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A highly selective fluorescent "turn on" probe for Al3+ based on the conjugate of aminourea and pyrazolone was devised and synthesized. The fluorescence intensity of the probe solution was significantly enhanced by the addition of Al3+ to the probe solution. Other cations such as Cu2+, Ni2+, Fe3+, Co2+, Ag+, Mn2+, Sn2+, Sn4+, Mg2+, Li+, Zn2+, Zr4+, Ce3+, Ca2+, and Na+ did not interfere the detection of Al3+. The 1 : 1 complexation between Al3+ and the probe was confirmed by Job's plot curve. In addition, the solution of the complex formed by Al3+ and the probe can be used for the specific detection of Hg2+, appearing as fluorescence quenching. [ABSTRACT FROM AUTHOR]

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow [ABSTRACT FROM AUTHOR]

Background: Individuals with low socioeconomic status (SES) are at a higher risk of developing depression. However, evidence on the role of cardiovascular health (CVH) in this chain is sparse and limited. The purpose of this research was to assess the mediating role of Life's Essential 8 (LE8), a recently updated measurement of CVH, in the association between SES and depression according to a nationally representative sample of adults. Methods: Data was drawn from the National Health and Nutrition Examination Survey (NHANES) in 2013–2018. Multivariate logistic regression analysis was applied to analyze the association of SES (measured via the ratio of family income to poverty (FIPR), occupation, educational level, and health insurance) and LE8 with clinically relevant depression (CRD) (evaluated using the Patient Health Questionnaire (PHQ-9)). Multiple linear regression analysis was performed to analyze the correlation between SES and LE8. Mediation analysis was carried out to explore the mediating effect of LE8 on the association between SES and CRD. Moreover, these associations were still analyzed by sex, age, and race. Results: A total of 4745 participants with complete PHQ-9 surveys and values to calculated LE8 and SES were included. In the fully adjusted model, individuals with high SES had a significantly higher risk of CRD (odds ratio = 0.21; 95% confidence interval: 0.136 to 0.325, P < 0.01) compared with those with low SES. Moreover, LE8 was estimated to mediate 22.13% of the total association between SES and CRD, and the mediating effect of LE8 varied in different sex and age groups. However, the mediating effect of LE8 in this chain was significant in different sex, age, and racial subgroups except for Mexican American (MA) individuals. Conclusion: The results of our study suggest that LE8 could mediate the association between SES and CRD. Additionally, the mediating effect of LE8 in this chain could be influenced by the race of participants. [ABSTRACT FROM AUTHOR]

In the cycloaddition reaction of carbon dioxide (CO2) with epoxides, halogen catalysts lead to halogen loss and environmental pollution. It is necessary to design efficient and environmentally friendly catalysts. A series of halogen‐free phosphonium ionic liquids (ILs) regulated by steric hindrance were designed and synthesized. It is further used as effective catalysts for the catalytic conversion of CO2 into cyclic carbonates. The effect of different catalyst structures on catalytic activity has been investigated. Amino group has been shown to contribute to increased activity, and its location determines the degree of increased activity. Among them, [Bu4P]2[5‐AMP] with meta‐substitution showed the best catalytic activity, the conversion of propylene oxide (PO) was 96 % at the optimum conditions of 120 °C, 2 MPa and 3 h. Based on characterization analysis and DFT calculations, the interaction of ILs with CO2 and PO were revealed. Finally, a plausible synergistic catalytic mechanism was proposed. The halogen‐free, metal‐free phosphonium ILs proposed in this paper provide a new insight on catalyst design and CO2 utilization. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper introduces a novel graph neural network (a hierarchical graph pooling model), SANEpool, to effectively detect core sub-networks of significant genes for predicting the synergy score of drug/drug combinations in cancer. SANEpool successfully addresses the limitations of the un-transparency in the prediction process of previous computational AI models for drug synergy prediction, while providing the superior predictive performance than popular baselines on numerous drug-synergy prediction datasets. The success of SANEpool indicates that significant gene-gene interactions and gene-drug interactions play a crucial role in designing powerful deep learning models to provide accurate prediction and to reveal the mechanism of the synergy (MoS). Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human–AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations. [ABSTRACT FROM AUTHOR]

Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best. [ABSTRACT FROM AUTHOR]

(1) Background: Sleep deprivation (SD) triggers a range of neuroinflammatory responses. Dexmedetomidine can improve sleep deprivation-induced anxiety by reducing neuroinflammatory response but the mechanism is unclear; (2) Methods: The sleep deprivation model was established by using an interference rod device. An open field test and an elevated plus maze test were used to detect the emotional behavior of mice. Mouse cortical tissues were subjected to RNA sequence (RNA-seq) analysis. Western blotting and immunofluorescence were used to detect the expression of p38/p-p38, MSK1/p-MSK1, and NFκBp65/p- NFκBp65. Inflammatory cytokines were detected using enzyme-linked immunosorbent assay (ELISA); (3) Results: SD triggered anxiety-like behaviors in mice and was closely associated with inflammatory responses and the MAPK pathway (as demonstrated by transcriptome analysis). SD led to increased expression levels of p-p38, p-MSK1, and p-NFκB. P38 inhibitor SB203580 was used to confirm the important role of the p38/MSK1/NFκB pathway in SD-induced neuroinflammation. Dexmedetomidine (Dex) effectively improves emotional behavior in sleep-deprived mice by attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex, mainly by inhibiting the activation of the p38/MSK1/NFκB pathway; (4) Conclusions: Dex inhibits the activation of the p38/MSK1/NFκB pathway, thus attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex of mice. [ABSTRACT FROM AUTHOR]

Background: Empagliflozin, a sodium–glucose co-transporter 2 inhibitor (SGLT2i), has been reported to significantly reduce the risk of heart failure in multiple clinical studies. However, the underlying mechanisms remain elusive. This study aimed to investigate the effect of empagliflozin on branched-chain amino acid (BCAA) metabolism in diabetic cardiomyopathy. Methods: Thirty male 8-week KK Cg-Ay/J mice were used to study diabetic cardiomyopathy; here, 15 were used as the model group, and the remaining 15 were administered empagliflozin (3.75 mg/kg/day) by gavage daily for 16 weeks. The control group consisted of fifteen male 8-week C57BL/6J mice, whose blood glucose and body weight were measured simultaneously with the diabetic mice until 16 weeks without additional intervention. Echocardiography and histopathology were performed to evaluate cardiac structure and function. Proteomic sequencing and biogenic analysis were performed on mouse hearts. Parallel Reaction Monitoring and western blotting were performed to validate the expression levels of differentially expressed proteins. Results: The results showed that empagliflozin improved ventricular dilatation and ejection fraction reduction in diabetic hearts, as well as the elevation of myocardial injury biomarkers hs-cTnT and NT-proBNP. At the same time, empagliflozin alleviates myocardial inflammatory infiltration, calcification foci deposition, and fibrosis caused by diabetes. The results of the proteomics assay showed that empagliflozin could improve the metabolism of various substances, especially promoting the BCAA metabolism of diabetic hearts by up-regulating PP2Cm. Furthermore, empagliflozin could affect the mTOR/p-ULK1 signaling pathway by reducing the concentration of BCAA in diabetic hearts. When mTOR/p-ULK1 protein was inhibited, ULK1, the autophagy initiation molecule, increased. Moreover, autophagy substrate p62 and autophagy marker LC3B were significantly reduced, indicating that the autophagy activity of diabetes inhibition was reactivated. Conclusions: Empagliflozin may attenuate diabetic cardiomyopathy-related myocardial injury by promoting the catabolism of BCAA and inhibiting mTOR/p-ULK1 to enhance autophagy. These findings suggest that empagliflozin could be a potential candidate drug against BCAA increase and could be used for other cardiovascular diseases with a metabolic disorder of BCAA. [ABSTRACT FROM AUTHOR]

Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction. [ABSTRACT FROM AUTHOR]

Multi-resolution modelling (MRM) has been increasingly applied in engineering practice. While in a distributed simulation environments, time advance method for MRM is less studied. When the system is running in a relatively low-resolution status, it usually adopts basic time advance modes to save simulation resources. However, these basic modes usually suffer from inaccurate response of interactions and are separate from the resolution switching process. In this paper, we propose an improved time advance method for distributed multi-resolution models. We introduce an interaction table to enable dynamic management of interactions, and decouple the interaction request process from the interaction response process, resulting in a more fine-grained interactive mode. Moreover, the interactive deviation index is introduced to quantitatively characterize the cumulative errors from delayed interactions. This index has been incorporated into resolution switching conditions to provide a more flexible and intuitive way for multi-resolution modelling. Proposed method has been verified on both theoretical cases and practical applications, and can achieve high simulation accuracy with less time consumption. • A novel time advance method for distributed Multi-resolution Modelling is proposed. • It can achieve higher precision with a few simulation resources. • It introduces E c u m to describe cumulative errors from delayed interactions. • It is further incoporated into simulation resolution switching conditions. • It fills the gap in time advance methods combined with multi-resolution features. [ABSTRACT FROM AUTHOR]

Background: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. Results: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients' survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients' survival time. Conclusion: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients' survival by integrating multi-omics data and clinical factors. [ABSTRACT FROM AUTHOR]

The brain frontoparietal regions and the functional communications between them are critical in supporting working memory and other executive functions. The functional connectivity between frontoparietal regions are modulated by working memory loads, and are shown to be modulated by a third brain region in resting‐state. However, it is largely unknown whether the third‐region modulations remain the same during working memory tasks or were largely modulated by task demands. In the current study, we collected functional MRI (fMRI) data when the subjects were performing n‐back tasks and in resting‐state. We first used a block‐designed localizer to define the frontoparietal regions that showed higher activations in the 2‐back than the 1‐back condition. Next, we performed physiophysiological interaction (PPI) analysis using left and right middle frontal gyrus (MFG) and superior parietal lobule (SPL) regions, respectively, in three continuous‐designed runs of resting‐state, 1‐back, and 2‐back conditions. No regions showed consistent modulatory interactions with the seed pairs in the three conditions. Instead, the anterior cingulate cortex (ACC) showed different modulatory interactions with the right MFG and SPL among the three conditions. While the increased activity of the ACC was associated with decreased functional coupling between the right MFG and SPL in resting‐state, it was associated with increased functional coupling in the 2‐back condition. The observed task modulations support the functional significance of the modulations of the ACC on frontoparietal connectivity. [ABSTRACT FROM AUTHOR]

This paper investigates the correlation between a pilot's brain electroencephalographic (EEG) activity and his driving posture, addressing the intricate relationship between cognition and behavior in aviation. We designed and implemented a simulated experiment that recorded the fly pilot's attitude and collected EEG information as experimental data. The experiment is only based on EEG data to predict flight posture (pull, down, left, right). We propose a flight-driving attitude prediction model (CA-FAP) based on CEBRA and self-attention mechanism, with a prediction accuracy of 0.83. This model is better than common spatial pattern (CSP) and uniform manifold approximation and projection (UMAP) dimension reduction methods in the experiment. Moreover, a better effect can be obtained in the larger attitude radian dataset (accuracy is 0.85), and the effect is not obvious in the dataset of closing the six-axis motion platform, indicating that the model prediction of flight attitude is closely related to the pilot position transformation. By comparing the prediction effect of each category, the pull-up and drop are better than the steering prediction result. The study can help pilots adjust their posture and decisions, and serve as a basis for studying flight pilot cognitive load, mental load, mood changes, and flight performance. [ABSTRACT FROM AUTHOR]

Abstract Photodegradation is one of the major degradation paths for antibiotics as aquatic micropollutants in surface water. The photodegradation involves a number of complicated photophysical and photochemical processes. Exploration for the rate-limiting step among these processes can be essential for the elimination of antibiotics. In this work norfloxacin was selected as a target compound. The rate constants of photophysical transitions and their competitions were discussed under the framework of Fermi Golden rule and time-depended perturbation theory. Using density functional theory, the reaction paths in triplet state were searched. The competitions among the photophysical transitions and photochemical reaction paths indicate the intersystem crossing (ISC) from the S 1 state to T 1 state is the rate-limiting step in the aquatic photodegradation of norfloxacin. Ca2+ ion significantly accelerates this bottleneck by coordinating with the carbonyl and carboxyl groups of norfloxacin. The coordination creates more ISC paths to triplet states and increases the spin-orbit coupling, Huang-Rhys factors, and the vibrational coupling of the ISCs. Graphical abstract Image 1 Highlights • Comprehensively studied photophysical and photochemical processes of Norfloxacin. • Identified intersystem crossing as the rate-limiting step of photodegradation. • Enhanced photophysical characteristics by Ca2+ ion. • Theoretical exploration under the framework of Fermi Golden rule. [ABSTRACT FROM AUTHOR]

For organic pollutants, photodegradation, as a major abiotic elimination process and of great importance to the environmental fate and risk, involves rather complicated physical and chemical processes of excited molecules. Herein, we systematically studied the photophysical and photochemical processes of a widely used antibiotic, namely sulfapyridine. By means of density functional theory (DFT) computations, we examined the rate constants and the competition of both photophysical and photochemical processes, elucidated the photochemical reaction mechanism, calculated reaction quantum yield ( Φ ) based on both photophysical and photochemical processes, and subsequently estimated the photodegradation rate constant. We further conducted photolysis experiments to measure the photodegradation rate constant of sulfapyridine. Our computations showed that sulfapyridine at the lowest excited singlet state (S 1 ) mainly undergoes internal conversion to its ground state, and is difficult to transfer to the lowest excited triplet states (T 1 ) via intersystem crossing (ISC) and emit fluorescence. In T 1 state, compared with phosphorescence emission and ISC, chemical reaction is much easier to initiate. Encouragingly, the theoretically predicted photodegradation rate constant is close to the experimentally observed value, indicating that quantum chemistry computation is powerful enough to study photodegradation involving ultra-fast photophysical and photochemical processes. [ABSTRACT FROM AUTHOR]

• ABbILs catalyst reduces the reaction barriers by activating all reaction substrates through acid-base synergistic effect. • The addition of epoxide breaks the equilibrium limit, which facilitates the cyclic esterification reaction. • Combined with characterization and DFT calculation, the reaction mechanism of acid-base multi-site synergistic catalysis was proposed. In view of the harsh conditions of cyclic esterification reaction of ethylene glycol (EG) and carbon dioxide (CO 2) to synthesize ethylene carbonate (EC), the yield is low and the catalysts are mostly metals, a series of green acid-base bicentric ionic liquids (ABbILs) were developed. Among them, the ABbILs of 1,5,7-Triazabicyclo[4.4.0]dec‑5-ene glutarimide ([TBDH][Glu]) had the best catalytic performance. Under the optimal reaction conditions, the yield of EC reached 29.5%, which was higher than most of the conventional metal-based catalysts. Through activity screening results and DFT calculation, it was found that different cations affect the interaction between N on anion and H proton of EG, and the H on the cation can activate the O on EG, which would affect the ability of ABbILs to activate EG. The acid-base bicentered synergistic catalytic mechanism of Bronsted acid and Lewis base was proposed by characterization and DFT calculation. This study opens up new avenues for the efficient conversion of diols and CO 2 for the preparation of cyclic carbonates. A series of green acid-base bicentric ionic liquids (ABbILs) were developed to catalyze the synthesis of cyclic carbonates from ethylene glycol (EG), CO 2 and styrene oxide (SO). The addition of SO has a promoting effect on the reaction. On the one hand, SO avoids the hydrolysis of ethylene carbonate (EC) by removing H 2 O through reacting with H 2 O, which improves the yield of EC; on the other hand, SO reacts with CO 2 to generate styrene carbonate (SC), which further generates EC by transesterification with EG, which promotes the yield of EC. [Display omitted] [ABSTRACT FROM AUTHOR]

A novel protocol mediated by DBU for the construction of sp2 C–N bond between 3-alkenyl oxindoles and indazole/benzotriazole has been successfully accomplished. But the cascade reaction of 3-alkenyl oxindoles with benzimidazole leads to the formation of sp2 C-C coupling products under similar conditions. Starting from 3-alkenyl oxindoles, various stereodefined aromatic substituted 3-alkenyl oxindoles can be obtained under simple conditions without contamination by transition-metal residues. [Display omitted] [ABSTRACT FROM AUTHOR]

While smart city initiatives have characterized global urbanization from the 1990s to the 2020s, nowadays a novel artificial intelligence (AI) enabled approach to urban governance is rapidly emerging, thereby shaping the governance and planning of present and future cities. This urban phenomenon can be understood theoretically through the notion of anticipatory governance, and empirically through so-called City Brain systems. This is particularly evident in China where a wide range of urban AI solutions are being experimented at different scales which this paper seeks to illustrate. First, by building a database of AI-urban policy texts associated with Chinese cities, we capture and discuss the national network of discourses surrounding urban AI. Second, we draw on empirical research conducted in Beijing to examine an existing city brain project and explain its impact on urban governance. Our study reveals the multi-scalar policy landscape of urban AI transitions in China and sheds light on the extent to which emerging AI technologies such as city brains can proactively address urban problems, thus developing an understanding of anticipatory governance in the age of urban AI. We conclude the paper by reflecting on the complex corporate-state relations embedded in the co-production of city brains, their diffusion and impact beyond China. [ABSTRACT FROM AUTHOR]

Sulfonamides have been found in aquatic environments. Degradation of sulfachloropyridazine (SCP) mainly proceeds through direct and indirect photolysis in the aquatic environment. However, the mechanisms underlying the triplet photolysis of SCP and the influence of metal ions on the photolysis mechanism have not yet been fully explained. In this study, we elucidated the triplet photolysis mechanisms of SCP and the effects of three selected metal ions (Zn 2+ , Ca 2+ , and Cu 2+ ) on the SCP photolysis mechanisms using quantum chemical calculation. Optimization of molecular structures and reaction pathways analysis of SCP were carried out at the B3LYP/6-31+G(d,p) level of theory. Two minimum energy pathways were investigated in the triplet photolysis of SCP. In Step 2 of Path-I, the photolysis product of SCP is a sulfur dioxide extrusion product, (4-(3-chloro-6-iminopyridazine-1(6H)-yl)aniline). The estimated activation energies of Step 2 and Step 3 of Path-I were much higher than in Path-II. Therefore, Path-II was found as the lowest energy pathway to obtain the SCP photoproducts, and Step 2 of Path-II was confirmed as the rate-determining step (RDS) in the photolysis mechanism of SCP. For the RDS of Path-II, computations with the three metal ions complexes (IM1–Cu 2+ , IM1–Ca 2+ , and IM1–Zn 2+ ) show that the metal ions Cu 2+ and Ca 2+ promote triplet-sensitized photolysis of SCP by reducing the activation energy of RDS of Path-II, whereas Zn 2+ showed an inhibitory effect in photolysis of SCP by increasing the activation energy. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays simulation is commonly used in engineering design for verifying design concepts before physical prototypes are produced. The simulation of complex products such as mechatronics in general involves a synergy of multiple traditional disciplinary areas and entails the collaborative work of a multidisciplinary team. A need thus arises for supporting the effective and efficient integration of subsystem models at simulation runtime and in a distributed environment. These models are generally created using different simulation tools and depend on the inputs from each other to perform numerical integration. As such, many issues need to be addressed, e.g. system modeling, the use of computing technologies, and the runtime interaction between models. In this paper, a service-oriented paradigm is presented which is underpinned by collaborative computing technologies to enable the provision of simulation models as services as well as the integration of these services for performing simulation tasks in product design. As well as the implementation of such a paradigm, a method for the interaction between models is in particular developed to achieve high accuracy for the simulation of design problems involving the solving of system equations. Preliminary evaluation work shows that the proposed paradigm underpinned by collaborative computing technologies is viable and have great potential in supporting collaborative simulation development in industry and the method for interaction control successfully achieves better accuracy compared with traditional methods. [Copyright &y& Elsevier]

Abstract: The design and development of complex artifacts and systems is shifting towards a distributed and collaborative paradigm. The simulation environments for such a paradigm, therefore, need to take into account the cooperation between design teams, i.e. supporting multidisciplinary simulation in a distributed environment. However, current simulation tools can not fulfil this requirement as they have been developed to solve the specific problems from different disciplines. Although it’s already possible to perform multidisciplinary simulations by using several tools together, it is very difficult to implement it when these tools are distributed on the Internet. A solution which can support the integration of distributed simulation models at run-time is presented, involving a computational infrastructure and a high-level modelling approach. Specifically, the infrastructure is constructed by a novel combination of two distributed computing techniques to implement the synchronization of distributed models, as well as to ensure the interoperability at run-time. In addition, a model-driven approach is developed to bridge the high-level model of a simulation system and the infrastructure which implements this model. The solution is evaluated by making a comparison with other approaches, as well as by developing a prototype tool. It’s shown in the evaluation that (1) it is viable to develop multidisciplinary simulations in a distributed environment using this solution; (2) the model-driven approach allows designers to focus only on the high-level structure of a design without getting concerned with the details of the infrastructure. [Copyright &y& Elsevier]

In this paper, the concurrent design approach and collaborative environment for complex product development are firstly analysed from the viewpoint of authors. The process reengineering and optimisation for complex product development is discussed. Furthermore, the architecture and technologies of distributed multidisciplinary collaboration environments for complex product development are studied. An industrial application case undertaken by the authors during the research period is also given. [ABSTRACT FROM AUTHOR]

Cerebrovascular reactivity (CVR) is of great significance for the treatment and prevention of cerebrovascular diseases. CVR can be mapped using the blood oxygenation level-dependent (BOLD) signal of fMRI. Breath holding (BH) is a reliable method to produce the desired increase in arterial CO2, while its application in clinical research is limited due to subject's compliance and variability. BH task with variable respiratory rates could allow more flexibility in clinical populations. In this study, 50 healthy volunteers were scanned for end-inspiration BH tasks with three different respiration rates. For the three respiratory rates BH tasks, the CVR was estimated based on the BOLD signal and general linear model (GLM) separately. Specifically, the extra time delay was considered for the hemodynamic response function, and the optimal delay was estimated for each voxel. To measure CVR in grey matter, BOLD signals of end-inspiration BH were used as regressors in general linear models to quantify their impact on CVR. This was performed for regions and voxels. Systematic differences were observed between the three end-inspiratory breathing rates. The greatest increase in activation intensity was found in fast breathing followed by self-paced and slow breathing. We conclude that the BH task of variable respiratory rates allows for CVR measurement, making breath-holding challenges more flexible and appropriate for routine practice. [ABSTRACT FROM AUTHOR]

A novel exopolysaccharide, designated hs EPS, was successfully prepared from the high-salt-fermented broth of a novel species Halomonas saliphila LCB169T by ethanol precipitation, anion-exchange and gel-filtration chromatography, and its structure was well-characterized by means of chemical and spectral analyses. Results showed that hs EPS was primarily composed of mannose and glucose with a relative weight-average molecular weight of 5.133 × 104 g/mol. It was deduced that the major backbone contained (1→2)-linked α-D-Man p and (1→6)-linked α-D-Man p with branches substituted at C-2 by T-α-D-Man p and at C-6 by the fragment of T-α-D-Man p -(1→2)-α-D-Man p -(1→. A sheet-like structure was observed under high magnification. The water solubility index, water holding capacity, oil holding capacity and foaming capacity of hs EPS were 98.0, 19.3, 1386.7 and 82.2%, respectively. It also exhibited outstanding emulsifying activity against all tested edible oils. Together, the resulted data indicated that hs EPS might serve as an active ingredient in food, cosmetics and detergents. • An exopolysaccharide designated as hs EPS was isolated from a novel bacterial species in a high-salt broth. • The structure of hs EPS was comprehensively identified by means of chemical and spectral analyses. • hs EPS showed excellent functional properties, especially on oil-holding capacity as well as emulsifying activity. • This is the first report of exopolysaccharide production by the halophilic bacterium Halomonas saliphila. [ABSTRACT FROM AUTHOR]

As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF). The algorithm not only retains the advantages of Square-root Cubature Kalman Filter (SCKF) but also has strong robustness to non-Gaussian noise. The experimental results of tightly integrated vehicular cooperative navigation show that compared with the Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF), the localization accuracy of MCSCKF is improved by 35.08% and 31.83%, respectively, which verified the effectiveness in improving the accuracy and robustness of the relative position estimation. [ABSTRACT FROM AUTHOR]

Abstract Triphenylamine-based cyclometalated platinum(II) (TCP) emits phosphorescence at room temperature. The phosphorescence was quenched by oxygen molecule, which means that TCP is a good candidate for oxygen molecule recognition and measurement. By the way of quantum chemical calculation, we proposed the quenching mechanism to elucidate the nature of oxygen sensing, which was different from the traditional dynamic collision mechanism. The luminescence mechanism of TCP involves phosphorescent emission, and the TCP-O 2 complex fluorescent emission. By utilizing the density functional theory (DFT) method, we calculated the electron configurations and the frontier molecular orbitals of TCP and the TCP-O 2 complex. Furthermore, applying the time-dependent density functional theory (TDDFT) method, we calculated the geometric optimization and the excitation energies of TCP and the TCP-O 2 complex in the excited state. The results illustrate that the luminescence of TCP stems from localized excitation, while that of the TCP-O 2 complex stems from delocalized excitation. Moreover, we also calculated the radiative and non-radiative rate constants of TCP and the TCP-O 2 complex and clarified the photophysical processes of them. For TCP, first the electron arrived at the T 1 state via intersystem crossing from the initial S 1 state and finally back to the S 0 state via emitting phosphorescence. For the TCP-O 2 complex, with no spin flip, the electron jumped directly from the T 1 to T 0 state via internal conversion. Graphical abstract fx1 [ABSTRACT FROM AUTHOR]

In this paper, investigations of surface PiN diodes developed for a reconfigurable plasma antenna have been described. To increase carrier concentration within the surface PiN diodes as much as possible, parameters of the plasma region have been extensively discussed. According to these studies, it has been found that the average carrier concentration within the ‘i’ region has been achieved the level of 10 18  cm −3 at forward bias of 2 V. The carrier concentration becomes larger when the length and width of the ‘i’ region are reduced. Furthermore, a novel frequency reconfigurable antenna based on SPiN diodes is presented at Ku-band. The resonance frequencies at 13.71 GHz, 15.17 GHz, and 17.81 GHz have been easily achieved by turning on or off different sections of the antenna. The radiation efficiencies of the antenna are 79.70%, 80.70%, and 81.70%, respectively. Experimental results shown in this paper confirm the usefulness of the PiN diode’s application within a plasma antenna and other semiconductor fields. [ABSTRACT FROM AUTHOR]

Current research on design knowledge capture and reuse has predominantly focused on either the codification view of knowledge or the personalisation view of knowledge, resulting in a failure to address designers’ knowledge needs caused by a lack of context of information and insufficient computational support. Precisely motivated by this gap, this work aims to address the integration of these two views into a complete, contextual and trustworthy knowledge management scheme enabled by the emerging collaborative technologies. Specifically, a knowledge model is developed to represent an integrated knowledge space, which can combine geometric model, knowledge-based analysis codes and problem-solving strategies and processes. On this basis, a smart collaborative system is also designed and developed to streamline the design process as well as to facilitate knowledge capture, retrieval and reuse as users with different roles are working on various tasks within this process. An engineering case study is undertaken to demonstrate the idea of collaborative knowledge creation and sharing and evaluate the effectiveness of the knowledge representation model and the collaborative technologies employed. As evidenced in the development and evaluation, the methods proposed are effective for capturing an integrated knowledge space and the collaborative knowledge management system not only facilitates problem-solving using knowledge-based analysis but also supplies in-context tacit knowledge captured from the communications between users throughout the design process. [ABSTRACT FROM AUTHOR]

In this paper, the structure of fully-depleted Ge NMOS with P-substrate (FDP-NMOS) is proposed. The DIBL effect of fully depleted GeOI NMOS (FD-NMOS) and FDP-NMOS has been studied based on the simulation results. It is demonstrated that DIBL of FD-NMOS occurs at the deep bulk of channel before the surface. From the current-voltage (I D -V G ) characteristics, the DIBL effect, the leakage current and the on-state current are determined. The results indicate that with the doping concentration of P-substrate (N B ) increasing, the DIBL effect is lower and the leakage current decreases significantly. Therefore, the FDP-NMOS has excellent subthreshold characteristics. When N B is 10 × 10 17 /cm 3 , the subthreshold swing is 76.8 mV/dec and the on/off ratio is 1.65 × 10 8 , respectively. The on/off ratio of FDP-NMOS increases more than 4 orders compared with the FD-NMOS and the experimental results. [ABSTRACT FROM AUTHOR]

In this study, the catalytic hydrothermal liquefaction technology was adopted to convert the Chinese herb residue (CHR) into liquid bio-oil. The effect of the reaction conditions was investigated including temperature, water/biomass mass ratio (W/B) and time. Different combinations of Fe, Ni and ZSM-5 molecular sieve catalyst were evaluated to improve the bio-oil quality. The optimized liquefaction parameters were determined through experiments, which were the reaction temperature of 320 °C, W/B of 15:1, and reaction time of 10 min; the yield and high heat value (HHV) of bio-oil were 24.7 wt% and 29.39 MJ/kg, respectively. There was a synergy between Fe and ZSM-5, and Fe/ZSM-5 can achieve in situ hydrodeoxygenation in the liquefaction process, increasing the HHV of bio-oil to 31.74 M J/kg at 10 wt% loading with the yield of 21.3 wt%. The enhancement of bio-oil quality by Ni/ZSM-5 was weak due to the cleavage of C–C bond by Ni, resulting in the poor HHV and a low yield; the catalytic performance of Fe–Ni/ZSM-5 was between the former two catalysts. Elemental analysis and GC-MS showed that Fe/ZSM-5 increased H/C and decreased O/C in bio-oil, and the content of aliphatic compounds in bio-oil increased to 72.96%. • Chinese herb residue can be efficiently utilized by hydrothermal liquefaction. • Reaction temperature and W/B have a significant effect. • The operating conditions were given for highest yields of bio-oil. • Fe/ZSM-5 exhibits synergistic effect of hydrodeoxygenation. • 10 wt% Fe/ZSM-5 increases the HHV of bio-oil to 31.74 MJ/kg. [ABSTRACT FROM AUTHOR]

Real-time aircraft dynamics simulation requires very high accuracy and stability in the numerical integration process. Nonetheless, traditional multistep numerical methods cannot effectively meet the new requirements. Therefore, a novel real-time multistep method based on Predict-Evaluate-Correct scheme of three-step fourth-order method (RTPEC-34) is proposed and developed in this research to address the gap. In addition to the development of a highly accurate algorithm based on predictor-corrector, the contribution of this work also includes the analysis of truncation error for real-time problems. Moreover, the parameters for the RTPEC-34 method are optimized using intelligent optimization algorithms. The application and comparison of the optimization algorithms also lead to general guidelines for their applications in the development of improved multistep methods. Last but not least, theoretical analysis is also conducted on the stability of the proposed RTPEC-34 method, which is corroborated in simulation experiments and thus provides general guidelines for the evaluation of real-time numerical methods. The RTPEC-34 method is compared with other multistep algorithms using both numerical experiments and a real engineering example. As shown in the comparison, it achieves improved performance in terms of accuracy and stability and it is also a viable and efficient algorithm for real-time aircraft dynamics simulation. [ABSTRACT FROM AUTHOR]

Multidisciplinary collaborative simulation is an important technique for the development of complex engineering systems such as aircrafts and automobiles. It emphasizes the synergic collaboration of multidisciplinary computational models, involving the exchange of simulation data generated in parallel at runtime from the numerical integration processes of these models. Current research on the interaction of models in multidisciplinary collaborative simulation is mainly focused on performing data exchange at fixed intervals, i.e. macro time steps, and using interpolation/extrapolation to find out values at the points in time when data are not available. However, the selection of an appropriate size for macro time tends to be complex and iterative. Firstly, it is mostly based on experience rather than the estimation of simulation errors by analyzing the numerical integration processes involved. Moreover, it is hardly possible to select a fixed step size suitable for all the stages of the simulation process. This paper presents a novel variable-step algorithm which is able to adjust the step size automatically at simulation runtime based on the evaluation of truncation errors. This algorithm not only avoids the instability and inaccuracy caused by a fixed large step but also speeds up the simulation by using a smaller step when a large one is not necessary. It is demonstrated in the numerical experiments that the variable-step algorithm can achieve improved simulation performance in terms of both speed and accuracy. [ABSTRACT FROM AUTHOR]

We study a two-stage, multi-item inventory system where stochastic demand occurs at stage 1, and nodes at stage 1 replenish their inventory from stage 2. Due to the complexity of stochastic inventory optimization in multi-echelon system, few analytical models and effective algorithms exist. In this paper, we establish exact stochastic optimization models by proposing a well-defined supply-demand process analysis and provide an efficient hybrid genetic algorithm (HGA) by introducing a heuristic search technique based on the tradeoff between the inventory cost and setup cost and improving the initial solution. Monte Carlo method is also introduced to simulate the actual demand and thus to approximate the long-run average cost. By numerical experiments, we compare the widely used installation policy and echelon policy and show that when variance of stochastic demand increase, echelon policy outperforms installation policy and, furthermore, the proposed heuristic search technique greatly enhances the search capacity of HGA. [ABSTRACT FROM AUTHOR]

Abstract: The design and development of complex engineering systems, e.g. mechatronics, is increasingly characterized as an interdisciplinary process, which entails the co-operative work of distributed teams. However, traditional simulation tools generally emphasize discipline-oriented purposes and centralized running, and cannot adequately support the collaborative simulation in a distributed environment. Although some tools have been developed to enable remote access to simulations, most of them focus on the sharing of information and the provision of simulations via a single and centralized program. The long-term aim of this research is to develop a collaborative modeling and simulation platform, which will provide an integrated environment for multi-disciplinary teams to create, share, and integrate simulation services on the Internet. A step towards such a platform is described which includes the infrastructures for distributed communication, the mechanism for run-time interaction, and the representation of a simulation system at the abstract level. In this paper, we will (1) formulate a collaborative simulation problem; (2) identify the factors influencing the performance of a simulation; (3) propose a method for run-time interaction to achieve optimal simulation accuracy; and (4) illustrate how the components serving different purposes are integrated. A case study is described, based on a prototype implementation of the proposed solution, to explore supporting collaborative simulation in an Internet-distributed environment. [Copyright &y& Elsevier]

[Display omitted] Extant giant pandas are divided into Sichuan and Qinling subspecies. The giant panda has many species-specific characteristics, including comparatively small organs for body size, small genitalia of male individuals, and low reproduction. Here, we report the most contiguous, high-quality chromosome-level genomes of two extant giant panda subspecies to date, with the first genome assembly of the Qinling subspecies. Compared with the previously assembled giant panda genomes based on short reads, our two assembled genomes increased contiguity over 200-fold at the contig level. Additional sequencing of 25 individuals dated the divergence of the Sichuan and Qinling subspecies into two distinct clusters from 10,000 to 12,000 years ago. Comparative genomic analyses identified the loss of regulatory elements in the dachshund family transcription factor 2 (DACH2) gene and specific changes in the synaptotagmin 6 (SYT6) gene, which may be responsible for the reduced fertility of the giant panda. Positive selection analysis between the two subspecies indicated that the reproduction-associated IQ motif containing D (IQCD) gene may at least partly explain the different reproduction rates of the two subspecies. Furthermore, several genes in the Hippo pathway exhibited signs of rapid evolution with giant panda-specific variants and divergent regulatory elements, which may contribute to the reduced inner organ sizes of the giant panda. [ABSTRACT FROM AUTHOR]

[Display omitted] • The biomass catalyst pyrolysis is assisted by a magnetic field. • 10 wt.% Ni/CaO with magnetic property has the favorable catalytic ability. • The liquid yield is decreased and the gas yield is increased by magnetic field. • The H 2 content and yield increased significantly to 62.2 vol.% and 469.22 mL/g. To prepare H 2 -rich gases from wood sawdust, thermal conversion experiments were conducted using magnetic field-assisted catalytic pyrolysis of biomass. The effects of catalyst content, magnetic field intensity and temperature on H 2 production by pyrolysis of biomass were studied. The experimental results indicated that a 10 wt.% Ni/CaO catalyst with magnetic property presented favorable catalytic ability to produce H 2 by biomass pyrolysis. The H 2 content and yield increased with increasing magnetic field intensity. Under the condition of temperature of 650 ℃, the magnetic field intensity of 80 m T and 10 wt.% Ni/CaO catalyst, the H 2 content and yield reached 62.20 vol.% and 469.2 mL/g. Compared with the absence of a magnetic field, they increased by 9.50 % and 19.78 %, respectively. Thus, H 2 -rich gas production by biomass catalytic pyrolysis assisted with a magnetic field is a promising technology for biomass utilization. [ABSTRACT FROM AUTHOR]

Immune checkpoint molecules, also known as cosignaling molecules, are pivotal cell-surface molecules that control immune cell responses by either promoting (costimulatory molecules) or inhibiting (coinhibitory molecules) a signal. These molecules have been studied for many years. The application of immune checkpoint drugs in the clinic provides hope for cancer patients. Recently, the poliovirus receptor (PVR)-like protein cosignaling network, which involves several immune checkpoint receptors, i.e., DNAM-1 (DNAX accessory molecule-1, CD226), TIGIT (T-cell immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibitory motif (ITIM)), CD96 (T cell activation, increased late expression (TACLILE)), and CD112R (PVRIG), which interact with their ligands CD155 (PVR/Necl-5), CD112 (PVRL2/nectin-2), CD111 (PVRL1/nectin-1), CD113 (PVRL3/nectin-3), and Nectin4, was discovered. As important components of the immune system, natural killer (NK) and T cells play a vital role in eliminating and killing foreign pathogens and abnormal cells in the body. Recently, increasing evidence has suggested that this novel cosignaling network axis costimulates and coinhibits NK and T cell activation to eliminate cancer cells after engaging with ligands, and this activity may be effectively targeted for cancer immunotherapy. In this article, we review recent advances in research on this novel cosignaling network. We also briefly outline the structure of this cosignaling network, the signaling cascades and mechanisms involved after receptors engage with ligands, and how this novel cosignaling network costimulates and coinhibits NK cell and T cell activation for cancer immunotherapy. Additionally, this review comprehensively summarizes the application of this new network in preclinical trials and clinical trials. This review provides a new immunotherapeutic strategy for cancer treatment. [ABSTRACT FROM AUTHOR]

• A framework based on Bidirectional Gated Neural Networks is proposed for fault diagnosis with uncertainty in dynamic environments. • A Sample Sensitive Bidirectional Gated Neural Networks model is developed to tackle imbalanced fault diagnosis challenges. • Cost sensitive active learning is used to explore unlabeled data. • Effective methods are developed to address both binary Fault Diagnosis and multi-class Fault Diagnosis. Most existing fault diagnosis methods may fail in the following three scenarios: (1) serial correlations exist in the process data; (2) fault data are much less than normal data; and (3) it is impractical to obtain enough labeled data. In this paper, a novel form of the bidirectional gated recurrent unit (BGRU) is developed to underpin effective and efficient fault diagnosis using cost sensitive active learning. Specifically, BGRU is devised to consider the dynamic behavior of a complex process. In the training phase of BGRU, the idea of weighting each training example is proposed to reduce the effect of class imbalance. Besides, in order to explore the unlabeled data, cost sensitive active learning is utilized to select the candidate instances. The effectiveness of the proposed method is evaluated on the Tennessee Eastman (TE) dataset and a real plasma etching process dataset. The experiment results show that the proposed cost senstive active learning bidirectional gated recurrent unit (CSALBGRU) method achieves better performance in both binary fault diagnosis and multi-class fault diagnosis. [ABSTRACT FROM AUTHOR]

Kinematics simulation is central to the control of serial manipulators in human machine interaction, which highly depends upon computational efficiency and accuracy for the sake of enabling real-time analysis and interaction. In this paper, a novel learning-based multiscale modelling approach is proposed to effectively address the efficiency and accuracy trade-offs through a combination of models with different levels of fidelity. Specifically, low-fidelity models are formulated using Kernel Ridge Regression (KRR) to achieve much lower computational cost as its kinematic approximation. Additionally, high-fidelity models are created based on the Long Short Term Memory (LSTM) neural network, which can calibrate fidelity by training the significant samples and eliminate position singularity of a serial manupulator. The proposed approach is evaluated both by using numerical tests and by applying it to a collaborative industrial robot arm. The experimental results obtained demonstrate that it can achieve better performance in terms of both accuracy and efficiency compared with the other popular methods. [ABSTRACT FROM AUTHOR]

• The catalytic rules of the non-halogen anion and cation for the cycloaddition reaction were revealed. • the reaction mechanism was deeply explored by DFT calculations and in-situ IR spectroscopy. • Six new non-halogen ionic liquids were synthesized and used as efficient catalysts for cycloaddition reaction. Non-halogen ionic liquids are considered efficient catalyst for CO 2 conversion into cyclic carbonates. In this work, six carboxylic acid ionic liquids with different types and sizes of cations are designed and synthesized to systematically investigate the catalytic contribution of the non-halogen cation and anion for CO 2 conversion into cyclic carbonate. The result shows that the anion could improve the catalytic activity by improve the basicity of ionic liquids, and the cation that has a weak interaction with anion is more conducive for the reactivity. Further, the reaction mechanism is deeply studied by using the optimized [P 4444 ][IsoNic]: two reaction intermediates are captured by In-situ infrared, combined with DFT calculation, the anion directly inducing ring-opening mechanism is proposed. This work provides an in-depth understanding of catalytic contribution of non-halogen ionic liquids used in cycloaddition reaction. Graphical Abstract [Display omitted] [ABSTRACT FROM AUTHOR]

The continuous annealing line is a key equipment in industrial metal heat treatment. It is a large-thermal-inertia cascade thermal system, and has minute-level time hysteresis between adjustable control parameters and outlet strip temperature, which is challenging for real operation. Large strip temperature deviation from targets will greatly damage the mechanical properties and the surface coating quality of the strip. Traditional methods such as energy balance method, computational fluid dynamics and end-to-end data-driven model are hard to solve the problem due to complex parameter settings, large computing costs and low interpretability, respectively. Here, a novel way seamlessly coupling physical model and data based on physics-informed neural network is used to solve the problem. The mathematical-physical model of the continuous annealing line is developed and a physics-informed neural network model is built to solve inverse problem to identify the heat transfer coefficient of the continuous annealing line. The strip temperature is numerically simulated both with physics-informed neural network model and computational fluid dynamics model. The simulated results are compared with the measured data. The simulated outlet strip temperatures of the two models agree well with the measured data and the accuracy of the identified heat transfer coefficient is verified, where the computational fluid dynamics model has higher accuracy. The physics-informed neural network models developed here will benefit intelligent feedforward cascade control of the continuous annealing line and improve the strip quality in real productions. [ABSTRACT FROM AUTHOR]

Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design knowledge for such a collaborative knowledge sharing scheme. This paper proposes a hypernetwork-based approach to explicitly capturing the relationships between various elements in a multifaceted knowledge representation. Specifically, a knowledge hypernetwork model is constructed, which is composed of a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to specific node properties. In addition, topological characteristics of the hypernetwork structure are analyzed together with the statistical indicators. Based on this model, the Bayesian approach is adopted to conduct the collaborative reasoning process whereby knowledge elements relevant to the current design task are recommended according to the issues to be resolved and the current design context. A case study conducted in this work shows that the proposed approach is effective in capturing the complex relationships between multi-faceted knowledge elements and enables collaborative retrieval and reasoning of knowledge records. [ABSTRACT FROM AUTHOR]

Highlights • A novel SPiN diode with high carrier concentration. • A reconfigurable dipole antenna based on SPiN diodes is also examined. • A band stop filter is introduced to improve the integration of antenna system. Abstract To improve the integration of reconfigurable antenna systems, a novel high-integration solid state plasma reconfigurable dipole antenna based on surface PiN (SPiN) diodes was presented in this paper. Silicon-based SPiN diodes were the basic building blocks of plasma channel, and carrier concentration within the intrinsic region achieved a relatively high level (exceeding 1018 cm−3). In this paper, parameters of the plasma region have been extensively discussed, and several optimized band stop filters were also introduced to replace the conventional capacitor and inductor. This method greatly improved the integration of antenna system to meet the requirements of modern communications. The resonant frequencies at 2.43 GHz and 2.53 GHz have been achieved by turning on or off different sections of the dipole. The Voltage Standing Wave Ratio (VSWR) of the antenna was 1.03 and 1.34, respectively. Other parameters of this antenna were also investigated in this paper. Experimental results confirm the usefulness of the SPiN diodes within solid state plasma antenna and other semiconductor fields. [ABSTRACT FROM AUTHOR]

High-quality low Sn-content GeSn film has been grown on tensile strained Ge/Si (100) substrate using a physical vapor deposition reactor and no subsequent annealing was carried out after the deposition of the film. High Resolution X-ray diffraction (HR-XRD) measurement was performed to estimate the crystallinity and the residual strain in the Ge layer. Secondary-ion mass spectrometry (SIMS) and X-ray photoelectron spectrometer (XPS) show Sn incorporation of the film is approximately 3%. High-resolution transmission electron microscopy (TEM) and atomic force microscopy (AFM) results show that the crystallinity of the film is quite good and it shows a smooth surface (RMS = 0.744 nm) in 5 µm × 5 µm scan. Raman spectroscopy and photoluminescence (PL) measurements were carried out to study the structural and optical properties of the film. Room-temperature PL of the film was observed and it results in a decrease in the difference between Γ and L valleys of tensile strained Ge (0.21%) from 87 meV to 76 meV, which makes this method promising for obtaining Pseudo-direct bandgap group IV films. [ABSTRACT FROM AUTHOR]

Readily-available and efficient catalyst is essential for activating oxidants to produce reactive species for deeply remediating water bodies contaminated by antibiotics. In this study, Ferrocene (Fc) was introduced to establish a heterogeneous photo-Fenton system for the degradation of sulfonamide antibiotics, taking sulfamethoxazole as a representative. Results showed that the removal of sulfamethoxazole was effective in Fc-catalyzed photo-Fenton system. Electron spin resonance and radical scavenging experiments verified that there was a photoindued electron transfer process from Fc to H 2 O 2 and dissolved oxygen resulting in the formation of OH that was primarily responsible for the degradation of sulfamethoxazole. The reactions of OH with substructure model compounds of sulfamethoxazole unveiled that aniline moiety was the preferable reaction site of sulfamethoxazole, which was verified by the formation of hydroxylated product and the dimer of sulfamethoxazole in Fc-catalyzed photo-Fenton system. This heterogeneous photo-Fenton system displayed an effective degradation efficiency even in a complex water matrices, and Fc represented a long-term stability by using the catalyst for multiple cycles. These results demonstrate that Fc-catalyzed photo-Fenton oxidation may be an efficient approach for remediation of wastewater containing antibiotics. [ABSTRACT FROM AUTHOR]

A recent study has described the normal vaginal bacterial community in giant pandas, but there is a lack of knowledge of the fungal community residing in the vagina of giant pandas. In order to comprehensively understand the vaginal fungal microbial diversity and abundance in giant pandas, high throughput sequencing was used to analyse the ITS1 region, based on thirteen samples taken from the pandas' vaginas, which were grouped by sampling points and age. The results showed that the most abundant phyla were Basidiomycota (73.37%), followed by Ascomycota (20.04%), Zygomycota (5.23%), Glomeromycota (0.014%) and Chytridiomycota (0.006%). At the genus level, Guehomyces (37.92%) was the most abundant, followed by Cladosporium (9.072%), Trichosporon (6.2%) and Mucor (4.97%). Furthermore, Candida only accounted for a low percentage of the vaginal fungal community. With the saturation of rarefaction curves and fungal diversity indices, the samples from Dujiangyan and Chungking Safari Park (DC group) showed a higher fungal species richness and diversity than other living environments. Shannon diversity indices showed significant difference between group WL (Wolong nature reserve) and DC ( P  < .05). Additionally, a higher diversity was found in ten to fifteen years old (Group 2) than other groups. Group 2 and Group 3 displayed significant differences in the diversities of their vaginal fungal communities ( P  < .05). These data that has been collected from this research will be helpful for further study to improve the reproductive status of giant pandas. [ABSTRACT FROM AUTHOR]

Pt-doped K x WO 3 nanorods with excellent near-infrared shielding and heat insulation performance were prepared by solvothermal reduction method using cheap sodium tungstate as raw materials. The effects of K/W ratio and Pt doping on the microstructure, morphology and transparent heat insulation performance of K x WO 3 particles were investigated. It was found that Pt doping contributed to increasing the content of W in reduced state (W 5+ and W 4+ ), promoting the uniform growth of hexagonal K x WO 3 nanorods and decreasing the particle size. Compared with the undoped K x WO 3 particles, Pt-doped K x WO 3 samples exhibited improved near-infrared shielding and heat insulation properties. Particularly, when K/W was 0.4, the incorporation of trace Pt (0.1 mol%) greatly improved the near-infrared shielding and transparent insulation properties of K x WO 3 particles, which is ascribed to its lower powder resistivity and more content of W in reduced state (W 5+ and W 4+ ). Whereas too much Pt-doping amount tended to decrease the visible light transmittance of K x WO 3 . The mechanism of Pt doping promoting the W 5+ /W 4+ formation and improving the heat insulation of K x WO 3 were discussed. It is suggested that the trace Pt-doped K x WO 3 particles with excellent transparency and heat insulation properties are more suitable for the preparation of transparent insulation films and have broad applications in the field of energy-saving window glasses. [ABSTRACT FROM AUTHOR]

In this paper, a fully-depleted (FD) Ge double-gate (DG) n-type Tunneling Field-Effect Transistors (TFET) structure is studied in detail by two-dimensional numerical simulation. The simulation results indicated that the on-state current I on and on-off ratio of the FD Ge DG-TFET increases about 1 order of magnitude comparing with the Conventional Ge DG-TFET, and I on =3.95×10 -5 A/μm and the below 60 mV/decade subthreshold swing S=26.4 mV/decade are achieved with the length of gate L D =20 nm, the workfuntion of metal gate Φ m =0.2 eV and the doping concentration of n + -type-channel N D =1×10 18 cm -3 . Moreover, the impacts of Φ m , N D and L D are investigated. The simulation results indicated that the off-state current I off includes the tunneling current at the middle of channel I B the gated-induced drain leakage (GIDL) current I GIDL . With optimized Φ m and N D , I off is reduced about 2 orders of magnitude to 2.5×10 -13 A/μm with L D increasing from 40 nm to 100 nm, and on-off ratio is increased to 1.58×10 7 . [ABSTRACT FROM AUTHOR]

In this paper, a novel structure of the fully-depleted Ge-on-Insulator (GOI) n-channel MOSFET with field plate is proposed and studied by two-dimensional numerical simulation. The results indicate that the gated-induced drain leakage (GIDL) of the fully-depleted GOI NMOSFET with field plate (FD-FP NMOSFET) is suppressed effectively. Besides, off-state current I off decreases by 2 orders of magnitude compared with the device without field plate. The impacts of the distance of field plate from the drain L cb and the difference of work function between field plate metal and channel material Φ fps on the electrical characteristics of FD-FP NMOSFET are investigated. FD-FP NMOSFET with a Φ fps of 0 eV and a L cb of 5 nm, demonstrates a reduction in I off by more than 3 orders of magnitude as compared with the control device. An on/off ratio I on /I off = 2.57 × 10 7 , and a sub-threshold swing of 76.8mV/decade are achieved. [ABSTRACT FROM AUTHOR]