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Objective: To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. Methods: The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. Results: Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. Conclusion: This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the world's economy has led to the large-scale development and utilization of ecological resources on the earth, due to which the ecological environment has been continuously and seriously damaged, resulting in the waste of resources, soil erosion, land desertification, etc. To avoid further damage to the ecological environment and ecological resources, improve the utilization rate of ecological resources, and ensure the sustainable development of human society, it is necessary to evaluate the ecological environment. In this study, we collected the required data using the Delphi method and remote sensing technology. Secondly, the green Olympic building evaluation system (which refers to the CASBEE method in Japan) was used to evaluate the impact of green roofs on architectural design and the urban ecological environment. Third, a deep learning (DL)-based hybrid model, which consists of a convolutional neural network (CNN) and long–short-term memory (SLSTM), known as CNN–LSTM, was used to evaluate the impact of green roofs on urban ecology and building architectural design. The influence of thermal comfort on the indoor environment of green roof buildings was studied. For experimentation, six samples of Shanghai Thumb Plaza, Splendid Tesco Point, Chaoshan Yuan Hotel, Green Management Office, Huangpu District Domestic Waste Transfer Station, and Changning District Fuxin Slaughterhouse were selected as evaluation objects, and the effect of green roofs on building design and urban ecology was evaluated from six levels: ecological, ornamental, safety, functional, social, and economic. Both the CASBEE and DL-based methods, CNN–LSTM, performed well and increased the evaluation results to some extent. The CNN–LSTM model increased the accuracy of the system by 3.55%, precision by 3.50%, recall by 4.46%, and F1-score by 3.30%. Overall, this study summarizes the existing problems of green rooftop buildings in Shanghai at this stage, which is conducive to formulating optimization strategies to improve the ecological benefits of green roof buildings and has important practical significance for realizing the sustainable development of human society. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis is an important research field for rotating machinery health monitoring. Recently, many intelligent fault diagnosis methods driven by big data, such as transfer learning, have been studied. However, there are two shortcomings for the prior transfer learning method in industry application. First, it is necessary to design a complex loss function to enhance the similarity between the two domains further. Second, previous studies required big data both in source and target task, without considering the lack of sufficient training samples. Inspired by relevant research work, this article proposes a local joint distribution discrepancy to increase similar features. A sub-domain adaptive transfer learning is designed to detect bearing faults based on the residual network. Two kinds of transfer experiments are designed to verify the method effectiveness. After that, the impact of small training samples and noise on the results is explored. The proposed method reaches high accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Considering the differences between overhead lines and underground cables, the lower bound of the restoration time of each load is estimated based on optimal restoration strategies in different fault scenarios. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis using deep learning has achieved much success in recent years. Using deep learning method to diagnose bearing fault requires designing an appropriate neural network model and then train with a massive data. On the one hand, up to now, a variety of neural network structures have been proposed for different diagnostic tasks, but there is a lack of research of unified structure. On the other hand, the fault data of the training neural network are collected from the fault location point, which is quite different from the actual data, because the sensor cannot be located at the fault location point accurately. This paper attempts to design a unified neural network structure based on Resnet and improve the generalization performance by using transfer learning techniques. The effectiveness of the proposed method in this paper is verified using experiment under different working loads and non-fault location point. [ABSTRACT FROM AUTHOR]

The phase partition and site preference of Re atoms in a ternary Ni–Al–Re model alloy, including the electronic structure of different Re configurations, are investigated with first-principles calculations and atom probe tomography. The Re distribution of single, nearest neighbor (NN), next-nearest neighbor (NNN), and cluster configurations are respectively designed in the models with γ and γ′ phases. The results show that the Re atoms tend to entering γ′ phase and the Re atoms prefer to occupy the Al sites in γ′ phase. The Re cluster with a combination of NN and NNN Re–Re pair configuration is not preferred than the isolated Re atom in the Ni-based superalloys, and the configuration with isolated Re atom is more preferred in the system. Especially, the electronic states are analyzed and the energetic parameters are calculated. The electronic structure analyses show there exists strong Ni–Re electronic interaction and it is mainly contributed by the d–d hybridization. The characteristic features of the electronic states of the Re doping effects are also given. It is also found that Re atoms prefer the Al sites in γ′ side at the interface. The density of states at or near the Fermi level and the d–d hybridizations of NN Ni–Re are found to be important in the systems. [ABSTRACT FROM AUTHOR]

By use of the self-consistent-field Xα scattered-wave (SCF-Xα-SW) method, the electronic structure was calculated for two models of γ-Fe[SUB12]-B[SUB2]-V cluster as well as the γ-Fe[SUB13] cluster for contrast. Results are presented for total energy, energy levels, density of states, and one-electron properties. Comparisons are made between two models of γ-Fe[SUB12]-B[SUB2]-V cluster, and between γ-Fe[SUB12]-B[SUB2]-V and γ-Fe[SUB13] clusters. The analyses are of the effect of the boron-vacancy (B-V-B) complex on the physical properties of γ-Fe. The calculations indicate that there is a strong interaction between boron and host atoms. Thus the B-V-B complex is favorable for thermal stability. Comparison of the average energy of an atom between the γ-Fe[SUB12]-B[SUB2]-V and α-Fe[SUB8]-B[SUB2]-V clusters shows that a B-V-B complex may restrain the nucleation of ferrite in austenite transformation and thereby is beneficial to the hardenability on quenching of austenitic steel. The results also indicate a strong binding between B atoms and vacancies, which is favorable for the creep-rupture life of austenitic steel. [ABSTRACT FROM AUTHOR]

The influence of refractory additions on tensile deformation behavior has been studied at an intermediate temperature (760°C) using scanning and transmission electron microscopy. Tensile experiments revealed that elemental additions of tantalum (Ta), tungsten (W), and rhenium (Re) could improve the yield strength (YS) of the model alloys with an order of YS(Ni–Al–W) > YS(Ni–Al–Ta) > YS(Ni–Al-Re) > YS(Ni–Al). During the plastic deformation of model alloys at the intermediate temperature, the deformation via Orowan bypassing and shear could be activated synergistically. The changes of anti-phase boundary (APB) energies on {001} and {111} planes are related to the cross-slip driving force, critical resolved shear stress and stage II work hardening rate in γ' precipitates. The effects of γ' size and refractory additions (Ta, W, and Re) on the yield strength of model alloys at intermediate temperature are also discussed, which can provide qualitative reference for alloy design. • The effect of Re on the yield strength and plasticity is less than that of Ta and W. • The lattice distortion related with changes in APB energies and order strengthening. • There is a positive correlation between cross-slip driving force and stage II WHR. • At medium temperature, Orowan bypassing and γ' shearing are activated synergistically. • W and Ta are two promising candidates for replacing precious Re element. [ABSTRACT FROM AUTHOR]

Abstract: Combing computational efficiency and accuracy, quantum mechanic/molecular mechanic (QM/MM) methods are promising for understanding and predicting materials properties at atomic scales. We present a general QM/MM method that can be implemented in a variety of QM and MM combinations; we focus on a tight-binding and a plane-wave pseudopotential methods for the QM part and the Stillinger–Weber and EAM potentials for the MM part. The QM/MM coupling errors are analyzed. It is found that a sufficient size of the coupling region and the buffer zone is crucial in minimizing the coupling errors. On the other hand, the size of the weighting zone turns out to be less important. The QM/MM method is applied to model the dynamical propagation of Si cracks with different orientations and under different mode I loadings. The QM/MM method is found to correctly reproduce the brittle fracture of Si, whereas the SW potential fails to do the same. The QM/MM method is also used to study the ductile fracture in Au and compared to the EAM potential. Finally, the QM/MM method is applied study the vacancy diffusion in a Cu grain boundary. The QM/MM results compare very well to the previous EAM results. [ABSTRACT FROM AUTHOR]

Abstract: Using first-principles density function for molecules method (DMol) and discrete variational method (DVM) based on the density functional theory, we studied the doping effect of Re in Ni3Al. The structure relaxation and the alloying energy show that Re has a strong Al site preference and leads to the local deformation, which is in agreement with the experimental results and other theoretical results. In addition, the charge density difference and the bond order show that Re can strongly enhance the interatomic interaction between the nearest neighbor atoms. From the density of states and the Pauli spectrum, we find that resonance states and localized states are induced by doping Re, and the doped Re atom forms the hybridized bond with the nearest neighbor atoms. [Copyright &y& Elsevier]

Using the molecular dynamics method, we have constructed two kink models corresponding to the (100){010} and (100){011} edge dislocations (EDs) in body centred cubic (bcc) Fe. It is found that the geometric structure of a kink depends on the type of edge dislocation and the structural energies of the atoms sites in the dislocation core region. The formation energies, migration energies and widths of the kinks in different types of EDs are calculated. The results show that formation and migration of the kink in the (100){010} edge dislocation are difficult. The (100){011} edge dislocation moves primarily through kink nucleation, rather than kink migration. [ABSTRACT FROM AUTHOR]

The effects of Si doping on geometric and electronic structure of closed carbon nanotube (CNT) are studied by, a first-principles method, DMol. It is found that the local density of states at the Fermi level (EF) increases due to the Si-doping and the non-occupied states above the EF go down toward the lower energy range under an external electronic field. In addition, due to the doping of Si, a sub-tip on the CNT cap is formed, which consisted of the Si atom and its neighbor C atoms. From these results it is concluded that Si-doping is beneficial to the CNT field emission properties. [ABSTRACT FROM AUTHOR]

The economic effect of climate hazard events varies by time and by location. This paper investigates how climate shocks to local property markets transmit to capital markets and provides evidence of the extent to which forward‐looking climate risk is capitalized into the public valuations of those property markets. We first quantify the exposure of real estate portfolios to locations that recently experienced climate events (Event Exposure). Using an event study framework, we find that, in the post‐event period, a one‐standard‐deviation increase in ex‐ante Event Exposure is associated with a 0.2–1.4 percentage points decrease in quarterly stock returns. Cross‐sectional analyses reveal that differences in return effects can be explained by variation in the extent to which the area focuses on climate change. Similarly, we find that forward‐looking climate risk assessment negatively affects firm valuations only in markets with a focus on climate change. Consistent with these findings, we provide evidence that climate events (shocks) induce retail investors (noise traders) to decrease their stock holdings and that blockholders tend to take the opposite side in these transactions. We also show that conditioning on consumer sentiment helps to explain cross‐sectional variation in the response of stock returns to climate events. [ABSTRACT FROM AUTHOR]

Face-fo-face contact is known to be the primary transmission route of the Covid-19 virus. Social norm and positional good theories suggest that people would interact with others selectively in order to fulfil social needs and prevent infections simultaneously. Previous studies focus on the impact of conventional social status factors (e.g., income and occupation) on virus transmission. It is unclear whether housing status, as another proxy of social status, also plays a role. This article thus makes use of three salient housing features – housing price, ownership rate, and wealth gap – to elucidate housing status and its link to public health. We combine the data of Covid-19 cases, housing transactions, and Census statistics to quantitatively estimate the association between housing status and Covid-19 infections at the street block level in Hong Kong from January 2020 to December 2021. It is found that among infected neighbourhoods, those with lower housing prices, lower ownership share, and less variation in housing wealth face a larger risk of Covid-19 infections. The potential circular causation between housing status and viral transmission highlights the need for better coordination between public health and housing policies. [ABSTRACT FROM AUTHOR]

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene. [ABSTRACT FROM AUTHOR]

The electron structure of the typical calcium phosphate ceramic, hydroxyapatite (OHAP), is investigated by the use of the tight-binding recursion method. The local density of states, partial density of states, structural energy and charge transfer are calculated, as well as the interatomic energy between atoms and bond order integral. Some physical and chemical properties of OHAP are discussed. The calculated results show that both the OH group and the PO4 group can be regarded as individual structure units in OHAP owing to strong interaction between atoms. Meanwhile, the atomic structure of a grain boundary is determined on the basis of the near-coincidence site lattice model with consideration of the bond length in the crystal and the lattice distortion. The electron structure and energy of a Sigma = 7 grain boundary are calculated. The results show that a grain boundary with a slight lattice misfit has a lower energy. [ABSTRACT FROM AUTHOR]

The integration of numerous intermittent renewable energy sources (IRESs) poses challenges to the power supply-demand balance due to the inherent intermittent and uncertain power outputs of IRESs, which requires higher operational flexibility of the power system. The deployment of flexible ramping products (FRPs) provides a new alternative to accommodate the high penetration of IRESs. Given this background, a bi-level risk-limiting real-time unit commitment/real-time economic dispatch model considering FRPs provided by different flexibility resources is proposed. In the proposed model, the objective is to maximize the social surplus while minimizing the operational risk, quantified using the concept of conditional value-at-risk (CVaR). Energy and ramping capabilities of conventional generating units during the start-up or shut-down processes are considered, while meeting the constraints including unit start-up/shut-down trajectories and ramping up/down rates in consecutive time periods. The Karush–Kuhn–Tucker (KKT) optimality conditions are then used to convert the bi-level programming problem into a single-level one, which can be directly solved after linearization. The modified IEEE 14-bus power system is employed to demonstrate the proposed method, and the role of FRPs in enhancing the system flexibility and improving the accommodation capability for IRESs is illustrated in some operation scenarios of the sample system. The impact of the confidence level in CVaR on the system operational flexibility is also investigated through case studies. Finally, a case study is conducted on a regional power system in Guangdong Province, China to demonstrate the potential of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the most common aggressive tumors in the world. Despite the availability of various treatments, its prognosis remains poor due to the lack of specific diagnostic indicators and the high heterogeneity of HCC cases. CircRNAs are noncoding RNAs with stable and highly specific expression. Extensive research evidence suggests that circRNAs mediate the pathogenesis and progression of HCC through acting as miRNA sponges, protein modulators, and translation templates. Tumor microenvironment (TME) has become a hotspot of immune-related research in recent years due to its effects on metabolism, secretion and immunity of HCC. Accordingly, understanding the role played by circRNAs in TME is important for the study of HCC. This review will discuss the crosstalk between circRNAs and TME in HCC. In addition, we will discuss the current deficiencies and controversies in research on circRNAs and predict future research directions. [ABSTRACT FROM AUTHOR]

Background: Disulfidptosis, a recently discovered cellular death mechanism, has not been extensively studied in relation to breast cancer (BC). Specifically, no previous research has integrated disulfidptosis-related genes (DRGs), cuproptosis-related genes (CRGs), and ferroptosis-related genes (FRGs) to construct a prognostic signature for BC. Methods: DRGs, CRGs and FRGs with prognostic potential were identified through Cox regression analysis. A predictive model was constructed by intersecting the core genes obtained from unsupervised cluster analysis and weighted correlation network analysis (WGCNA). Differences in chemotherapy drug sensitivity, immune checkpoint levels were analyzed according to different risk score groups. The expression of the core disulfidptosis gene, SLC7A11, was analyzed using immunofluorescence. Results: Single-cell RNA sequencing analysis revealed differential expression of DRGs in the BC tumor microenvironment. We developed a prognostic model, consisting of six genes, based on machine learning which included unsupervised cluster analysis and Lasso-Cox analysis. An internal training set and a validation set, both derived from the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) database, GSE20685 and GSE42568 as external validation sets all verified the model's validity. The low-risk group exhibited increased sensitivity to paclitaxel. Additionally, the high-risk group demonstrated significantly higher expression of tumor mutation burden and microsatellite instability compared to the low-risk group. A nomogram confirmed that the risk score can be an independent risk factor for BC. Notably, our findings highlighted the impact of SLC7A11 on the BC tumor microenvironment. Immunofluorescence analysis revealed significantly higher expression of SLC7A11 in BC tissues compared to paracancerous tissues. Conclusion: Multiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients. [ABSTRACT FROM AUTHOR]

In Ni-based single crystal superalloys, ruthenium is sometimes introduced as one of the creep resistances through retarding the thermally-activated deformation processes, such as dislocation glide and climb. In the present study, an embedded-atom-method potential of Ni–Al–Ru system was constructed. Using the present potential, the effect of Ru on the lattice misfit between γ (Ni) and γ ' (Ni 3 Al) phases was investigated. The results show that Ru doping decreases the lattice misfit, which is consistent with the calculations from first-principles. The interaction between Ru and misfit dislocation was also studied. The results of interaction energy between Ru and misfit dislocation, and the simulations of misfit dislocation motion in the Ru-doping system by molecular dynamics method both show that Ru possesses the pinning effect on the misfit dislocation. These results can provide useful help for further insights of the role of Ru in the alloys. Image 1 • A ternary Ni–Al–Ru EAM potential is constructed. • The results of interaction energy show that Ru possesses the pinning effect. • Ru atom and dislocation attract mutually from dislocation motion simulations. [ABSTRACT FROM AUTHOR]

Presents a study which examined the electronic-structure effects on the intermetallic compounds Al[sub3]Ti with the addition of the alloying elements chromium or manganese. Calculation method; Results and discussion; Conclusion.

Focuses on a study which investigated the electronic structure of aluminum (Al)-vanadium-titanium alloys with and without planar faults. Methodology of the study; Results for the bond order integral and interaction energy between atoms, local density of states, and the energies of planar faults; Discussion on the effect of the directional bonding of the p orbital of Al; Findings of the local density of states of the atoms on the planar faults.

Background: Among the most lethal cancers, pancreatic adenocarcinoma (PAAD) is an essential component of digestive system malignancies that still lacks effective diagnosis and treatment methods. As exosomes and competing endogenous RNA (ceRNA) regulatory networks in tumors go deeper, we expect to construct a ceRNA regulatory network derived from blood exosomes of PAAD patients by bioinformatics methods and develop a survival prediction model based on it. Methods: Blood exosome sequencing data of PAAD patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, lncRNA, and circRNA were differentially analyzed by R. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Then, we conducted GO and KEGG enrichment analysis of mRNA in the ceRNA network. Genes that express differently in pancreatic cancer tissues compared with normal tissues and associate with survival (P < 0.05) were determined as Hub genes by GEPIA. We identified optimal prognosis-related differentially expressed mRNAs (DEmRNAs) and generated a risk score model by performing univariate and multivariate Cox regression analyses. Results: 205 DEmRNAs, 118 differentially expressed lncRNAs (DElncRNAs), and 98 differentially expressed circRNAs (DEcircRNAs) were screened out. We constructed the ceRNA network, and a total of 26 mRNA nodes, 7 lncRNA nodes, 6 circRNA nodes, and 16 miRNA nodes were identified. KEGG enrichment analysis showed that the DEmRNAs in the regulatory network were mainly enriched in Human papillomavirus infection, PI3K-Akt signaling pathway, Osteoclast differentiation, and ECM-receptor interaction. Next, six hub genes (S100A14, KRT8, KRT19, MAL2, MYO5B, PSCA) were determined through GEPIA. They all showed significantly increased expression in cancer tissues compared with control groups, and their high expression pointed to adverse survival. Two optimal prognostic-related DEmRNAs, MYO5B (HR = 1.41, P < 0.05) and PSCA (HR = 1.10, P < 0.05) were included to construct the survival prediction model. Conclusion: In this study, we successfully constructed a ceRNA regulatory network in blood exosomes from PAAD patients and developed a two-gene survival prediction model that provided new targets which shall aid in diagnosing and treating PAAD. [ABSTRACT FROM AUTHOR]

Due to evaluation performance limits and compatibility problems with PDP (Policy Decision Point) in practical information systems, some established schemes have limits in handling massive complex requests. To address the existing challenges of fast rule match on interval values, we propose a novel policy evaluation engine, namely Poliseek with three desired modules. A preprocessing module of Poliseek is equipped with a static encoding strategy and converts the XACML rules and requests into four-dimensional numeric vectors in an attribute space. Owing to a novel optimization object of minimizing interval collisions, a dimensionality reducer and diffuser module can generate candidate values related to each rule vector in the identification space. These values and requests are handled by a fast policy evaluation module using well-constructed hash buckets and a characterized search algorithm. The experimental results show that if the number of requests reaches 10,000, Poliseek can find the target rule approximately 1090, 15, and 15 times faster than the Sun PDP, XEngine, and SBA-XACML, respectively. Poliseek also offers a fast evaluation progress of handling 10,000 complex policy rules with interval attribute values in 275.9 ms, which shows its strong robustness and practicality. [ABSTRACT FROM AUTHOR]

• MAPK14 and ACSL4 were identified as the core ferroptosis-related genes by comprehensive bioinformatics analysis based on all patients with sepsis from GEO database. • MAPK14 and ACSL4 expressions were validated in lung and heart tissue of sepsis-induced mice. • MAPK14and ACSL4 were significantly increased in blood of sepsis patients. Sepsis is a life-threatening infectious disease in which an immune inflammatory response is triggered. The potential effect of ferroptosis-related genes (FRGs) in inflammation of sepsis remained unclear. We focused on identifying and validating core FRGs and their association with immune infiltration in blood from currently all patients with sepsis. All current raw data of septic blood were obtained from Gene Expression Omnibus. After removing the batch effect merging into a complete dataset and obtaining Diferentially expressed genes (DEGs). Common cross-talk genes were identified from DEGs and FRGs. WGCNA, GO, KEGG, PPI, GESA, ROC curves, and LASSO regression analysis were performed to indentify and validate key genes based on external septic datasets. Infiltrated immune cells in 2 hub genes (MAPK14 and ACSL4) were conducted using CIBERSORT algorithm and Spearman correlation analysis. Further, the expressions of 2 core FRGs were verified in the LPS-induced ALI and cardiac injury sepsis mice. MAPK14 and ACSL4 were identified, mostly enriched in T cell infiltration through NOD-like receptor signaling pathway according to the high or low 2 hub genes expression. The upregulated 2 ferroptosis-related genes were validated in LPS-induced ALI and cardiac injury mice, accompanied by upregulation of the NLRP3 pathway. MAPK14 and ACSL4 could become robustly reliable and promising biomarkers for sepsis by regulating ferroptosis through the NLRP3 pathway, which is mainly associated with T-cell infiltration. [ABSTRACT FROM AUTHOR]

Using the first-principles self-consistent discrete variational method based on density functional theory, we investigated the effect of light impurities C and N on the electronic structure of kink on the [100](010) edge dislocation (ED) in bcc iron. Our energetic calculations show that the light impurities have a strong segregation tendency to enter the kink. The results of the charge distribution and the local density of states indicate that the strong bonds between the impurity atoms and the neighboring Fe atoms are formed due to the hybridizations of impurity atoms 2p states and Fe 3d4s4p states. The introduction of light impurities can stabilize the kink system, and impedes the sideward motion of the kink in the (100](010) ED. This is, the light impurities induce a strong pinning effect on the [100](010) ED and may result in the solid solute hardening. [ABSTRACT FROM AUTHOR]

It is important and necessary to understand the micro-mechanism of rhenium (Re) effect in the deformation process. The in-situ tensile experiments of Ni–Al and Ni–Al-Re model single crystal superalloys have been carried out in scanning electron microscope at 750 °C. It is found that Re can promote the complex stacking fault (CSF) formation, which is similar to the previous results after tensile experiments at room temperature. The micro-mechanism of Re promoting the CSF formation has been investigated by means of advanced probe-corrected scanning transmission electron microscopy and energy dispersive X-ray spectroscopy. The local high concentration of Re is observed at CSF regions after tensile experiments both at room temperature and 750 °C. The first principles calculation results further indicate that low concentration of Re increases CSF energy, while high concentration of Re decreases CSF energy, which is consistent with the experimental results. • In-situ tensile experiments of model single crystal superalloys are carried out. • Re can promote the complex stacking fault (CSF) formation. • High concentration of Re is observed in local region along CSF. • The calculation results show high concentration of Re decreases CSF energy. [ABSTRACT FROM AUTHOR]

Creep rupture life is a key material parameter for service life and mechanical properties of Ni-based single crystal superalloy materials. Therefore, it is of much practical significance to accurately and efficiently predict creep life. Here, we develop a divide-and-conquer self-adaptive (DCSA) learning method incorporating multiple material descriptors for rational and accelerated prediction of the creep rupture life. We characterize a high-quality creep dataset of 266 alloy samples with such features as alloy composition, test temperature, test stress, and heat treatment process. In addition, five microstructural parameters related to creep process, including stacking fault energy, lattice parameter, mole fraction of the γ' phase, diffusion coefficient and shear modulus, are calculated and introduced by the CALPHAD (CALculation of PHAse Diagrams) method and basic materials structure-property relationships, that enables us to reveal the effect of microstructure on creep properties. The machine learning explorations conducted on the creep dataset demonstrate the potential of the approach to achieve higher prediction accuracy with RMSE, MAPE and R 2 of 0.3839, 0.0003 and 0.9176 than five alternative state-of-the-art machine learning models. On the newly collected 8 alloy samples, the error between the predicted creep life value and the experimental measured value is within the acceptable range (6.4486 h–40.7159 h), further confirming the validity of our DCSA model. Essentially, our method can establish accurate structure-property relationship mapping for the creep rupture life in a faster and cheaper manner than experiments and is expected to serve for inverse design of alloys. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• Re, Ta and W atoms are tend to occupy the Al sites in the ternary model alloy. • Mechanical property of interface increases the most when atoms occupy the Al sites. • The influence of Re, Ta and W on the creep strength is explored at electronic level. The strengthening effects of (0 0 1) Ni/Ni 3 Al interface alloying with Re, Ta and W in Ni-based single crystal superalloys were studied using first-principles density functional theory. By placing alloying elements on (0 0 1) Ni/Ni 3 Al interface, the substitution formation energy, the Griffith work, the generalized stacking fault energies, the partial density of states and the charge density difference were all calculated. It is figured out that Re, Ta and W atoms prefer to substitute Al positions of γ ' phase. It is consistent with the experimental results. The strengthening effect of Re atom on (0 0 1) Ni/Ni 3 Al interface is stronger than that of Ta and W atoms addition. The order of strengthening effects of Re, Ta and W atoms from our calculations is consistent with relevant experimental observations. By analyzing the charge density difference and the partial density of states, our results show that the hybridization between Re atom and the nearest neighbor Ni atoms is the strongest, which is conducive to explore the essential reason for the different strengthening effects of alloying atoms at electronic level. [ABSTRACT FROM AUTHOR]

Tie-line power exchanges among regional power systems facilitate renewable accommodations. Power exchanges can be calculated based on a tie-line security region that provides the feasible region of coupling parameters. However, a tie-line security region is a high-dimension polytope due to multiple time periods and border buses in power system operations, generally leading to a heavy computational burden. A fast calculation for high-dimension tie-line security regions is studied in this paper. The high-dimension polytope across all the time periods is decomposed as a Cartesian production of lower-dimension polytopes at each time period by leveraging dispatch levels of generations. For each lower-dimension polytope, the computational burden brought by multiple border buses is alleviated by aggregating tie-line power. Minimum renewable curtailments are preserved in the tie-line security region by adding an additional dimension, providing the information for renewable accommodations based on the tie-line security region. For coupling parameters within our tie-line security region, a feasible decision of the regional power system always exists. Finally, a decentralized and non-iterative framework is demonstrated to utilize our tie-line security region for renewable accommodations in an interconnected power system. The effectiveness of our methods is corroborated in the IEEE 9-bus system, a 661-bus utility system, and a five-region system. • Fast calculate tie-line security regions with multiple periods and border buses. • Guarantee the feasibility of tie-line security regions in a more flexible manner. • Preserve minimum renewable curtailments over tie-line security regions. • Coordinate interconnected power systems in a decentralized and non-iterative manner. [ABSTRACT FROM AUTHOR]

Partitioning and diffusion of transition metal solutes can significantly affect the coarsening rate of γ′ precipitates in Ni-based single crystal superalloys. Using first-principles density-functional calculations and atom probe tomography, we investigated the partitioning coefficient and diffusion rate of 3d (Ti Ni), 4d (Zr Pd), and 5d (Hf Pt) transition metal solutes. For ternary model Ni-based single crystal superalloys, the 3d solutes (except Ti) partition to the matrix phase, whereas 4d and 5d solutes (except Ru, Rh, and Ir) prefer γ′ precipitates. The existing atom probe tomography results are consistent with the calculation results. Across the periodic table, middle-row elements have a higher (lower) diffusion activation energy (diffusion rate) than early and late row elements. The band-filling effect that is coupled with electronic structure analysis can explain the parabolic behavior of energetics that involves bond breaking. To minimize the coarsening rate, we have screened alloying elements that both have a low diffusion rate in the γ matrix and partition to the γ′ phase. These alloying elements will suppress the coarsening behavior of the precipitates and are expected to contribute to the stability of precipitates at elevated temperature. [ABSTRACT FROM AUTHOR]

The effect of elemental interactions between solute elements on diffusion in Ni X Y (X, Y: Al, Ti, Nb, Ta, Cr, Co, Mo, W, Re, Ru) systems and γ′-coarsening in model Ni-based superalloys are investigated by performing first-principles calculations and high-temperature thermal exposure experiments. The ternary interactions are important for the diffusion barrier of a solute in Ni X Y and the coarsening rate of precipitates. These results are related not only to the electronic structure but also to chemical bonding. [ABSTRACT FROM AUTHOR]

A comprehensive method is presented in this work to locate faults in distribution systems with distributed generators (DGs). A two-level model is developed for this purpose with both telecommunication and telemetering data employed, so as to make good use of fused information for attaining a more credible optimization solution under scenarios with alarm distortions of feeder terminal units (FTUs) or loss during communication. First, at the upper level, an analytic model is developed to search all potential faulted sections/candidates based on the telecommunication data. Then, on the lower level, a model is presented using the telemetering data to identify the most likely fault location from the candidates provided by the upper model. The essential features of the two-level diagnosis model are demonstrated through a number of case studies. Simulation results have shown that the proposed approach is capable of not only locating the faulted section(s) in a distribution system with DGs but also identifying false and/or missing alarms. [ABSTRACT FROM AUTHOR]