efficient tracking method is proposed to track the detected 3D poses across the multi-view video. The proposed approach achieves the state-of-the-art performance on the Campus and Shelf datasets, while being efficient for real-time applications.

Background The treatment of posterior inferior cerebellar artery aneurysms is controversial. Recently, flow diverters have emerged as an attractive treatment option. Here, we performed a systematic review and meta-analysis of the angiographic and clinical outcomes of flow diverter-treated posterior inferior cerebellar artery aneurysms. Methods We searched the PubMed, EMBASE and Web of Science databases for studies published from inception to January 2021. We included studies that described flow diverters procedures for posterior inferior cerebellar artery aneurysms with ≥2 patients. The outcomes were the complete occlusion rate and flow diverter-related complication rate. Meta-analysis was performed using a random or fixed effects model based on heterogeneity. Results We included six studies involving 46 posterior inferior cerebellar artery aneurysms. There were 14 ruptured aneurysms. All flow diverters were successfully released and the technical success rate was 100%. The complete occlusion rate was 81% (95% confidence interval = 65–93%; I2 = 0.00%; P 2 = 0.00%; P 2 = 0.00%; P = 0.951). Conclusions Treatment of posterior inferior cerebellar artery aneurysms with flow diverters is feasible and carries a high degree of technical success. However, this treatment is underutilized in patients with posterior inferior cerebellar artery aneurysms due to a higher complication rate and lower occlusion rate compared with clipping and traditional endovascular treatment. Further well-designed prospective and randomized studies are required to fully understand the effects of flow diverters especially in posterior inferior cerebellar artery aneurysms patients requiring endovascular treatment.

Objectives: This study was to investigate the expression and clinical significance of long intergenic noncoding RNA 00665 (LINC00665) in ovarian cancer , as well as its effect on malignant biological behavior of ovarian cancer cells. Methods: The expressions of LINC00665, miR-148b-3p and KLF5 in ovarian cancer (OC) tissues and cells were detected by RT-qPCR. Western blotting was used to detect KLF5 expression. The expression patterns of LINC00665, U6 and GAPDH in nuclear and cytoplasm fractions were detected using qRT-PCR. Besides, CCK-8 assay, clone formation assay, transwell, scratch test, and flow cytometry were respectively used to detect the cell activity, proliferation, invasiveness, healing of cells, and apoptosis rate of OC cells. Furthermore, the interactions between LINC00665 and miR-148b-3p and between miR-148b-3p and KLF5 were verified by luciferase reporter assay, and the correlations among these three genes were analyzed.Results: LINC00665 expression was up-regulated in ovarian cancer cell lines and tissues. Si-LINC00665 inhibited cell proliferation, invasion and migration, and induced the apoptosis to a certain extent. Subcellular fraction assay revealed the location of LINC00665 was mainly situated in the cytoplasm. Besides, miR-148b-3p was a target of LINC00665, and KLF5 was directly targeted by miR-148b-3p. Si-LINC00665 inhibited KLF5 expression, miR-148b-3p inhibitor promoted KLF5 expression, and si-KLF5 inhibited LINC00665 expression. Interestingly, expressions of LINC00665 and miR-148b-3p, and expressions of miR-148b-3p and KLF5 were negatively correlated, while expressions of LINC00665 and KLF5 were positively correlated. In addition, si-KLF5 inhibited the malignant biological behavior of ovarian cancer cells, while miR-148b-3p inhibitor had the opposite effect. Most importantly, the si-LINC00665 could reverse the promotion effect of miR-148b-3p inhibitor on the malignant biological behavior of ovarian cancer cells.Conclusions: LINC00665 can be used as an effective prognostic indicator of ovarian cancer, which has the potential to be a new drug target.

Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing the costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems, and then summarizes the research contributions of BaaS from technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions.

Background and aim: Achyranthis Bidentatae Radix plus Semen Vaccariae are traditional Chinese medicines, which have been widely applied in the treatment of migraine and Erectile Dysfunction (ED) for many years. This study verified the effect of Achyranthis Bidentatae Radix plus Semen Vaccariae in improving migraine-induced ED and explored its potential mechanism. Methods: Key targets and signaling pathways of Achyranthis Bidentatae Radix plus Semen Vaccariae in migraine-induced erectile dysfunction treatment were predicted by network pharmacology. A rat model of migraine was established by nitroglycerin injection. Apomorphine was injected into rats to screen the migraine-induced erectile dysfunction model, Achyranthis Bidentatae Radix-Semen Vaccariae granule suspension administered, and erectile function evaluated. Hematoxylin and eosin staining was used to compare the histological structure of the penile tissue, while RT-qPCR and Western blotting were used to determine mRNA and protein levels, respectively. Results: Screening allowed us to identify common targets for migraine and ED; the signaling pathway exhibiting the greatest change was the Myosin light chain kinase- Calcium (MLCK-CaM) signal pathway. From Western blotting and RT-qPCR, we found that the levels of MLCK mRNA and protein in rats from Group B rats were significantly higher (P Conclusion: Data indicate that the regulatory effects of Achyranthis Bidentatae Radix plus Semen Vaccariae on migraine-induced ED in a rat model are mediated by the MLCK-CaM signaling pathway.

With the rapid development of web technology, social media platforms have become a breeding ground for rumors. These rumors can threaten peoples health, endanger the economy, and affect the stability of a country. In recent years, to mitigate the problem of rumors, computational detection of rumors have been studied, producing some promising early results. However, how to effectively capture the temporal information of retweet dynamics and the structural information of propagation structure are still neglected. In this paper, we innovatively propose a novel Multiview Structural-Temporal Learning Framework for Rumor Detection, MiSTR, to jointly learn the temporal features of retweet dynamics, structural features of propagation graph, and the textural features of source tweet. More specifically, we utilize the timestamp encoding, and timestamp level and sequential level attention mechanisms to learn the temporal correlation among individual retweets. We propose two specific methods to learn the overall representation of propagation structure among users from both microscopic and mesoscopic perspectives. Encouraging empirical results on three real large-scale datasets demonstrate the superiority of our proposed method over the state-of-the-art approaches.

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

Fueled by the advances in the Internet of Things, and the growing capacity of smart mobile devices at the edge of the Internet, we have witnessed a growing trend in research and development for edge computing and edge storage, which extends the abilities of single mobile device on the edge through on-demand collaboration among multiple geographically distributed mobile devices. In this paper, we address several technical challenges that are unique for collaborative storage at the edge due to unique characteristics of mobile devices. First, we formalize the collaborative storage problem as an optimization problem. Second, we design an Acceleration Algorithm for Collaborative Storage, called A2CS, based on the architecture of Alternating Direction Method of Multipliers (ADMM). Specifically, we use the Nesterov's Acceleration strategy and the step size rules in the process of updating variables and determining the optimal speed of convergence. We develop a novel collaborative storage policy in order to guide the whole lifecycle of collaborative storage. Finally, we conduct a series of experiments for acceleration performance analysis and validation. We show that A2CS delivers a better convergence performance compared with ADMM and ADMM-OR, and a better utility performance compared with Average Distribution Strategy and Distance Preferred Distribution Strategy.

Pulmonary aspergilloma is chronic and invasive, potentially leading to life-threatening massive hemoptysis. The role of surgery for treating pulmonary aspergilloma and its effect on long-term survival needs more study.We reviewed 166 patients with aspergillomas treated at Shanghai Pulmonary Hospital from 2004 to 2017. Surgery indications included destroyed lung parenchyma, recurrent hemoptysis, despite appropriate medical treatment, and isolated pulmonary nodules suspected to be aspergilloma. Pulmonary aspergillomas are classified as simple (in an isolated thin-walled cavity, ≤3 mm) or complex (a thick-walled cyst,3 mm) based on computed tomographic scan findings.Aspergilloma was complex in 100 patients (60.2%) and simple in 66 (39.8%). The median (interquartile range) size of complex aspergillomas (2.5 [0.3-8.0] cm) was larger than that (2.0 [0.2-6.0] cm) of simple types (P.001). Hemoptysis occurred in 72 patients (72%) with complex disease and in 35 (53%) with simple disease (P = .014). Video-assisted thoracoscopic surgery was performed in 42 patients (63.6%) with simple aspergillomas, and 75 patients (75%) with of complex aspergillomas underwent thoracotomy. Prolonged air leakage (7 days) was the most common (17 [10.2%]) postoperative complication. A bronchopleural fistula developed postoperatively in 1 patient (0.6%). One patient (0.6%) died of respiratory failure within 30 days postoperatively. Two patients (1.2%) experienced recurrence during follow-up. The overall 10-year survival rates were 87.7% for complex aspergillomas and 94.97% for simple aspergillomas (P = .478). Diabetes (12 [7.2%]; hazard ratio, 13.15; 95% CI, 1.12-154.46) was associated with a worse prognosis.The perioperative morbidity and mortality of pulmonary aspergillomas are acceptable. Overall survival rates of simple and complex types are comparable.

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous chaotic parameter ranges, lack of robust chaos, and easy occurrence of chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial chaotic system (2D-PPCS) as a general system that can yield many 2-D chaotic maps with different exponent coefficient settings. The 2D-PPCS initializes two parametric polynomials and then applies modular chaotification to the polynomials. Setting different control parameters allows the 2D-PPCS to customize its Lyapunov exponents in order to obtain robust chaos and behaviors with desired complexity. Our theoretical analysis demonstrates the robust chaotic behavior of the 2D-PPCS. Two illustrative examples are provided and tested based on numeral experiments to verify the effectiveness of the 2D-PPCS. A chaos-based pseudorandom number generator is also developed to illustrate the applications of the 2D-PPCS. The experimental results demonstrate that these examples of the 2D-PPCS can achieve robust and desired chaos, have better performance, and generate higher randomness pseudorandom numbers than some representative 2-D chaotic maps.

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ-constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm.

Background Mayer-Rokitansky-Kuster-Hauser syndrome (MRKH syndrome), as congenital anomaly of female reproductive tract, is characterized by the congenital absence of uterus and vagina. Dilation maneuver was recommended as the non-surgical treatment for these patients. However, only several cases were reported depicting the prolapse of dilated neovagina of MRKH syndrome patients. Case We report our own experiences in treating 2 neovaginal prolapse MRKH syndrome patients receiving ischial spinous fascia fixation (ISFF) and reviewed neovaginal prolapse occurrence in MRKH syndrome. In approach to evaluate anatomic and functional outcome after operation, we compared the prolapse stage with POP-Q score and analyzed sexual activities by standardized Female Sexual Function Index 1 (FSFI) and Modified Body Image Scale 2 (MBIS). Summary and Conclusion Both subjective and objective improvement were observed in these two patients.

With the rapid development of information technology, the number of devices in the Internet of Things (IoT) is increasing explosively, which makes device identification a great challenge. Deep neural networks (DNN) have been used for device identification in IoT due to their superior learning ability. However, DNNs are susceptible to adversarial attacks, which can greatly degrade the accuracy of deep learning (DL) models for device identification. Adversarial attack is one of the fundamental security concerns for DNNs, and it is of great importance to study the generation of adversarial examples and to examine the attack effects for the design of robust DNN-based device identification schemes. In this paper, we examine the effects of non-targeted and targeted adversarial attacks on CNN-based device identification and propose combined evaluation indicators of logits to enrich the evaluation criteria. Our experimental results demonstrate that the identification accuracy degrades with the increase of perturbation level and iteration step size, and the proposed combined evaluation indicators are effective to show the individual device signal differences. The insights from this study will be useful for the design of robust DL-based IoT systems.

This paper addresses the problem of instance-level 6DoF object pose estimation} from a single RGB image. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occluded LINEMOD, YCB-Video, and Tless datasets, while being efficient for real-time pose estimation. We further create a Truncated LINEMOD dataset to validate the robustness of our approach against truncation. The code is available at https://github.com/zju3dv/pvnet.

Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this paper, we compare the recognition performance and robustness of two multimodal emotion recognition models: deep canonical correlation analysis (DCCA) and bimodal deep autoencoder (BDAE). The contributions of this paper are three folds: 1) We propose two methods for extending the original DCCA model for multimodal fusion: weighted sum fusion and attention-based fusion. 2) We systemically compare the performance of DCCA, BDAE, and traditional approaches on five multimodal datasets. 3) We investigate the robustness of DCCA, BDAE, and traditional approaches on SEED-V and DREAMER datasets under two conditions: adding noises to multimodal features and replacing EEG features with noises. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition results on all five datasets: 94.6% on the SEED dataset, 87.5% on the SEED-IV dataset, 84.3% and 85.6% on the DEAP dataset, 85.3% on the SEED-V dataset, and 89.0%, 90.6%, and 90.7% on the DREAMER dataset. Meanwhile, DCCA has greater robustness when adding various amounts of noises to the SEED-V and DREAMER datasets. By visualizing features before and after DCCA transformation on the SEED-V dataset, we find that the transformed features are more homogeneous and discriminative across emotions.

In this paper, we study optical bistability (OB) and optical multistability (OM) in a defect medium with a monolayer of graphene quantum system. The graphene quantum system interacts with two weak probe laser fields and an elliptical polarized coupling beam for adjusting the linear and nonlinear optical properties of defect layer. We find that by controlling the relative phase between applied lights and elliptical parameter of the coupling light, the enhanced Kerr nonlinearity with probe amplification can be obtained in resonance of probe light. Then we study the transmission and reflection coefficients of the incident lights for controlling the OB and OM features of transmitted and reflected light beams, respectively. Also, we discuss the thickness effect of the defect layer on the OB and OM properties and find that the switching from OB to OM or vice versa is possible. Moreover, we realize that by modifying the nonlinear properties of the defect medium, one can control the behaviors of the OB and OM, respectively.

BACKGROUND: To explore the development of central nervous system (CNS) symptoms and clinical application in predicting the clinical outcomes of SARS-COV-2 patients. METHODS: A retrospective cohort study was performed on the hospitalized patients with SARS-COV-2 recruited from four hospitals in Hubei Province, China from 18 January to 10 March 2020. The patients with CNS symptoms were determined. Data regarding clinical symptoms and laboratory tests were collected from medical records. RESULTS: Of 1268 patients studied, 162 (12.8%) had CNS symptoms, manifested as unconsciousness (71, 5.6%), coma (69, 5.4%), dysphoria (50, 3.9%), somnolence (34, 2.7%) and convulsion (3, 0.2%), which were observed at median of 14 (interquartile range 9-18) days after symptom onset and significantly associated with older age (OR = 5.71, 95% confidence interval [CI] 2.78-11.73), male (OR = 1.73, 95% CI 1.22-2.47) and preexisting hypertension (OR = 1.78, 95% CI 1.23-2.57). The presence of CNS symptoms could be predicted by abnormal laboratory tests across various clinical stages, including by lymphocyte counts of

Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an unbounded knapsack problem, and resource scaling as an one-step ahead resource allocation problem. Based on these models, we propose two scaling strategies: (a) delta capacity optimization, which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) full capacity optimization, which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach. Refereed/Peer-reviewed

Aroused by plant photosynthesis, Z-scheme heterostructures have been considered as a potential photocatalyst for solar-driven water splitting to solve the current energy crisis. Hence, based on first-principles calculations, we predict that Sc2CF2/Janus MoSSe can be used as a Z-scheme heterostructure for efficient photocatalytic water splitting. The research shows that the traditional type-II to direct Z-scheme heterostructure conversion can be realized through different stacking methods of Sc2CF2 and Janus MoSSe. Through the built-in electric field and band bending theory, the transition path of photogenerated carriers is analyzed, which reveals the completely different photocatalytic mechanism of type-II and direct Z-scheme heterostructure. Surprisingly, the direct Z-scheme heterostructure displays a high overpotential of the hydrogen evolution reaction ( χ H 2 =1.01 eV) and the oxygen evolution reaction ( χ O 2 =1.46 eV) compared with the type II heterostructure. More importantly, the direct Z-scheme heterostructure has an ultra-high solar-to-hydrogen (STH) efficiency (36.1%), which breaks through the limitation of traditional theoretical efficiency and reveals a tremendous prospect of commercial application. Furthermore, the calculation of free energy confirms that the water splitting reaction on the direct Z-scheme heterostructure occur spontaneously under the external potential, but not for type-II heterostructure. Finally, introducing the additional electronic conductor (N-doped graphene) can accelerate the electron (hole) transfer and interlayer carrier recombination, which will further improve the photocatalytic performance of Z-scheme heterostructure. These distinctive features make Sc2CF2/Janus MoSSe heterostructure as promising Z-scheme photocatalyst for water splitting.

Population and income growth are increasing global food demand at a time when a third of the world’s agricultural soils are degraded and climate variability threatens the sustainability of food production. Intercropping, the practice of growing two or more spatially intermingled crops, often increases yields, but whether such yield increases, their stability and soil fertility can be sustained over time remains unclear. Using four long-term (10–16 years) experiments on soils of differing fertility, we found that grain yields in intercropped systems were on average 22% greater than in matched monocultures and had greater year-to-year stability. Moreover, relative to monocultures, yield benefits of intercropping increased through time, suggesting that intercropping may increase soil fertility via observed increases in soil organic matter, total nitrogen and macro-aggregates when comparing intercropped with monoculture soils. Our results suggest that wider adoption of intercropping could increase both crop production and its long-term sustainability. Growing demand for food is confronting constraints to its sustainable production. This study finds that intercropping increases grain yields and their stability and that yield benefits increase over time.

Continuous memristor has been widely used in chaotic oscillating circuits and neuromorphic computing systems. However, discrete memristor and its coupling discrete map have not been noticed yet. This article presents a discrete memristor and constructs a general two-dimensional memristive map model by coupling the discrete memristor with an existing discrete map. The pinched hysteresis loops of the discrete memristor are demonstrated. Four examples of memristive discrete maps are provided and their coupling strength-relied and memristor initial-boosted complex dynamics are investigated using numerical measures. The evaluation results manifest that the discrete memristor can enhance the chaos complexity and its coupling maps can generate hyperchaos. Particularly, the hyperchaotic sequences can nondestructively be controlled by memristor initial state and the initial-controlled hyperchaos is robust, which is applicable to many chaos-based applications. Additionally, we develop a hardware platform to implement the memristive maps and acquire the four-channel hyperchaotic sequences. We also apply the memristive maps to the application of secure communication and the experiments show that the memristive maps display better performance than some existing discrete maps.

With the increasing application of power monitoring terminal and the increasing demand for its function, it is necessary to upgrade the field embedded software remotely.The existing online upgrade schemes generally deal with the terminals one by one, and there are also distributed upgrade schemes. However, due to the large differences in the communication performance of terminals in different places, there is a problem of load imbalance, resulting in the low efficiency of the whole upgrade. Aiming at the efficiency of remote online upgrade of power monitoring terminal, a distributed load balancing remote upgrade method is proposed. The power monitoring terminal is upgraded at the same time through the cloud platform, and the genetic algorithm is used for load balancing processing, so as to improve the efficiency. The practical results show that this remote upgrade method improves the upgrade efficiency of the terminal by comparing a variety of upgrade schemes, compared with no load balancing method, the average time of upgrading a terminal is reduced by 36%, which achieves good application effect in practical application.

Background Cognitive deficits are common in patients with sepsis. Previous studies in sepsis-associated encephalopathy (SAE) implicated the C-X-C chemokine receptor type (CXCR) 5. The present study used a mouse model of SAE to examine whether CXCR5 down-regulation could attenuate cognitive deficits. Methods Sepsis was induced in adult male C57BL/6 J and CXCR5−/− mice by cecal ligation and puncture (CLP). At 14–18 days after surgery, animals were tested in a Morris water maze, followed by a fear conditioning test. Transmission electron microscopy of hippocampal sections was used to assess levels of autophagy. Primary microglial cultures challenged with lipopolysaccharide (LPS) were used to examine the effects of short interfering RNA targeting CXCR5, and to investigate the possible involvement of the p38MAPK/NF-κB/STAT3 signaling pathway. Results CLP impaired learning and memory and up-regulated CXCR5 in hippocampal microglia. CLP activated hippocampal autophagy, as reflected by increases in numbers of autophagic vacuoles, conversion of microtubule-associated protein 1 light chain 3 (LC3) from form I to form II, accumulation of beclin-1 and autophagy-related gene-5, and a decrease in p62 expression. CLP also shifted microglial polarization to the M1 phenotype, and increased levels of IL-1β, IL-6 and phosphorylated p38MAPK. CXCR5 knockout further enhanced autophagy but partially reversed all the other CLP-induced effects, including cognitive deficits. Similar effects on autophagy and cytokine expression were observed after knocking down CXCR5 in LPS-challenged primary microglial cultures; this knockdown also partially reversed LPS-induced up-regulation of phosphorylated NF-κB and STAT3. The p38MAPK agonist P79350 partially reversed the effects of CXCR5 knockdown in microglial cultures. Conclusions CXCR5 may act via p38MAPK/NF-κB/STAT3 signaling to inhibit hippocampal autophagy during sepsis and thereby contribute to cognitive dysfunction. Down-regulating CXCR5 can restore autophagy and mitigate the proinflammatory microenvironment in the hippocampus.

Temperature has been studied in relation to many health outcomes. However, few studies have explored its effect on the risk of hospital admission for rheumatoid arthritis (RA). A distributed lag non-linear model (DLNM) was used to analyze associations between mean temperature, diurnal temperature range (DTR), temperature change between neighboring days (TCN), and daily admissions for RA from 2015 to 2019 in Anqing, China. Subgroup analyses based on age, gender, rheumatoid factors, and admission route were performed. In total, 1456 patients with RA were hospitalized. Regarding the cumulative-lag effects of extreme cold temperature (5th percentile = 3℃), the risks of admissions for RA were increased and highest at lag 0–11 (RR = 2.68, 95% CI: 1.23–5.86). Exposing to low (5th percentile = 1.9℃) and high (95th percentile = 14.2℃) DTRs both had increased risks of RA admission, with highest RRs of 1.40 (95% CI: 1.03–1.91) and 1.24 (95% CI: 1.0–1.53) at lag 0 day, respectively. As for TCN, the marginal risk of admission in RA patients was found when exposed to high TCN (95th percentile = 2.9℃) with the largest single-day effect at lag 10 (RR = 1.11, 95% CI: 1.01–1.23). In subgroup analyses, females were more susceptible to extreme cold temperature, low and high DTRs, and high TCN. In regard to extreme cold temperature, significant risk of hospital admission in females only appeared at lag 2 (RR = 1.48, 95% CI: 1.02–2.15) and lag 0–2 (RR = 2.35, 95% CI: 1.11–4.95). It is clear that RA patients exposed to changing temperature may increase risks of admission. Supplementary Information The online version contains supplementary material available at 10.1007/s00484-021-02207-9.

The abundant and inexpensive transition metal iron was loaded onto the abundant nonmetal minerals of coal-bearing kaolin (CK) to synthesize cost-effective iron nanoparticle (FeNP)-loaded kaolin heterogeneous catalysts. Various characterization techniques were performed, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The catalytic activity was screened for the direct esterification of benzoic acid with cyclic ether via activation of the C(sp3)-H bond in a cross-dehydrogenative-coupling reaction, and the corresponding α-acyloxy ether products were obtained in good yield. Notably, the FeNP-loaded CK solid catalysts were relatively stable and could be reused at least five times while retaining good catalytic activity.

RGB salient object detection (SOD) has made great progress. However, the performance of this single-modal salient object detection will be significantly decreased when encountering some challenging scenes, such as low light or darkness. To deal with the above challenges, thermal infrared (T) image is introduced into the salient object detection. This fused modal is called RGB-T salient object detection. To achieve deep mining of the unique characteristics of single modal and the full integration of cross-modality information, a novel Cross-Guided Fusion Network (CGFNet) for RGB-T salient object detection is proposed. Specifically, a Cross-Scale Alternate Guiding Fusion (CSAGF) module is proposed to mine the high-level semantic information and provide global context support. Subsequently, we design a Guidance Fusion Module (GFM) to achieve sufficient cross-modality fusion by using single modal as the main guidance and the other modal as auxiliary. Finally, the Cross-Guided Fusion Module (CGFM) is presented and serves as the main decoding block. And each decoding block is consists of two parts with two modalities information of each being the main guidance, i.e., cross-shared Cross-Level Enhancement (CLE) and Global Auxiliary Enhancement (GAE). The main difference between the two parts is that the GFM using different modalities as the main guide. The comprehensive experimental results prove that our method achieves better performance than the state-of-the-art salient detection methods. The source code has released at: https://github.com/wangjie 0825/CGFNet.git.

Background Brahma-related gene 1 (BRG1) is essential for embryogenesis and cellular metabolism. A deficiency of BRG1 in vivo decreases lipid droplets, but the molecular mechanism underlying its role in lipid metabolism associated with hepatocellular carcinoma (HCC) remains unknown. Aims We aimed to determine the role of BRG1 in lipid metabolism in HCC. Methods We assessed the differential expression of BRG1 in HCC and adjacent non-tumorous tissues using tissue microarrays. We stained lipid droplets in HCC cells with Bodipy fluorescence and Oil Red O, and verified BRG1 binding to the promoter region of glycosylated lysosomal membrane protein (GLMP) using chromatin immunoprecipitation. Results The expression of GLMP, a potential lipid metabolism regulator, was suppressed by BRG1 via transcriptional activity. Knockdown of BRG1 decreased lipid droplets, increased GLMP expression and altered the phosphoinositide-3-kinase adaptor protein 1 (PIK3AP1)/phosphatidylinositol-3 kinase (PI3K)/protein kinase B (AKT) pathway in HCC, which further GLMP knockdown partially restored. Thus, GLMP knockdown increased lipid droplets and differentially altered the PI3K/AKT pathway. Conclusions Downregulating BRG1 decreased lipid droplet deposition in HCC cells by upregulating GLMP and altering the PI3K/AKT pathway. Both BRG1 and GLMP might serve as therapeutic targets for disorders associated with dysregulated lipid metabolism, such as NAFLD and NAFLD-associated HCC.

Smart grids bring advantages of reliabilty and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and securtiy in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the paper. Firstly, we adopt Chinese Remainder Theorem to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center. Secondly, our scheme is efficient for encryption without high-cost additive homomorphic public key encryption scheme, such as Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computation complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity and data integrity of the users’ reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than existing competing approaches in terms of computation and communication overhead.

The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAEsn). After we define two thresholds of ``0.35'' and ``0.70'' from the percentage of eye closure, the output values are in the continuous range of 0-0.35, 0.36-0.70, and 0.71-1 representing the awake state, the tired state, and the drowsy state, respectively. To verify the efficiency of our strategy, we first applied the proposed approach to a single modality. Then, for the multimodality, since the complementary information between forehead electrooculography and electroencephalography features, we found the performance of the proposed approach using features fusion significantly improved, demonstrating the effectiveness and efficiency of our method.

Purpose Information regarding the views of patients, on visual field testing is limited, and no information exists regarding their preferences for test developments. This study aimed to increase knowledge of patients’ experiences of visual field assessment and to explore their opinions and priorities regarding current areas of research and development. Design Online questionnaire with purposive sampling design. Participants Adults who regularly undergo visual field tests in Australia who report having glaucoma or being at glaucomatous risk. Methods An anonymous survey, implemented using the Qualtrics webtool, with both closed- and-open ended questions designed to explore opinions regarding visual field testing, visit attendance for perimetry, as well as priorities for developments. Main Outcome Measures The survey assessed 3 domains: (1) opinions regarding test duration and visit frequency, (2) subjective experience, and (3) perspectives on future developments. Results One hundred fifty-two complete survey responses were obtained. The median age of participants was 66 years (interquartile range [IQR], 60–72 years). Most participants (70%) had experience of undergoing more than 11 visual field tests. Participants recalled that they completed visual field tests in median of 6 minutes (IQR, 5–8 minutes) and were willing to accept additional time (median, 5 minutes; IQR, 3–6 minutes) to obtain more information. Participants were prepared to increase both the number of visual field tests per eye and the frequency of visual field tests (median, 3 visits per year; IQR, 2–4 visits per year) to gain more information about their visual status. Regarding future developments, the most preferred option was “similar test times but an increase in the level of information about my visual field,” which ranked significantly higher than all other options, including “shorter test times that maintain the currently available level of information about my visual field.” Conclusions Our study confirms, in a different population and health care system, previous research reporting patient perspectives on visual field assessment. We further revealed that health care consumers show a strong preference for accurate information about their vision and report being prepared to undergo longer visual field tests or more visual field tests to achieve that outcome.

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.