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Context.— Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. Objective.— To detect aggressive adenocarcinoma and less aggressive pancreatic tumors from nonneoplasm cases using a graph convolutional network–based deep learning model. Design.— Our model uses a convolutional neural network to extract detailed information from every small region in a whole slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole slide–level structure and make the final prediction. Results.— We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. Conclusions.— If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.

The research on the invulnerability of software-intensive equipment can improve the continuous combat ability of equipment as a necessary means to deepen the understanding of equipment damage law and improve the equipment anti-destruction ability and recovery ability. The general process and key technologies of software-intensive equipment invulnerability research are summarized from component level, equipment level and equipment network level, and the existing problems are analyzed; the future development direction of equipment invulnerability research is prospected, which provides some reference to the research work of software-intensive equipment invulnerability.

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.

IntroductionAcross the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes.MethodsThis paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes.DiscussionResults may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response.Clinical Trial RegistrationIdentifier: NCT04535583.

Missile is an important weapon system of the army. The spare parts of missile equipment are significant effect on military operations. In order to improve the mission completion rate of missile equipment in wartime, thispaper introduces data sensing method to forecast the demand of valuablespare parts of missile equipment dynamically. Firstly, the information related to valuable spare parts of missile equipment was obtained by data sensing, and the sample size was determined by Bernoulli uniform sampling probability. Secondly, according to the data quality of multisource and multi-modal, the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained. Finally, according to the characteristics of the spare parts, the life of the spare parts was predicted, realizing the dynamic prediction of the demand for valuable spare parts of missile equipment. The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method, the accuracy is higher than 95%, and the real-time performance is more excellent.

Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide about the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. This study proposes a graph convolutional network-based deep learning model to detect aggressive adenocarcinoma and less aggressive pancreatic tumors from benign cases. Our model uses a convolutional neural network to extract detailed information from every small region in a whole-slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole-slide level structure and make the final prediction. We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.

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.

To investigate the clinical pharmacokinetics of CA4P, a high-throughput high-performance liquid chromatography-tandem mass spectrometry assay with an identical positive electrospray ionization (ESI) mode was developed for the simultaneous determination of CA4P, its active metabolite CA4, and CA4 glucuronide in human plasma. CA4P and CA4 were easier to protonate in positive ESI mode, whereas CA4G was reported to produce deprotonated ion in negative ESI mode. Because the baseline separation of CA4P and CA4G could not be achieved, using MS positive/negative ion switching is not feasible. In this study, an abundant ammonium adduct ion of CA4G in ESI+ was observed as an ideal precursor ion. The final precursor/product transition pairs chosen for CA4P, CA4, and CA4G were at m/z 397/350, 317/286, and 510/317, respectively. To the best of our knowledge, it is the first report on the simultaneous quantification of CA4P, CA4, and CA4G in biological samples. The proposed method was validated, which showed a wide linear dynamic range, high selectivity and sensitivity, good repeatability, and a short run time. Compared with the literatures, the lower limits of quantification were five- and two-fold more sensitive for CA4G and CA4, respectively. Therefore, this method was successfully applied to the pharmacokinetic study of CA4P in phase I clinical trial.

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.

In order to solve the problem that it is difficult to configure reasonably for the equipment maintenance material in each maintenance level, this paper proposes a method based on simulation modeling for the equipment maintenance material configuration considering with the complex relationship of various elements and the randomness of various maintenance activities. A basic simulation system framework is proposed to realize the more accurate demand for maintenance equipment which can be difficult for the traditional mathematical modeling and description. Finally the proposed model can lay the foundation for the optimal allocation of maintenance support resources in the future.

With its rich varieties and unique features, decorative glass has a great impact on the creation of indoor light environments. It may influence multiple aspects such as the color and amount of light, the relationship between light and shadow, and the dimension of the whole space. In this paper, decorative glass is studied in reference to its innovative applications in the world, thereby providing certain inspiration for staff in the design industry.

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.

Aiming at the characteristics of magnetron of airport surveillance radar, such as short operation time, complexity and nonlinear vibration signal, a fault reasoning method based on T-S fuzzy fault tree model was proposed to realize fault reasoning analysis of magnetron of radar receiving and receiving system. A T-S fuzzy fault tree fault inference model is constructed by using the field expert’s opinion expressed by fuzzy number language set. The experimental results show that compared with the traditional manual fault detection method, this method can make more comprehensive and efficient inference analysis for the magnetron fault events. This method provides a new idea for the application of magnetron fault reasoning analysis in engineering.

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.

In this study, a duplex-structured SnO2-Sb-CNT composite anode (Ti/SnO2-Sb/SnO2-Sb-CNT) was fabricated using pulse electrodeposition technique. SnO2-Sb-CNT composite composed of 4–5 nm SnO2-Sb particles was synthesized via co-precipitation prior to the electrode preparation and characterized with BET and TEM. Characterization of Ti/SnO2-Sb/SnO2-Sb-CNT by FESEM, EDS, XRD, XPS, LSV, CV and EIS revealed that the novel duplex structured electrode had larger electroactive surface area and lower charge transfer resistance than conventional Ti/SnO2-Sb prepared by thermochemical decomposition, due to its more compact and rough surface. It also improved the electrochemical stability of Ti/SnO2-Sb/SnO2-Sb-CNT with a service lifetime up to 86 h. The oxygen evolution potential (OEP) of Ti/SnO2-Sb/SnO2-Sb-CNT was determined to be 2.2 V (vs Ag/AgCl). Bulk electrolysis of bisphenol A (BPA) was conducted to evaluate the electrocatalytic performance of the electrodes at various current densities (10–40 mA cm−2) and initial pH (3–11) of solution by determination of BPA degradation, total organic carbon (TOC) removal and mineralization current efficiency (MCE). Ti/SnO2-Sb/SnO2-Sb-CNT intended to adsorb more BPA molecules, which may contribute to its high current efficiency for electrochemical oxidation of BPA. The presence of humic acid (HA) could decrease the performance efficiency by inhibiting the adsorption of BPA by Ti/SnO2-Sb/SnO2-Sb-CNT. A mechanism is proposed to illustrate the enhanced degradation of BPA by Ti/SnO2-Sb/SnO2-Sb-CNT by synergistic adsorption and electrochemical oxidation.

Using historical narratives and a qualitative research approach, this paper analyses the social, cultural and cognitive causes of differentiation and stratification among Chinese migrants in the Republic of Ireland. It discusses individuals’ diversified capabilities, attitudes and actual levels of integration, as well as their disparate patterns of self-identification. In the research area of Chinese migrants in Ireland, in-group diversity and its implications is still a novel research topic. With this introductory work, the authors seek to draw more attention to this particular group, especially to the need for studies of encounters between subgroups and longitudinal investigations. The paper points out that subgroups of Chinese migrants in Ireland are divided according to social classification and self-categorisation, which have distinct significances for subgroup members’ integration and identity.

There is great demand for an improved barrier membrane with osteogenic potential for guided bone regeneration (GBR). Natural acellular porcine pericardium (APP) is increasingly used in regenerative medicine as a kind of common extracellular matrix materials. This study aimed to investigate its potential application in GBR, especially its osteoconductive and osteoinductive properties. Bio-Gide (BG), a commercial collagen membrane, was set as the control group. APP samples were characterized by physicochemical analyses and their biological effects on human bone mesenchymal stem cells (hBMSCs) and human gingival fibroblasts (hGFs) were also examined. Additionally, the osteogenic potential of APP was tested on a bilateral critical-sized calvarial defect model. We discovered that the smooth surface of APP tended to recruit more hBMSCs. Moreover, promoted proliferation and osteogenic differentiation of hBMSCs was detected on this side of APP, with increased alkaline phosphatase activity and upregulated expression of bone-specific genes. Besides, the rough side of APP showed good biocompatibility and barrier function with hGFs. Histologic observation and analysis of calvarial defect healing over 4 weeks revealed enhanced bone regeneration under APP compared with BG and the control group. The results of this study indicate that APP is a potential osteoconductive and osteoinductive biomaterial for GBR.

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.

With the wide use of electronic devices, passwords are used increasingly frequently. How to effectively evaluate password security needs our further research. In this paper, the known leaked passwords obtained from the network are analyzed by cluster analysis, frequency analysis, and correlation analysis by using system clustering and correlation, the strength grade model of the password is established, and the relationship among the factors of the password is analyzed. It shows that most people’s personal information plays an important role in building passwords.

In order to solve the problem of multi view inconsistency in equipment support simulation, a semantic oriented MCM method based on ontology model is proposed. Firstly, the rules of extracting the basic elements of the model are given. Secondly, the algorithm description of ontology mapping is given. Finally, each meta concept in the domain is described by some model description method to form the corresponding description model.

In this study, electrochemical oxidation of bisphenol A (BPA) was investigated using BDD anode and two types of modified SnO2-Sb anodes, TiO2-NTs/SnO2-Sb-PTFE and Ti/SnO2-Sb/SnO2-Sb-CNT. The influences of solution pH (3, 7 and 11) and the type of supporting electrolyte (0.05 M Na2SO4 and 0.1 M NaCl) on the electrocatalytic activity of the three anodes were investigated. The anodes exhibited remarkably different behavior for BPA oxidation, due to their different surface morphology, oxygen evolution potential and ability in the hydroxyl radical generation. Both BDD and the modified SnO2-Sb anodes could degraded BPA effectively in 0.05 M Na2SO4 at pH 3 and 7. However, in 0.1 M NaCl, TiO2-NTs/SnO2-Sb-PTFE and Ti/SnO2-Sb/SnO2-Sb-CNT showed better electrocatalytic activity for BPA oxidation than BDD with less Cl loss and ClO3− generation. On the contrary, a considerable amount of chlorinated intermediates and polymer byproducts were observed with BDD, resulting in its ineffective TOC removal (46.8%). It indicates that the modified SnO2-Sb anodes are more environmentally benign for the treatment of wastewater containing chloride ion. LC–MS/MS and ion chromatography revealed the aromatic intermediates and aliphatic acids produced during BPA oxidation. Finally, the reaction pathways of BPA oxidation in Na2SO4 and NaCl supporting electrolytes are proposed.

Simulation training is based on virtual reality and computer simulation technology as the main technical support, so as to realize the combat method of training personnel in the virtual battlefield environment, weapon system operation, joint operations under the battlefield environment, including operational method of simulated training, simulation training as a military exercise method has important practical significance.

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.

In this work, to probe the crucial role of photo nanosensitizer clusterization played in determining the performance of the photoelectrocatalytic process, an illustrative nano-regional heterostructure was constructed via novel in-situ loading ultra-small cobalt oxide nanodots (

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.

The first co-working space (hereafter CWS for short) was founded in San Francisco in 2005. The following decade witnessed a fast development of CWSs around the world. Now “co-working” has become a buzzword that is widely used in different contexts and occasions, making it an ambiguous term with multiple meanings. As an innovative workplace concept and practice, co-working is still evolving and expanding. Therefore, it may turn out to be futile and even misleading to seek for a strict definition. A more reasonable and practicable option is to abandon preconceived “standards” and observe different CWSs from various angles, so as to fully understand why co-working emerges, what it offers and how it has transformed.

In this study, novel Sb-doped SnO2 electrodes with a polyetrafluoroethylene (PTFE) composite were fabricated by pulse electrodeposition. In this process, vertically aligned TiO2 nanotubes (TiO2-NTs) formed by anodization of Ti plates served as the substrate for SnO2 eletrodeposition. Comparing with the conventional SnO2-Sb electrodes, TiO2-NTs/SnO2-Sb-PTFE electrodes have higher oxygen evolution potential, improved surface hydrophobicity, superior hydroxyl radical (HO˙) generation and enhanced electrocatalytic activity by incorporation of PTFE nanoparticles. Field emission scanning electron microscopy (FESEM) shows that the surfaces of the PTFE composite electrodes exhibit a microspherical structure. Energy-dispersive X-ray spectroscopy (EDS) confirms the uniform distribution of Sn, Sb, F and C on TiO2-NTs/SnO2-Sb-PTFE surfaces. More importantly, the electrodes exhibit a distinctive improvement of oxygen evolution potential (OEP) from 2.0 to 2.4 V (vs. Ag/AgCl). The electrochemical impedance of TiO2-NTs/SnO2-Sb-PTFE also decreases significantly compared with Ti/SnO2-Sb(conventional). The electrocatalytic performance of TiO2-NTs/SnO2-Sb-PTFE compared with Ti/SnO2-Sb(conventional) and TiO2-NTs/SnO2-Sb were investigated using phenol as the model pollutant. The effects of initial solution pH and types of supporting electrolyte were investigated. The removal efficiency of total organic carbon (TOC), specific UV absorbance at 254 nm (SUVA254), mineralization current efficiency (MCE) and energy consumption (Ec) with respect to different PTFE loadings on the electrodes were investigated. The anodic leaching of Sn ions was also studied under different conditions.

Self-Organizing Feature Map (SOM) is a kind of self-organizing and self-learning network without teacher. It is mainly used for pattern recognition and region classification of input vectors. A fault diagnosis method of engine fuel supply system based on SOM neural network is proposed. The sensor is used to monitor the fuel pressure waveform of a certain engine fuel supply system, time domain analysis and feature extraction are carried out on the waveform, and the feature dimension reduction is realized by PCA to form the input vector of SOM neural network. The SOM neural network is used to establish the diagnosis model and then recognize fault patterns for test samples. The results of pattern recognition show that SOM neural network can identify and classify faults accurately, and it has certain engineering application value.

Mixed metal oxides (MMOs) have been extensively employed for heterogeneous catalysis. In recent years, MMOs have received an intensive interest as anode materials for electrochemical treatment of wastewaters which contain recalcitrant organics. This review article gives an overview of the classification of MMO anodes systems, synthesis methods, characterization techniques, and recent advance of removing or alleviating various recalcitrant organic pollutants using MMO anodes, highlighting areas of consensus and currently unresolved issues. Practical issues pertinent to modification to the catalytic activities of MMO anodes are discussed, including nano- and microstructured deposits, doping and polymer composites. Electrogenerated reactive oxygen species (ROS) with MMO anodes and their determination by various techniques have also been reviewed. In addition, this article discusses several important factors which could affect the electrochemical oxidation of recalcitrant organics with MMO anodes, and critically identifies the shortcomings in current research including discrepant results and ambiguous conclusions. Finally, the challenges and possible improvement of MMO anodes for their future application are also proposed.

Disc-like fossils from siltstones of the Taozichong Formation (Cambrian) in the Qingzhen area, Guizhou, South China are reported here. They are similar to some Ediacaran and Phanerozoic discoidal fossils, and assigned to Tirasiana? disciformis? Palij, 1976. Based on the study of 43 specimens, dewatering or fluid escape structures, soft-sediment loading, scratch circles or other inorganic origins are ruled out, and the fossil is interpreted as a discoidal body fossil of unknown affinities rather than trace fossils. Energy-dispersive X-ray spectroscopy and elemental mapping analyses reveal that the discoid fossils contain higher concentrations of C, Fe, and P than the surrounding matrix, indicating the possible presence of pyrite, apatite, and organic carbon as a result of authigenic mineralization in association with decay and early diagenetic processes. The possible presence of extracellular polymeric substance suggests that the discs were surrounded by thin microbial mats composed primarily of extracellular polymeric substances, which facilitated their fossilization by promoting conditions that are favorable to secondary mineral precipitation. The new specimens provide useful information about the phylogenetic affinities of these early discoidal fossils and help us to better understand the taphonomic modes of non-biomineralizing organisms in Ediacara-type and Burgess Shale-type biotas.

Pristine bismuth ferrite usually possesses weak magnetic properties (e.g., saturation magnetization Ms3 emu g(-1)) for practical magnetic separation applications. Herein, a superparamagnetic bismuth ferrite with coral-like hierarchical morphology (BFO-M) was fabricated through methanol solvothermal treatment of the as-prepared Bi2Fe4O9 nanoclusters (P-BFO). The BFO-M shows a higher Ms of ∼31 emu g(-1) compared to that of P-BFO treated in water (BFO-A), in ethanol (BFO-E) and in ethylene glycol (BFO-G). Compared to single-crystalline Bi2Fe4O9 (PS) and Bi2Fe4O9 clusters (NSP), BFO-M shows an excellent organic pollutant removal rate by virtue of its high adsorption capacity and catalytic activity when methyl orange (MO) is used as the model organic pollutant. BFO-M also exhibits good visible light photo-Fenton oxidation rates for pharmaceuticals and pesticides. Even at a low catalyst loading of 0.12 g L(-1), the removal rate of organic pollutants (e.g., 5-fluorouracil, isoproturon) can be ∼99% in 100 min under visible light irradiation. Besides, BFO-M is also a good adsorbent for different kinds of heavy metal ions (Pb(ii), Cr(iii), Cu(ii), As(v), etc.). For example, its maximal adsorption capacity for Pb(ii) is 214.5 mg g(-1). The used BFO-M can be recovered via magnetic separation. The outstanding performances of BFO-M can be ascribed to its coral-like hierarchical morphology which consists of the self-assembly of 1D nanowires (∼6 nm in diameter) and 2D ultrathin nanoflakes (∼4.5 nm in thickness). A schematic illustration of its morphology formation is proposed.

We describe a highly scalable parallel arithmetic coder for Markov inputs suitable for implementation on modern multi-core processors. The algorithm divides the input into interleaved sub-sequences which can be then processed independently on different processing units using LDPC-based Slepian-Wolf coding. Experimental simulations show good scalability of the proposed algorithm while also maintaining good compression performance. Notably, when compared with traditional parallel arithmetic coding, the proposed method maintains a much higher efficiency both respect to the entropy limit as well as in terms of the ability to distribute computations across multiple cores without performance loss.

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.

Multi-core architectures have become mainstream. Trends suggest that the number of cores integrated on a single chip will increase continuously. However, lock contention in operating systems can limit the parallel scalability on multi-cores so significantly that the speedup decreases with the increasing number of cores (thrashing). Although the phenomenon can be easily reproduced experimentally, most existing lock models are not able to do so. To overcome this challenge, this paper develops a discrete event simulation model which has the capability of capturing both the sequential execution in critical sections and the contention for shared hardware resources. The model is evaluated using a series of typical parameter configurations which can represent different degrees of lock contention. Experimental results suggest that the thrashing phenomenon can be observed when the model parameters are selected properly. To further understand this phenomenon, statistics such as the percentage of time spent waiting for locks and the number of cores waiting for a lock are exploited to characterize the lock thrashing. In addition, the model sensitivity to changes in memory latency and hardware architectures are also examined. Finally, we use this model to compare three methods which are proposed for preventing the lock thrashing.

Multi-core architectures have become mainstream. Trends suggest that the number of cores integrated on a single chip will continue to increase. However, lock contention in applications or kernels can degrade the scalability so significantly that the speedup decreases with the increasing number of cores (thrashing). Although the phenomenon can be easily reproduced on real multi-core platforms, existing lock models are not able to do so. To overcome the disadvantage, this paper proposes an analysis model which has the capability of capturing both the sequential execution of critical sections and the overhead of lock implementation. Numerical results indicate that thrashing can be observed by using the proposed model. Furthermore, this model can also be exploited to compare different mechanisms designed for avoiding the lock thrashing.