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A premature infant was delivered at 30 weeks with a birth weight of 1440 g. The infant required intubation at birth for respiratory distress syndrome. On the second day of life, a peripherally inserted central catheter (PICC) was placed via the left saphenous vein for administration of total

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving complex and computationally expensive optimization problems. However, most of the existing algorithms converge slowly in the later stage. This article proposes a novel two-stage data-driven evolutionary optimization (TS-DDEO) that meets the requirements of early exploration and later exploitation. In the first stage, a surrogate-assisted hierarchical particle swarm optimization method is used to find a promising area from the entire search space. In the second stage, we propose a best-data-driven optimization (BDDO) method with a strong exploitation ability to accelerate the optimization process. BDDO has a real-time update mechanism for the surrogate model and population and uses a predefined number of ranking-top solutions to update population and surrogates. BDDO combines three surrogate-assisted evolutionary sampling strategies: 1) surrogate-assisted differential evolution sampling; 2) surrogate-assisted local search; and 3) a surrogate-assisted full-crossover (FC) strategy which is proposed to integrate existing best genotypes in the population. Experiments and analysis have validated the effectiveness of the two-stage framework, the BDDO method, and the FC strategy. Moreover, the proposed algorithm is compared with five state-of-the-art SAEAs on high-dimensional benchmark functions. The result shows that TS-DDEO performs better both in effectiveness and robustness.

Objective To compare outcomes after surgery between patients who were not prescribed opioids and patients who were prescribed opioids. Summary of background data Postoperative opioid prescriptions carry significant risks. Understanding outcomes among patients who receive no opioids after surgery may inform efforts to reduce these risks. Methods We performed a retrospective study of adult patients who underwent surgery between January 1, 2019 and October 31, 2019. The primary outcome was the composite incidence of an emergency department visit, readmission, or reoperation within 30 days of surgery. Secondary outcomes were postoperative pain, satisfaction, quality of life, and regret collected via postoperative survey. A multilevel, mixed-effects logistic regression was performed to evaluate differences between groups. Results In a cohort of 22,345 patients, mean age (standard deviation) was 52.1 (16.5) years and 13,269 (59.4%) patients were female. About 3175 (14.2%) patients were not prescribed opioids, of whom 422 (13.3%) met the composite adverse event endpoint compared to 2255 (11.8%) of patients not prescribed opioids (P = 0.015). Patients not prescribed opioids had a similar probability of adverse events {11.7% [95% confidence interval (CI) 10.2%-13.2%] vs 11.9% (95% CI 10.6%-13.3%]}. Among 12,872 survey respondents, patients who were not prescribed an opioid had a similar rate of high satisfaction [81.7% (95% CI 77.3%-86.1%) vs 81.7% (95% CI 77.7%-85.7%)] and no regret [(93.0% (95% CI 90.8%-95.2%) vs 92.6% (95% CI 90.4%-94.7%)]. Conclusions Patients who were not prescribed opioids after surgery had similar clinical and patient-reported outcomes as patients who were prescribed opioids. This suggests that minimizing opioids as part of routine postoperative care is unlikely to adversely affect patients.

usc Refereed/Peer-reviewed Improving the service level of metro station is the key to attracting passengers to achieve sustainable development. The satisfaction evaluation approach from the passenger's point of view can intuitively and meticulously reflect the passenger's experience when taking the metro and provide a basis for improving the service. Therefore, this paper proposes an integrated approach for a metro station satisfaction evaluation based on passenger perception. In this approach, the evaluation index system is first dynamically screened and established. Then, the structural equation mixture model (SEMM) is adopted to determine the index weight based on the customer satisfaction index model and the benchmark. The result clouds are generated by the basic algorithm of the cloud model. Finally, the targeted improvement suggestions are proposed based on the evaluation results. Taking the Shanghai Metro People's Square Station as a case, the evaluation approach was verified, which reflected the effectiveness and rationality of the approach. As the results show, this approach can not only obtain the desired level of passenger satisfaction, but also reflect the differences and consensus of passengers' perception, making it practically useful and valuable.

Background: In recent years, fatty acid oxidation has been considered as an important energy source for tumorigenesis and development. There are extensive studies on CPT1A, a kind of fatty acid oxidation rate-limiting enzyme. However, prognostic value and regulatory network of another subtype CPT1B in AML remains elusive. This submission aims to clarify the independent prognostic role of the metabolic molecule CPT1B in CN-AML from cilinical data and molecule levels including mRNA, miRNA and lncRNA. Method: First, we analysed the CPT1B expression in AML conhort via online database “GEPIA”. Following the assessments on differential mRNA, miRNA, lncRNA expression analysis on the paired CPT1B high and low expression groups, there were abnormal changes at different molecular levels. Moreover, we determine the preliminary predictions on the regulatory network of CPT1B in AML by constructing miRNA-mRNA and ceRNA networks. The study had yielded a series of molecules that potentially proved the prognostic prediction and metabolic function of CPT1B from the side. Finally, we analysed the CPT1B expression in our own conhort to make a verification. Result: Notably, there was a significantly high expression of CPT1B in AML patients than normal people and was associated with the poor outcome. Molecules from different levels verified our finding and revealed the possible regulatory mechanism of CPT1B in AML. Conclusion: CPT1B is a potential prognostic factor as well as a therapeutic target of AML.

KRAS is one of the most commonly mutated oncogenes in cancers and therapeutics directly targeting the KRas have been challenging. Among the different known mutants, KRasG12C has been proved to be successfully targeted recently. Several covalent inhibitors selectively targeting KRasG12C have shown promising efficacy against cancers harboring KRASG12C mutation in clinical trials and AMG510 (sotorasib) has been approved for the treatment of KRASG12C-mutated locally advanced or metastatic non-small cell lung cancer. However, the overall responsive rate of KRasG12C inhibitors was around 50% in patients with non-small cell lung cancer and the efficacy in patients with colorectal cancer or appendiceal cancer appears to be less desirable. It is of great importance to discover biomarkers to distinguish patients who are likely benefitted. Moreover, adaptive resistance would occur inevitably with the persistent administration like other molecularly targeted therapies. Several combinatorial regimens have been studied in an effort to potentiate the efficacy of KRasG12C inhibitors in preclinical settings. This review summarized the recent progress of covalent KRasG12C inhibitors with a focus on identifying biomarkers to predict or monitor the efficacy and proposing rational drug combinations based on elucidation of the mechanisms of drug resistance.

The state of health (SOH) estimation is of great importance in the battery management system. However, the accurate real time state of health estimation is still a challenge because the capacity of the battery cannot be precisely monitored and measured in real-time. In response to this problem, this paper proposes an online SOH monitoring method for lithium-ion battery (LIB) based on the charge-discharge feature of battery in real time. Firstly, the relationship between the charge-discharge feature and SOH through principal component analysis (PCA) is analyzed, and the appropriate feature which can precisely describe the battery aging process is then selected. Secondly, as to decompose and denoise the collected charge-discharge feature data, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is applied. The major trend obtained is predicted by logistic regression based on the sliding time window (LR-STW), and the minor fluctuation is predicted by Kalman Filter (KF). Finally, an online SOH estimation method based on RBF neural network is proposed to establish the mapping relationship between feature and SOH. The proposed method was verified by NASA experimental data. The experimental results show that the proposed method can accurately predict the LIB SOH in real time, and the robustness of the proposed method is strengthened and promised.

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.

In multi-dimensional classification (MDC), a number of class variables are assumed in the output space with each of them specifying the class membership w.r.t. one heterogeneous class space. One major challenge in learning from MDC examples lies in the heterogeneity of class spaces, where the modeling outputs from different class spaces are not directly comparable. To tackle this problem, we propose a new strategy named decomposed label encoding which enables modeling alignment for MDC in an encoded label space derived from one-vs-one (OvO) decomposition. Specifically, the original MDC output space is transformed into a ternary encoded label space by conducting OvO decomposition w.r.t. each class space. Then, the manifold structure in the feature space is exploited to enrich the labeling information in the encoded label space. Finally, the predictive model is induced by fitting the metric-aligned modeling outputs with enriched labeling information. Extensive experiments over twenty benchmark data sets clearly show the superiority of the proposed MDC strategy against state-of-the-art approaches.

The digital fabrication of a maxillary obturator with a 3D-printed polyetheretherketone (PEEK) framework is described. Digital oral data were scanned for the computer-aided design (CAD) of the framework and the 3D printing of a preliminary resin cast. The framework was accurately printed from a PEEK filament material. A secondary impression was made to fabricate the definitive cast. The PEEK framework exhibited precise fit, excellent retention, and reduced weight compared with a typical metal framework.

Image fusion synthesizes a new image from multiple images of the same scene. The synthesized image should be suitable for human visual perception and follow-up high-level image-processing tasks. However, existing methods focus on fusing low-level features, ignoring high-level semantic perception information. We propose a new end-to-end model to obtain a more semantically consistent image in infrared and visible image fusion, termed semantic-supervised dual-discriminator generative adversarial network (SDDGAN). In particular, we design an information quantity discrimination (IQD) block to guide fusion progress. For each source image, the block determines the weight for preserving each semantic object's feature. By this way, the generator learns to fuse various semantic objects via different weights to preserve their characteristics. Moreover, the dual discriminator is employed to identify the distribution of infrared and visible information in the fused image. Each discriminator acts on a certain modality (infrared/visible) of different semantic objects in the fused image to preserve and enhance their modality features. Thus, our fused image is more informative. Both the thermal radiation in the infrared image and the visible image texture details can be well preserved. Qualitative and quantitative experiments demonstrate the superiority of our SDDGAN over state-of-the-art methods in terms of visual effects, efficiency, and quantitative metrics.

Neurogenic bladder and bowel are two critical autonomic complications following traumatic spinal cord injury (TSCI). Chronic lower urinary tract and bowel dysfunctions can lead to secondary complications, drastically affect the quality of life and significantly increase the risk of hospital readmission and mortality. Other than symptomatic treatments, a few effective therapies are available. Combined acupuncture and moxibustion therapy has positive effects on improving nerve repair and functional recovery in the early phases following TSCI. However, whether it is effective for TSCI-related chronic urinary and bowel dysfunctions remains unknown. This report presents the case of a 26-year-old male patient who experienced neurogenic bladder and bowel dysfunction following TSCI due to an accidental fall from height for more than 10 months and visited our department for combined acupuncture and moxibustion therapy. After 48 treatment sessions, he regained voluntary urination and defecation to a large extent. Urodynamic testing showed recovered bladder compliance and improved detrusor contractility. Symptom assessment with the Qualiveen Short-Form and neurogenic bowel dysfunction scores demonstrated reduced symptom severity. This case suggests that combined acupuncture and moxibustion therapy might help to restore the physiological functions of the lower urinary and digestive tracts following TSCI and might be a promising alternative for the treatment of neurogenic bladder and bowel dysfunction in patients with TSCI.

According to previous studies, the workforce competencies of engineers in manufacturing industries generally comprise technical, generic, and knowledge competencies. However, these categories of workforce competencies are insufficient for firms whose manufacturing processes are very easily interrupted by anomalies and whose core competency is highly related to production yield. In addition to drawing attention to the importance of identifying engineers’ problem-solving competencies, this article proposes a procedure to identify and rate workforce competencies for firms in the manufacturing industry. An IC packaging process engineer was used as an example to illustrate the proposed competency identification and rating procedure. In contrast to previous studies, this article suggests that problem-solving competencies can be identified from engineers’ responses to the anomalies commonly encountered in each step of the manufacturing process and that operational competency can be identified from the knowledge, techniques, or capabilities required to fulfill a task in each step of the manufacturing process. For the IC packaging and testing industry, this article can help human resources not only evaluate the competency of job candidates but also design training courses according to the job tenure of engineers. Furthermore, this article results can be used to design teaching material for apprenticeship to help engineers develop competencies in a structured manner and enhance their sensitivities to production-line problems.

The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts on environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88%. We have released the source codes for public use.

This article presents a framework for real-time analysis and visualization of ballet dance movements performed within a Cave Virtual Reality Environment (CAVE). A Kinect sensor captures and extracts dance-based movement features, from which a topology preserved "posture space" is constructed using a spherical self-organizing map (SSOM). Recordings of dance movements are parsed into gestural elements by projection onto the SSOM to form unique trajectories in posture space. Dependencies between postures in a trajectory are modeled using a Markovian empirical transition matrix, which is then used to recognize attempted movements. This allows for quantitative assessment and feedback of a student's performance, delivered using concurrent, localized visualizations together with a performance score based on incremental dynamic time warping (IDTW).

Objectives. To examine temporal trends in the classification of opioid-involved overdose deaths (OODs) and racial variation in the classification of specific types of opioids used. Methods. We analyzed OODs coded as other or unspecified narcotics from 1999 to 2018 in the United States using data from the National Vital Statistics System and the Centers for Disease Control and Prevention. Results. The total proportion of OODs from unspecified narcotics decreased from 32.4% in 1999 to 1.9% in 2018. The proportion of OODs from unspecified narcotics among African American persons was approximately 2-fold greater than that of non-Hispanic White persons until 2012. Similarly, the proportion of OODs from unspecified narcotics among Hispanic persons was greater than that of White persons until 2015. After we controlled for death investigation system, African American persons had a higher incidence rate of OODs from unspecified narcotics compared with White persons. Conclusions. There have been significant improvements in the specification OODs over the past 20 years, and there has been significant racial disparity in the classification of OODs until about 2015. The findings suggest a health data disparity; the excessive misclassification of OODs is likely attributable to the race/ethnicity of the decedent.

In this paper, a non‐negative matrix factorization feature expansion (NMFFE) approach was proposed to overcome the feature‐sparsity issue when expanding features of short‐text. First, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Second, we utilized the Dual regularization non‐negative matrix tri‐factorization (DNMTF) algorithm to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short‐text to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89%, and 1.79% on three data sets: Web snippets, Twitter sports, and AGnews, respectively compared with the Word2Vec algorithm and Char‐CNN algorithm. It indicated that the NMFFE algorithm was better than the BOW algorithm and the Char‐CNN algorithm in terms of classification accuracy and algorithm robustness.

Acinetobacter baumannii causes healthcare-associated infections worldwide. Capsular polysaccharide (CPS) is shown an important virulence factor of A. baumannii both in vitro and in vivo. Capsule locus 2 (KL2) for CPS is the most common KL type and is associated with carbapenem resistance. It is unclear whether KL2 is related to the clinical outcome of invasive A. baumannii infection. Here we had followed patients with A. baumannii bacteraemia prospectively between 2009 and 2014. One-third of the unduplicated blood isolates were randomly selected each year for microbiological and clinical studies. The KL2 gene cluster was identified using polymerase chain reaction. A total of 148 patients were enrolled randomly. Eighteen isolates (12.2%) carried KL2, and 130 isolates (87.8%) didn't. Compared with non-KL2 isolates, KL2 isolates had significantly higher resistance to imipenem, sulbactam, and tigecycline. Compared with the non-KL group, in the KL2 group, the hospital stay before development of bacteraemia was longer (P

Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. However, most of the chatbots act as a simple question answering system that responds with formulated answers. Traditional conversational chatbots usually adopt a retrieval-based model that requires a large amount of conversational data for retrieving various intents. Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. We propose a method to build a task-oriented chatbot using a sentence generation model that generates sequences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence and a discriminator that judges the sentences by comparing the generated and the ground-truth sentences. In the generator, we combine the attention model with the sequence-to-sequence model using hierarchical long short-term memory to extract sentence information. For the discriminator, our reward mechanism assigns low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model that generates more diverse and information-rich sentences than those of the existing approaches.

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach.

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.

This article proposes a novel and efficient dual-space co-evolutionary memetic algorithm (DCMA) to tackle a practical hybrid differentiation flowshop scheduling problem with limited buffer constraints. In this scheduling problem, jobs are divided into different types and each job consists of multiple parts. The manufacturing of a job involves three stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. Due to the assembly operation and limited buffers between adjacent stages, deadlock and blocking will occur. We formulate the problem and present a deadlock handling policy to guarantee all schedules feasible. Then, we propose the DCMA metaheuristic to approximate the optimal solutions in acceptable time. Global exploration of DCMA is performed by a hybrid of three parts: 1) a continuous optimizer to be executed in continuous search space; 2) a discrete optimizer to be executed in combinatorial solution space; and 3) a meta-Lamarckian learning-based selection mechanism for adaptively determining that which optimizer is more suitable for current global exploration campaign. To balance exploration and exploitation, three problem-special local search strategies are presented, which collaborates with each other and are adaptively started to avoid high computational costs. The effect of parameter setting on DCMA is checked by the Taguchi method of design of experiment. The computational experiments demonstrate the effectiveness of DCMA special designs, and show that DCMA performs better than the existing algorithms for the considered problem.

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous period time. In this model, the spatio-temporal features are fused with the weights of dual attention learning, and the fused features are input into the softmax classifier for emotion classification. In addition, we utilize SJTU Emotion EEG Dataset (SEED) to appraise the feasibility and effectiveness of the proposed algorithm. Finally, experimental results display that the 3DCANN method has superior performance over the state-of-the-art models in EEG emotion recognition.

OBJECTIVE Free tissue transfer is widely used for head and neck reconstruction. In certain circumstances, vein grafting is required to elongate free flap pedicles to connect them to appropriate recipient vessels. Because of controversy regarding the use of interposition vein grafts in free tissue reconstruction, this paper reports vein graft indications, techniques, safety, and outcomes for head and neck microvascular surgery. METHODS Twenty-six patients (23 men and 3 women) who underwent interposition vein grafting concurrent with free tissue transfer were included in this study. The most common reason for head and neck reconstruction with vein graft was tumor recurrence, followed by flap salvage. The interposition vein grafts were applied in two manners as temporary arteriovenous (A-V) loop and conduit to extend the length of the free flap for venous drainage. RESULTS The most common reconstructions were anterolateral thigh flaps (15 cases), followed by vastus lateralis myocutaneous (3 cases) and radial forearm (2 cases) flaps. The common recipient vessels were superior thyroid artery, superficial temporal artery and external jugular vein. The free flap loss rate was 7.7% with vein grafts and 4.9 without vein grafts (p = 0.380). The free flap complication rate was 50.0% and 16.8% in patients with and without vein grafts, respectively (p

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs). Recent works have shown that attention-based RL models outperform recurrent neural network-based methods on these problems in terms of both effectiveness and efficiency. However, existing RL models simply aggregate node embeddings to generate the context embedding without taking into account the dynamic network structures, making them incapable of modeling the state transition and action selection dynamics. In this work, we develop a new attention-based RL model that provides enhanced node embeddings via batch normalization reordering and gate aggregation, as well as dynamic-aware context embedding through an attentive aggregation module on multiple relational structures. We conduct experiments on five types of VRPs: 1) travelling salesman problem (TSP); 2) capacitated VRP (CVRP); 3) split delivery VRP (SDVRP); 4) orienteering problem (OP); and 5) prize collecting TSP (PCTSP). The results show that our model not only outperforms the learning-based baselines but also solves the problems much faster than the traditional baselines. In addition, our model shows improved generalizability when being evaluated in large-scale problems, as well as problems with different data distributions.

Large and comprehensive datasets are crucial for the development of vehicle ReID. In this paper, we propose a large vehicle ReID dataset, called VERI-Wild 2.0, containing 825,042 images. It is captured using a city-scale surveillance camera system, which consists of 274 cameras covering 200 km2. Specifically, the samples in our dataset present rich diversities thanks to the long time span collecting settings, unconstrained capturing viewpoints, various illumination conditions, and diversified background environments. Furthermore, we define a challenging test set containing about 400K vehicle images that do not have any camera overlap with the training set. Besides, we also design a new method. We observe that the orientation is a crucial factor for vehicle ReID. To match vehicle pairs captured from similar orientations, the learned features are expected to capture specific detailed differential information, while features are desired to capture the orientation invariant common information when matching samples captured from different orientations. Thus a novel disentangled feature learning network(DFNet) is proposed. It explicitly considers the orientation information for vehicle ReID, and concurrently learns the orientation specific and common features that thus can be adaptively exploited via a hybrid ranking strategy when dealing with different matching pairs. The comprehensive experimental results show the effectiveness of our proposed method.

In the last few years, mobile cloud computing (MCC) gains a huge development because of the popularity of mobile applications and cloud computing. User authentication and access control are two indispensable security components in the MCC environment. To the best of our knowledge, they are generally designed in different procedures. Access control can be executed after the authentication completes successfully. In order to improve efficiency, this paper constructs an integrated scheme of authentication and hierarchical access control using self-certified public key cryptography (SCPKC) and the Chinese remainder theorem (CRT) for MCC environment. The proposed scheme can achieve mutual authentication while determining the access rights of mobile users without storing any access control list in the MCC service provider side. Besides, we also give a dynamic adding or deletion of MCC service provider to efficiently address potential changes in the hierarchy. The security of our proposed scheme is proved by the random oracle model. Compared with recently related multi-server authentication schemes for the MCC environment, the proposed scheme not only adds a new function of hierarchical access control but also has better computation and communication efficiencies. Therefore, the proposed scheme is more suitable for real-life MCC applications.

This article investigates the multirobot cooperative navigation problem based on raw visual observations. A fully end-to-end learning framework is presented, which leverages graph neural networks to learn local motion coordination and utilizes deep reinforcement learning to generate visuomotor policy that enables each robot to move to its goal without the need of environment map and global positioning information. Experimental results show that, with a few tens of robots, our approach achieves comparable performance with the state-of-the-art imitation learning-based approaches with bird-view state inputs. We also illustrate our generalizability to crowded and large environments and our scalability to ten times number of the training robots. In addition, we demonstrate that our model trained for multirobot case can also improve the success rate in the single-robot navigation task in unseen environments.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

Graphical abstract, Highlights • PLA2G16 is up-regulated in CRC, and high expression of PLA2G16 is associated with the advanced stages. • PLA2G16 promotes the malignant progression of CRC through the Hippo signaling pathway. • GCK exerts its anti-CRC effects by inhibiting the protein expression of PLA2G16. • Provide a new insights towards the development of effective therapeutic strategies for CRC treatment by targeting PLA2G16., Introduction Colorectal cancer (CRC) is a common malignant tumor with a high global incidence, metastasis rate and low cure rate. Changes in lipid metabolism-related genes can affect the occurrence and development of CRC, and may be a potential therapeutic target for CRC. Therefore, starting from lipid metabolism-related genes to find natural medicines for tumor treatment may become a new direction in CRC research. Objectives This study aimed to investigate the effect of PLA2G16, a key gene involved in lipid metabolism, on the biological function of CRC, and whether the anti-CRC effect of GCK is related to PLA2G16. Methods To explore the role of PLA2G16 in CRC in vitro and in vivo, we performed cell proliferation, migration, invasion and nude mice tumorigenesis assays. As for the mechanism, we designed RNA-seq analysis and verified by western blotting and immunofluorescence experiments. Subsequently, we found the anti-CRC effect of GCK is related to PLA2G16 through western blotting and rescue experiments. Results We showed that PLA2G16 was significantly higher in CRC tissues than the adjacent normal appearing tissues, and high PLA2G16 expression correlates with unfavorable prognosis of CRC patients. Further, PLA2G16 promoted the malignant progression of CRC by inhibiting the Hippo signaling pathway determined by RNA-seq analysis, and GCK exerted anti-CRC effects by inhibiting the protein expression of PLA2G16 in vitro and in vivo. Conclusion Our results suggested that PLA2G16 promote the malignant progression of CRC by inhibiting the Hippo signaling pathway and the anti-CRC effect of GCK is through inhibiting the protein expression of PLA2G16.

Background Large-scale quarantine facilities staffed with non-healthcare workers (NHCW) were instrumental in preventing community spread of COVID-19 (coronavirus disease of 2019). The objective of this study was to determine the effectiveness of a newly developed procedural skills training framework in ensuring personal protective equipment (PPE) compliance of PPE-naïve NHCWs. Materials and methods We developed a PPE procedural skills training framework for NHCWs using the Learn, See, Practice, Prove, Do, and Maintain (LSPPDM) framework and international guidelines on PPE for healthcare workers. The NHCWs underwent PPE training using this framework, conducted by a team of Infection Prevention nurses, prior to being stationed within the CCF. Effectiveness of the LSPPDM PPE training framework was assessed using: 1) competency assessment scores for NHCWs, 2) PPE compliance rates from daily audit findings, and, 3) healthcare-associated COVID-19 infection rates of NHCWs. Results A total of 883 NHCWs had completed the PPE training and demonstrated competency in PPE compliance, fulfilling 100% of the checklist requirements. Mean PPE compliance of all NHCWs during the 11-week study period was noted to be >96%. The post-implementation improvement was statistically significant when the compliance was expressed in 3-days blocks) and in bed management staff (P = < 0.05). None of the 883 NHCWs who underwent PPE training via the LSPPDM framework were diagnosed with healthcare-associated COVID-19 infection. Conclusions An evidence-based skills training framework is effective in PPE training of large numbers of NHCWs, resulting in high compliance of appropriate PPE use and prevention of healthcare-associated COVID-19 infection.

BACKGROUND Hormone receptor-positive, human epidermal growth factor receptor II (HER2)-negative luminal B1 breast cancer is associated with a higher risk of disease relapse than luminal A breast cancer. Therefore, we assessed and compared the distant metastasis pattern and clinical outcomes associated with luminal B1 and luminal A breast cancer in an Asian population. METHODS In this observational study, we assessed patients with estrogen receptor-positive, HER2-negative breast cancer who underwent surgery from 2009 to 2016. Patients were classified into luminal A or luminal B1 subsets via immunohistochemical analysis. Disease-free survival, post-metastasis survival, and overall survival were estimated; time to disease relapse and patterns of distant metastasis were compared. Risk of relapse and mortality were assessed using Cox proportional hazards model. RESULTS Patients with luminal B1 breast cancer (n = 677) were significantly younger and had larger tumors and a higher degree of affected axillary lymph nodes, lymphovascular invasion, and tumor necrosis than those with luminal A breast cancer (n = 630). Higher rates of local recurrence and distant metastasis were observed for luminal B1 (both p < 0.05); however, no difference was observed in the specific distant metastatic sites. We observed a significant increase in disease relapse risk in luminal B1 patients compared with that in luminal A (hazard ratio: 2.157, 95% confidence interval: 1.340-3.473, p < 0.05). Patient age, tumor size, stage, lymphovascular invasion, and receiving chemotherapy and hormone therapy were independent risk factors for metastasis and recurrence. Only the luminal B1 subtype (hazard ratio: 5.653, 95% confidence interval: 1.166-27.409, p < 0.05) and stage (hazard ratio: 3.400, 95% confidence interval: 1.512-7.649, p < 0.05) were identified as independent risk factors for post metastatic mortality. CONCLUSION Luminal B1 breast cancer has aggressive tumor biology compared with luminal A breast cancer in the follow-up period. However, there was no significant difference in the disease relapse pattern between the groups.

STUDY QUESTION Do women with polycystic ovary syndrome (PCOS) have a greater risk of adverse pregnancy complications (gestational diabetes, preeclampsia, cesarean section, placental abnormalities) and neonatal outcomes (preterm birth, small for gestational age, prolonged delivery hospitalization) compared to women without a PCOS diagnosis and does this risk vary by BMI, subfertility and fertility treatment utilization? SUMMARY ANSWER Deliveries to women with a history of PCOS were at greater risk of complications associated with cardiometabolic function, including gestational diabetes and preeclampsia, as well as preterm birth and prolonged length of delivery hospitalization. WHAT IS KNOWN ALREADY Prior research has suggested that women with PCOS may be at increased risk of adverse pregnancy outcomes. However, findings have been inconsistent possibly due to lack of consistent adjustment for confounding factors, small samples size and other sources of bias. STUDY DESIGN, SIZE, DURATION Massachusetts deliveries among women ≥18 years old during 2013–2017 from state vital records linked to hospital discharges, observational stays and emergency department visits were linked to the Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) and the Massachusetts All-Payers Claims Database (APCD). PARTICIPANTS/MATERIALS, SETTING, METHODS PCOS was identified by ICD9 and ICD10 codes in APCD prior to index delivery. Relative risks (RRs) and 95% CI for pregnancy and delivery complications were modeled using generalized estimating equations with a log link and a Poisson distribution to take multiple cycles into account and were adjusted a priori for maternal age, BMI, race/ethnicity, education, plurality, birth year, chronic hypertension and chronic diabetes. Tests for homogeneity investigated differences between maternal pre-pregnancy BMI categories ( MAIN RESULTS AND THE ROLE OF CHANCE Among 91 825 deliveries, 3.9% had a history of PCOS. Women with a history of PCOS had a 51% greater risk of gestational diabetes (CI: 1.38–1.65) and a 25% greater risk of preeclampsia (CI: 1.15–1.35) compared to women without a diagnosis of PCOS. Neonates born to women with a history of PCOS were more likely to be born preterm (RR: 1.17, CI: 1.06–1.29) and more likely to have a prolonged delivery hospitalization after additionally adjusting for gestational age (RR: 1.23, CI: 1.09–1.40) compared to those of women without a diagnosis of PCOS. The risk for gestational diabetes for women with PCOS was greater among women with a pre-pregnancy BMI LIMITATIONS, REASONS FOR CAUTION PCOS was defined by ICD documentation prior to delivery so there may be women with undiagnosed PCOS or PCOS diagnosed after delivery included in the unexposed group. The study population is limited to deliveries within Massachusetts among most private insurance payers and inpatient or observational hospitalization in Massachusetts during the follow-up window, therefore there may be diagnoses and or deliveries outside of the state or outside of our sample that were not captured. WIDER IMPLICATIONS OF THE FINDINGS In this population-based study, women with a history of PCOS were at greater risk of pregnancy complications associated with cardiometabolic function and preterm birth. Obstetricians should be aware of patients’ PCOS status and closely monitor for potential pregnancy complications to improve maternal and infant perinatal health outcomes. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the NIH (R01HD067270). S.A.M. receives grant funding from NIH, AbbVie and the Marriot Family Foundation; payment/honoraria from the University of British Columbia, World Endometriosis Research Foundation and Huilun Shanghai; travel support for attending meetings for ESHRE 2019, IASP 2019, National Endometriosis Network UK meeting 2019; SRI 2022, ESHRE 2022; participates on the data safety monitoring board/advisory board for AbbVie, Roche, Frontiers in Reproductive Health; and has a leadership role in the Society for Women’s Health Research, World Endometriosis Research Foundation, World Endometriosis Society, American Society for Reproductive Medicine and ESHRE. The other authors have no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.

Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. Within this multiscale feature fusion module, the high-level features are introduced directly into the low-level features at different convolutional stages. Besides, deep supervision provides integrated direct supervision for convolutional feature fusion, which is helpful to improve model convergency and final performance of crack detection. Multiscale convolutional features learned at different convolution stages are fused together to robustly represent cracks, whose geometric structures are complicated and hardly captured by single-scale features. To demonstrate its superiority and generalizability, we evaluate the proposed network on three public crack data sets, respectively. Sufficient experimental results demonstrate that our method outperforms other state-of-the-art crack detection, edge detection, and image segmentation methods in terms of F1-score and mean IU.

Seizure prediction from intracranial electroencephalogram (iEEG) has great potentials to improve the life quality of epileptic patients, but faces big challenges. One major difficulty lies in that brain signal changes occasionally during long-term monitoring, due to electrode movements or the nonstationary brain dynamics. This leads to a serious situation that a predictor learned from historical data usually only works well in a short time period as long as the data do not change much. While in a long time span, the performance of the learned features decreases or even become totally invalid. To deal with the problem, we propose a domain adaptation convolutional neural network to learn robust preictal features that is invariant across different time periods. Specifically, the preictal feature is learned and enhanced by multi-scale temporal convolutions in the neural network. Based on this, a domain adaptation method is adopted to constrain that the learned features should be invariant across different time periods. Experimental results demonstrate that our approach can effectively improve seizure prediction performance against signal changes.

Few-shot human-object interaction (FS-HOI) recognition aims at inferring new interactions between human actions and surrounding objects merely with a few available instances. It is beneficial to alleviate the long-tail and combinatorial explosion problems in human-object interaction (HOI). Nevertheless, the existing FS-HOI methods only focus on modeling the relationships between labeled samples and unlabeled samples in the Euclidean domain, which neglects the rich relational structures of the visual information among labeled samples and between human actions and objects. Accordingly, we tackle the few-shot HOI task in the non-Euclidean domain and present a graph-based model, namely, task-oriented high-order context graph network (THCG-Net). It contains a task attention module (TA-Module) and a high-order context graph module (HG-Module). In TA-Module, an attention mechanism is designed by utilizing task information to build a task-oriented space, in which the discriminative information for the current task (episode) is captured by embedding the visual features into the task-oriented space. The HG-Module is proposed to construct a task-level graph and takes the context information as high-order knowledge, which provides discriminative guidance for propagating visual information. It captures the discriminability among different categories while highlights the commonality of related categories adaptively, which effectively transfers knowledge to related categories. Extensive experimental results on two benchmark datasets, HICO-FS and TUHOI-FS, are provided. It demonstrates that our THCG-Net significantly outperforms the state-of-the-art approaches, which proves its impressive effectiveness in recognizing various human actions and surrounding objects in few-shot scenarios.

Background/Purpose The long-term disease course and efficacy of maintenance therapies have rarely been investigated in Asian patients with neuromyelitis optica spectrum disorder (NMOSD). Methods Medical records of patients fulfilling the 2015 International Consensus Diagnostic Criteria for NMOSD at three medical centers in Taiwan were systematically analyzed. Linear regression analysis was performed to investigate factors related to annualized relapse rate (ARR); survival analysis was used to estimate the relapse-free intervals among therapies. Results A total of 557 relapses affecting 648 regions (202 optic neuritis, 352 acute myelitis, and 94 brain syndromes) in 204 patients were analyzed during a follow-up period of 69.5 months (range, 1–420). Up to 36.1% of myelitis-onset patients and 24.0% of optic neuritis-onset patients exhibited a limited form disease, defined as having one or more relapses confined to the same region. The median ARR was significantly lower in patients with limited form disease than those with relapses involving multiple regions (0.30 vs. 0.47, respectively). An older age at disease onset was associated with a lower ARR (p = 0.023). Kaplan–Meier analysis showed that the estimated time (months) to next relapse was longest in rituximab-treatment group (58.0 ± 13.2), followed by immunosuppressant (48.5 ± 4.8) or prednisone (29.6 ± 4.6) groups, and shortest in those without maintenance therapy (27.6 ± 4.2) (p = 8.1 × 10−7). Conclusion Limited form disease and older age at disease onset are associated with a lower relapse rate in NMOSD. Compared to no maintenance therapy, rituximab and immunosuppressant significantly reduce the relapse risks.

Background Before 2010, guidelines recommended adenosine 6, 12, and a repeat dose of 12 mg for paroxysmal supraventricular tachycardia (PSVT). After 2010, these doses were reduced to two. This study aims to outline adenosine using trend from 2000 to 2012 in Taiwan emergency departments (EDs). Methods This was an ecological study. PSVT were drawn from one million individuals of the National Health Insurance Database. The χ2 test was used to determine an association between different adenosine doses and other antiarrhythmic drugs (OADs), including verapamil, diltiazem, amiodarone, digoxin, and labetalol. Results There were 3361 PSVT visits from 2000 to 2012; 834 (24.8%) did not receive an antiarrhythmic drug, and 2527 (75.2%) did, either adenosine with/without OADs or OADs alone. The use of an OAD was significantly different between the adenosine 6–18 mg and 19 + mg groups. Conclusions Most PSVT episodes converted with adenosine within 18 mg, and the success conversion rate was 62.2%. It could be up to 65.2% if they received more. Of the patients who did not have their PSVT reverted with

Aim The safety concern of statins is still a major issue for Asians. The aim of this study is to compare the risk of statin-associated adverse events among potent statins. Methods We included patients from the Taiwan National Health Insurance Research Database who had been treated with atorvastatin, rosuvastatin, or pitavastatin and were without diabetes at baseline. They were classified into three groups: usual-dose statin (atorvastatin 10 mg/d or rosuvastatin 5-10 mg/d), high-dose statin (atorvastatin 20-40 mg/d and rosuvastatin 20 mg/d), and pitavastatin (2-4 mg/d). The primary endpoint is a composite of safety events, including hepatitis, myopathy, and new-onset diabetes mellitus (NODM). We matched age, sex, and year of recruitment among the three groups (n=50,935 in each group) and then used the multivariate Cox proportional hazards model to evaluate the relation between the safety endpoint and different statin groups. Results After a mean follow-up of 3.08±0.83 years, the safety events occurred in 9.84% in the pitavastatin group, 10.88% in the usual-dose statin group, and 10.49% in high-dose statin group. The multivariate Cox proportional hazards model indicated that usual-dose statin and high-dose statin were associated with a higher risk of the composite safety events compared with pitavastatin (adjusted hazard ratio [aHR]: 1.12, 95% confidence interval [CI]: 1.08-1.17 for usual-dose statin and aHR: 1.06, 95% CI: 1.02-1.10 for high-dose statin). The risks of hepatitis requiring hospitalization and NODM were especially lower in pitavastatin group. Conclusions Compared with atorvastatin and rosuvastatin, pitavastatin might be associated with a lower risk of safety events in Asians.

Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation, environmental evaluation, and so on. Previous image recognizers cannot fulfill this task due to three challenges: 1) localizing visually/semantically salient regions within each aerial photograph in a weakly annotated context due to the unaffordable human resources required for pixel-level annotation; 2) aerial photographs are generally with multiple informative attributes (e.g., clarity and reflectivity), and we have to encode them for better aerial photograph modeling; and 3) designing a cross-domain knowledge transferal module to enhance aerial photograph perception since multiresolution aerial photographs are taken asynchronistically and are mutually complementary. To handle the above problems, we propose to optimize aerial photograph's feature learning by leveraging the low-resolution spatial composition to enhance the deep learning of perceptual features with a high resolution. More specifically, we first extract many BING-based object patches (Cheng et al., 2014) from each aerial photograph. A weakly supervised ranking algorithm selects a few semantically salient ones by seamlessly incorporating multiple aerial photograph attributes. Toward an interpretable aerial photograph recognizer indicative to human visual perception, we construct a gaze shifting path (GSP) by linking the top-ranking object patches and, subsequently, derive the deep GSP feature. Finally, a cross-domain multilabel SVM is formulated to categorize each aerial photograph. It leverages the global feature from low-resolution counterparts to optimize the deep GSP feature from a high-resolution aerial photograph. Comparative results on our compiled million-scale aerial photograph set have demonstrated the competitiveness of our approach. Besides, the eye-tracking experiment has shown that our ranking-based GSPs are over 92% consistent with the real human gaze shifting sequences.

Trajectory prediction for mobile phone users is a cornerstone component to support many higher-level applications in LBSs (Location-Based Services). Most existing methods are designed based on the assumption that the explicit location information of the trajectories is available (e.g., GPS trajectories). However, collecting such kind of trajectories lays a heavy burden on the mobile phones and incurs privacy concerns. In this paper, we study the problem of trajectory prediction based on cell-id trajectories without explicit location information and propose a deep learning framework (called DeepCTP) to solve this problem. Specifically, we use a multi-graph embedding method to learn the latent spatial correlations between cell towers by exploiting handoff patterns. Then, we design a novel spatial-aware loss function for the encoder-decoder network to generate cell-id trajectory predictions. We conducted extensive experiments on real datasets. The experiment results show that DeepCTP outperforms the state-of-the-art cell-id trajectory prediction methods in terms of prediction error.

The energy-efficient flexible job shop scheduling problem (FJSP) has attracted much attention in deterministic cases; however, uncertainty is seldom incorporated into energy-efficient FJSP and the neglecting of uncertainty will greatly diminish the application value of scheduling results. These make it necessary to handle uncertainty in the problem. In this study, energy-efficient fuzzy FJSP (EFFJSP) is considered and a bi-population evolutionary algorithm with feedback (FBEA) is proposed to minimize fuzzy makespan and fuzzy total energy consumption and maximize minimum agreement index. The computation of fuzzy energy consumption is given and four heuristics are proposed to produce the initial population. An effective method is presented to evaluate the quality of two populations and a feedback mechanism based on population quality is adopted to dynamically adjust the size of each population. A novel process of reproduction, crossover and mutation is developed based on feedback. An enhanced local search is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of FBEA. FBEA can provide promising results for EFFJSP.

Influence maximization, which aims to find a small number of influencers in a social network to maximize the influence spread under a certain propagation model, has attracted substantial attention due to its widespread applications, such as viral marketing and social advertising. However, most of the former studies focus primarily on maximizing the influence spread of a single product, which is not very common in actual marketing campaigns. In this article, we study a novel compatible influence maximization problem for two considered products, which involves more complex product adoption decisions of users in many realistic settings. The problem is NP-hard, and the objective function no longer exhibits monotonicity and submodularity. We propose an adapted greedy algorithm to solve the problem effectively. Due to its poor computational efficiency in the seed selection, we further propose a fast greedy algorithm that integrates several effective optimization strategies without compromising the accuracy and devise an efficient heuristic algorithm to approximate the influence spread calculation. Extensive experiments over real-world social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods.

Background:Although public-academic partnerships (PAPs) to improve the health and well-being of vulnerable populations have proliferated in public care for youth, existing literature lacks information about whether PAPs lead to public care agency leaders’ use of research evidence and promote youth mental health and well-being. Aims and objectives:The document analysis was conducted to understand PAP contexts and mechanisms leading to public care agency leaders’ use of research evidence. This paper introduces US public mental health and child welfare systems, shares strategies of identifying PAPs, obtaining and conducting systematic document review of PAPs, and documents analysis findings. Methods:This project conducted document analysis of US PAPs aiming to improve mental health and promote well-being of youth aged 12–25 years. Findings:The 23 PAPs analysed had diverse partnership goals including implementation and dissemination of research/evaluation evidence, information sharing, and prioritising and streamlining research priorities. PAPs sustained longer than 10 years had more focused goals of programme and policy evaluations and professional training, while PAPs 10 years or newer were engaged in more diverse goals. The majority of PAPs used journal articles, presentations, and multimedia as dissemination strategies of findings. Fewer than half of the PAPs reported on use of PAP-generated evidence in subsequent decision making by public care agency leaders. Discussion and conclusions:Further research should examine which mechanisms link partnership contexts, PAP leaders’ research evidence use, and youth outcomes improvement. Future research should also examine PAPs by detailed stages of development and ask PAP leaders directly about their evidence use.

The first-principles calculations demonstrate that covalently bonded (cb) heterojunction and van der Waals (vdW) heterojunction can coexist in silicene/CeO2 heterojunctions, due to the different stacking patterns. Especially, the cb heterojunction with band gap of 1.97 eV, forms a type-II heterojunction, exhibits good redox performance and has high-effective optical absorption spectra, thus it is a promising photocatalyst for overall water splitting. Besides, for the vdW heterojunction, the Dirac cone of silicene is well kept on CeO2 semiconducting substrate, with a considerable energy gap of 0.43 eV, which can be an ideal material in building silicene-based electronic device. These results may open a new gateway in both of nanoelectronic device and energy conversion for silicene/CeO2 nanocomposites.