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In this paper, an optical multiple image encryption method based on ellipsometry reconstruction is proposed. In our proposal, the cryptosystem is designed as multiple paths, and each path is used t...

Network function virtualization (NFV) and mobile edge computing (MEC) are two of the promising technologies that are expected to play a critical role in mobile edge cloud networks, thus satisfying ambitious quality of experience (QoE) requirements of the Internet of things (IoT) applications. In MEC-NFV system, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs) that are connected based on the business logic of service providers. However, the inefficiency of the SFC orchestration process is one major problem due to the dynamic nature of mobile edge cloud networks and abundance of IoT terminals. In this letter, a quantum machine learning (QML)-based scheme is proposed as solution that can handle complex and dynamic SFC orchestration in mobile edge cloud networks. Simulation results show that our proposal significantly provides more than 8-fold reduction of run time compared to the Viterbi algorithm, and the end-to-end delay is only about 1.1 times of the exact solution.

Developing efficient photocatalysts has been proved to be of great importance for many emerging applications, including the removal of recalcitrant organic pollutants in wastewaters and transforming solar energy into important chemical feedstocks. One of the major challenges for high performance photocatalysts is that most semiconductor-mediated photocatalysts suffer severe charge recombination which finally hinders the overall photocatalytic efficiency. Herein, a delicately designed epitaxial grown heterostructure composed of zinc oxide (ZnO) nanowire and ultra-small bismuth oxyiodide (BiOI) nanoflakes was synthesized featuring quasi-free lattice mismatch at the ZnO/BiOI interface. With the advances of suitable p–n junction energy band alignment and minimized lattice mismatch, the synthesized ZnO/BiOI heterostructure shows significantly high interfacial charge transfer and separation efficiency. The high performance heterostructured photocatalyst was applied for the photodegradation of Bisphenol-A (BPA) in an artificial organic wastewater. The results showed that the epitaxial-LMF heterostructure is much superior to both ZnO nanowires and BiOI micro-sheets in catalytic efficiency. Analyzing the time-resolved kinetic features of photo-induced charge carriers revealed that it is the high-degree lattice match at the ZnO/BiOI interface that contributes to the significant charge-separation in the LMF heterostructure, leading to the substantial improvement of photocatalytic efficiency. An interesting finding is that a strong Foster-resonance energy transfer (FRET) from ZnO to BiOI in the heterostructure was observed, which could enhance the solar energy utilization. This study provides a general strategy to improve the interfacial charge separation efficiency of heterostructured photocatalysts, thereby greatly promoting the photocatalytic performance.

ObjectivesThe objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians.Materials and MethodsEight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations.ResultsSixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set.DiscussionOur medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software.ConclusionIntegration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

With the rapid development of modern communication system, more and more attention has been paid to communication interference and anti-interference, which affects the key to smooth communication. In order to ensure the security and stability of communication in modern complex electromagnetic environment, the communication system must have strong anti-interference ability. Wireless communication is the main means of modern communication. Nowadays, the vehicular command and control communication system is mainly composed of ultrashort wave frequency hopping radio. Frequency hopping radio has good anti-interference ability and can carry out accurate and reliable communication in complex electromagnetic environment. In order to deepen the study of FH radio, this paper uses Simulink as an auxiliary means to simulate FH radio, observe the BER of FH radio in single frequency interference, multi frequency interference, tracking interference and channel high signal-to-noise ratio, and analyze the anti-interference performance of FH communication system.

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a form of profound cardiac shock defined as measurable cardiac activity without clinically detectable pulses. p-PEA has a distinct physiology and etiology from VF and true-PEA, and may constitute up to 40% of reported cases of cardiac arrest. Electrical impedance tomography (EIT) uses cutaneous electrodes to generate images based on cross sectional resistance. We utilized EIT to predict the number of interventions required to achieve ROSC from p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential cutaneous thoracic and abdominal electrode arrays. A deep learning model was utilized to detect features within the EIT video clips of the p-PEA disease state to predict the number of treatments required to achieve ROSC. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 1630 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Data were labeled based on the number of interventions required to achieve ROSC (100% O 2 , 100% O 2 + CPR, 100% O 2 + CPR + Epi, ROSC not achieved). Results: This approach yielded receiver operator characteristic curves - area under the curve (ROC-AUC, Figure 1) values of 0.75 for micro (weighted) AUC and 0.78 for macro (unweighted) AUC on a 4 class prediction model. Conclusion: EIT combined with machine learning may differentiate the required treatments needed to achieve ROSC in p-PEA.

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. p-PEA may constitute up to 40% of reported cases of cardiac arrest and its management may be different from ventricular fibrillation or even true-PEA. Currently, diagnosis of p-PEA requires either echocardiography or intravascular catheterization, neither of which are ideal in the prehospital setting. Electrical impedance tomography (EIT) uses skin surface electrodes to generate images based on cross sectional resistance. We investigated the ability of EIT and machine learning (ML) to detect p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% O 2 in 94% N 2 , defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential thoracic and abdominal electrode arrays. A deep learning model was utilized to detect p-PEA using EIT sequences. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 3033 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Results: This technique yielded a receiver operator characteristic curve - area under the curve (ROC-AUC) of 0.91 for detection of p-PEA in the testing dataset (Figure 1), with 84% accuracy, 88% sensitivity, and 84% specificity. Conclusions: EIT combined with machine learning may be able to reliably delineate p-PEA in a hypoxic porcine model. This approach may be promising for non-invasive operator-independent p-PEA detection.

Background Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. Objective This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. Methods Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. Results Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. Conclusions Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

Wear is the main cause of failure of gear transmission system. In order to solve the problem that it is difficult to extract fault features from complex environmental noise for condition recognition, this paper proposes a method based on EEMD and INPP for gearbox wear condition recognition. Firstly, EEMD method is used to decompose the original vibration signal of gearbox, and then the decomposition results are sorted by kurtosis criterion, and the components with large kurtosis index are selected for time-frequency domain analysis to get the time-frequency domain high dimensional feature set; then the improved Neighborhood Preserving Project (INPP) algorithm is used to reduce the dimension of high-dimensional features, and then the reduced dimension features are obtained for state recognition. Finally, the algorithm is verified by the vibration response data of gearbox and compare with several algorithms, and the results show that the proposed algorithm has stable dimensionality reduction effect, good classification effect, and shows the effectiveness of the method.

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

Purpose Hybrid simulation, which is a general technique for obtaining the seismic response of an entire structure, is an improvement of the traditional seismic test technique. In order to improve the analysis accuracy of the numerical substructure in hybrid simulation, the purpose of this paper is to propose an innovative hybrid simulation technique. The technique combines the multi-scale finite element (MFE) analysis method and hybrid simulation method with the objective of achieving the balance between the accuracy and efficiency for the numerical substructure simulation. Design/methodology/approach To achieve this goal, a hybrid simulation system is established based on the MTS servo control system to develop a hybrid analysis model using an MFE model. Moreover, in order to verify the efficiency of the technique, the hybrid simulation of a three-storey benchmark structure is conducted. In this simulation, a ductile column—represented by a half-scale scale specimen—is selected as the experimental element, meanwhile the rest of the frame is modelled as microscopic and macroscopic elements in the Abaqus software simultaneously. Finally, to demonstrate the stability and accuracy of the proposed technique, the seismic response of the target structure obtained via hybrid simulation using the MFE model is compared with that of the numerical simulation. Findings First, the use of the hybrid simulation with the MFE model yields results similar to those obtained by the fine finite element (FE) model using solid elements without adding excessive computing burden, thus advancing the application of the hybrid simulation in large complex structures. Moreover, the proposed hybrid simulation is found to be more versatile in structural seismic analysis than other techniques. Second, the hybrid simulation system developed in this paper can perform hybrid simulation with the MFE model as well as handle the integration and coupling of the experimental elements with the numerical substructure, which consists of the macro- and micro-level elements. Third, conducting the hybrid simulation by applying earthquake motion to simulate seismic structural behaviour is feasible by using Abaqus to model the numerical substructure and harmonise the boundary connections between three different scale elements. Research limitations/implications In terms of the implementation of the hybrid simulation with the MFE model, this work is helpful to advance the hybrid simulation method in the structural experiment field. Nevertheless, there is still a need to refine and enhance the current technique, especially when the hybrid simulation is used in real complex engineering structures, having numerous micro-level elements. A large number of these elements may render the relevant hybrid simulations unattainable because the time consumed in the numeral calculations can become excessive, making the testing of the loading system almost difficult to run smoothly. Practical implications The MFE model is implemented in hybrid simulation, enabling to overcome the problems related to the testing accuracy caused by the numerical substructure simplifications using only macro-level elements. Originality/value This paper is the first to recognise the advantage of the MFE analysis method in hybrid simulation and propose an innovative hybrid simulation technique, combining the MFE analysis method with hybrid simulation method to strike a delicate balance between the accuracy and efficiency of the numerical substructure simulation in hybrid simulation. With the help of the coordinated analysis of FEs at different scales, not only the accuracy and reliability of the overall seismic analysis of the structure is improved, but the computational cost can be restrained to ensure the efficiency of hybrid simulation.

Effective filtering and noise reduction, feature extraction and fault diagnosis, and prognostics technology are important to Prognostics and Health Management (PHM) of equipment. Therefore, a multifeature fusion fault diagnosis method based on the combination of quadratic filtering and QPSO-KELM algorithm is proposed. In the quadratic filtering, stable filtering can reduce the impact of noise and fast-kurtogram can filtrate fault frequency bands with rich fault information. Then, the time-domain, frequency-domain, and time-frequency parameters of the secondary filter signal are extracted. MSSST was used to analyze the filtered signal, and the time-frequency image was obtained. The time-frequency parameter was extracted from the time-frequency image by 2DPCA, and all the extracted parameters are taken as the fusion fault feature of the gearbox. Finally, the fault feature parameters are taken as the training sample and testing sample of QPSO-KELM for training and testing to achieve the purpose of fault diagnosis. The experimental results show that the proposed method can effectively filter the noise, complete the fault mode identification of gearbox, and improve the fault diagnosis accuracy better than other methods.

Objective. The aim of the study is to evaluate the effects of multilaminated small intestinal submucosa (mSIS) combined with bone substitute material to repair peri-implant defects during guided bone regeneration procedures. Methods. Twelve implants were placed in bilateral lower premolars of three beagle dogs, and a peri-implant buccal bone defect (3 mm width and 4 mm height) was created at each implant site. A total of 12 sites were filled with a particulate bone substitute material and then randomly divided into three treatment groups: covered by mSIS membrane (mSIS group), covered by collagen membrane (BG group), and no treatment (control group), each group of four sites. After 12 weeks of healing, all of the animals were euthanized and dissected blocks were obtained for micro-computed tomography (micro-CT) and histological analyses. Results. Micro-CT results revealed similar horizontal width of augmented tissue and new bone formation between mSIS and BG groups (P<0.05). Histological analyses revealed that the differences in horizontal widths of newly formed bone and bone-to-implant contact between mSIS and BG groups were not significant (P>0.05). All of these parameters were significantly different from those in the control group (P<0.05). Conclusions. These findings confirmed that mSIS combined with the bone substitute material enhanced bone regeneration in peri-implant defects, in a manner similar to that of a collagen membrane.

Objective Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text. Materials and Methods We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the sigmoid cross-entropy objective function. Results and Discussion We used two established datasets of semantic similarities among biomedical concept pairs to evaluate the quality of the generated word embeddings. On the first dataset with 29 concept pairs, with similarity scores established by physicians and medical coders, MORE’s similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model, and 12.4% higher than that of the best ontology-based similarity measure. On the second dataset with 449 concept pairs, MORE’s similarity scores have a correlation of 0.481, based on the average of four medical residents’ similarity ratings, and that outperforms the skip-gram model by 8.1%, and the best ontology measure by 6.9%. Furthermore, MORE outperforms three pre-trained transformer-based word embedding models (i.e., BERT, ClinicalBERT, and BioBERT) on both datasets. Conclusion MORE incorporates knowledge from several biomedical ontologies into an existing corpus-based distributional semantics model, improving both the accuracy of the learned word embeddings and the extensibility of the model to a broader range of biomedical concepts. MORE allows for more accurate clustering of concepts across a wide range of applications, such as analyzing patient health records to identify subjects with similar pathologies, or integrating heterogeneous clinical data to improve interoperability between hospitals.

Modern mechanical equipment has powerful function and complex structure, which puts forward new requirements for equipment maintenance. Aiming at the new requirements of improving maintenance efficiency and reducing maintenance cost, a decision model of combined maintenance is proposed. First of all, determine the specific maintenance parts and their maintenance methods, then get the distribution of maintenance cost and service life according to the historical data, with reliability as the constraint, combined with the length of service replacement model to optimize the preventive maintenance of the action device of a certain type of mechanical equipment, which can effectively reduce the maintenance cost and improve the maintenance efficiency.

Command Forces with intelligent decision-making model is necessary to the simulation system with man-not-in the loop in order to evaluate of equipment maintenance support effectiveness, repair order need use it to generate. By analyzing the features of equipment maintenance in wartime, command forces of equipment maintenance support is researched by the method of case-based reasoning. The framework for command forces system of equipment maintenance support based on case-based reasoning is presented. The work process of the inference machine is described. The method of representation and storage of equipment maintenance case is analyzed, and the algorithm of retrieval and match case is analyzed too. The research is of benefit to improving the fidelity of the simulation system with man-not-in the loop in order to evaluate of equipment maintenance support effectiveness.

At present, the Military Geographic Information System(MGIS) is applied in many equipment maintenance support activities. With the extensive application in getting, storing, handling, analyzing the battlefield environmental information and making supplementary decision for commanders, MGIS makes it more informative, visible and intelligent in the maintenance support. However, a mass of system applications are facing with the different constraints when spanned between different software and hardware platforms. MGIS has met so large a bottleneck in Cross-Platform experience that many systematic functions can not be used and the performance declined severely. Therefore it is a hot spot in the research of the new Cross-Platform technology for MGIS. A Cross-Platform Military Geographic Information System is put forward, which is based on many components. With the technique, the complicated MGIS software is divided into different function components which can be random combined and mutually manipulated under a certain standard. MGIS is made more flexible and convenient integrate into the maintenance support system which has specific user pattern and platform in the less cost. Furthermore, the work structure of the Cross-Platform MGIS in maintenance support system application is detailed in the paper And a series of cross-platform component object models, graphical libraries and data access engine in the hierarchical and modular system organization are built. In the end, this technique is applied in the different software and hardware maintenance support system platform to ensure the whole process of maintenance support more reasonable and optimized. Eventually the goal of the timely and Appropriate-place maintenance support is realized.

With FLAC3D finite difference analysis software, the effects of the existing buried pipeline on the stratum settlement due to shield tunneling construction in the condition of that the pipeline is parallel to the tunnel are analyzed in two aspects of the transverse and longitudinal settlement feature. Results show that the existing buried pipeline has remarkable influences on the displacement field of the stratum around the tunnel. On the ground surface above the existing buried pipeline, the transverse and longitudinal settlement curves of the stratum are all obviously less than those without considering the buried pipelines. The stratum displacement field is more severely affected when the stratum is closer to the existing pipeline. Furthermore, the existence of the buried pipeline evidently obstructs the propagation of the stratum transverse displacement wave and shields the development of stratum subsidence above the pipe.