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To carry out digital and intelligent research on structural safety assessment methods for rail transport infrastructure, which has an essential impact on the maintenance and management of rail transport infrastructure. This study selects 22 risk indicators, which are then used in a comprehensive evaluation of the operational context of the structure, which aims to develop a holistic risk assessment index system encompassing the interactions between structure, vehicle, and environment. Using a dynamic weight model, we compute node weights and construct a weighted Bayesian network model. This approach addresses the limitations of the conditional independence assumption typical in standard Bayesian network models., thereby addressing the restrictions associated with the conditional independence assumption inherent in the Bayesian network model. The utilization of trapezoidal fuzzy numbers and the maximum noise algorithm to compute network parameters, as well as in the determination of the safety probability of a given structure and the identification of the most probable risk factors associated with it. The findings reveal that the security level of this structure, as determined by a standard Bayesian network, is classified as level I. In contrast, the Level II security status obtained from the weighted Bayesian network corroborates with the fuzzy analytic hierarchy method's results, indicating a significant enhancement in inference accuracy with the weighted approach. [ABSTRACT FROM AUTHOR]

Summary: The article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: A monolithic column‐based mass spectrometry (MS) analysis kit was prepared for whole blood analysis with MS. The kit is disposable and can be used for purification, storage, transportation and direct analysis of whole blood. The kit mainly consists of a capillary for quantitative microsampling, a cation exchange monolithic column for purification and storage, and a syringe for loading sample. This kit is very friendly to various users that one can easily siphon the blood in the kit followed by rapid clean‐up. We established a quantitative method using the kit with a limit detection as low as 0.33 nmol/L, and achieved more than five orders of magnitude enhancement in sensitivity compared to direct nanoelectrospray ionization MS analysis. The column can avoid analyte exposure to environment, which helps the storage of the sample for laboratory analysis. The relative standard deviation of immediate blood analysis and storage blood analysis within 10 d was less than 10%. This method has been successfully applied to the quantitative analysis of procainamide hydrochloride in 2 μL rat blood. These results indicate that this disposable kit does have the potential to achieve highly sensitive quantitative MS analysis in biological samples, which is expected to become a cost‐effective and powerful tool for in vitro diagnostics. [ABSTRACT FROM AUTHOR]

Geothermal resources are rich in the Guangdong-Hong Kong-Macao Greater Bay Area, South China. The spatial distribution characteristics of geothermal anomalies can provide scientific supports for evaluating geothermal resources potential and decision-making on geothermal exploration and development in the Greater Bay area. Based on geographic information systems (GIS) technology, spatial data layers including terrestrial heat flow, granite distribution, fault, Bouguer gravity anomaly, aeromagnetic anomaly, and Curie depth were constructed, and their spatial correlations to geothermal anomalies were analyzed. The results show that geothermal anomalies are favorably located in the areas of heat flow value greater than 75 mW/m2, Yanshaian granite bourdary or interior, distance less than 3 km form a major fault, Bougue gravity anomaly gradient greater than 1. 6 mGal/km, aeromagnetic anomaly value of -5~5 nT, and Curie depth smaller than 15 km. The occurrence of strong geothermal anomalies is most closely related to Curie depth, Yanshanian granites and major faults. On the basis of the constructed data layers and the classification of their geothermal anomaly relevancies, two methods for predicting mineral resources, namely the weights of evidence (WoE) model and the weighted information value (WIV) model, were employed to integrate and analyze the multivariate spatial information for geothermal resources prospect. This study shows that lower mountainous or hilly areas with widespread distribution of Yanshanian granite and high rock thermal conductivity are prone to strong geothermal anomalies. The western Jiangmen, central Zhaoqing, northeastern Guangzhou, northeastern Huizhou, and Shenzhen are of these characteristics. They are considered as high-potential geothermal resource areas in the Greater Bay area. [ABSTRACT FROM AUTHOR]

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed. [ABSTRACT FROM AUTHOR]

Road construction will have a large impact on the ecosystem in the road area, and carbon stock, as an important indicator to measure the scale and quantity of primary productivity of the ecosystem, will also change, but the specific pattern is not clear. It is important to study the impact of road construction on carbon stock for regional ecosystem protection and sustainable economic and social development. Based on the InVEST model, this paper quantifies the spatial and temporal changes of carbon stocks in Jinhua, Zhejiang Province, from 2002 to 2017, using remote sensing image classification data land cover types as model driving data, geodetector, trend analysis, and buffer zone analysis methods, explores the driving effect of road construction on carbon stocks, and analyzes the spatial and temporal impacts of road construction on carbon stocks within the buffer zone. Results indicate that the total carbon stock in the Jinhua area showed a decreasing trend during the 16 years, decreasing by about 8.58 × 106 t. The spatial changes in the areas with higher carbon stocks were not significant. The explanatory power of road network density on carbon stock reaches 37%, and the anisotropic effect of road construction on carbon stock is strong and had a significant driving effect on carbon storage reduction. The new highway construction will accelerate the rate of carbon stock decline in the buffer zone, and the spatial situation is generally "the farther away from the highway, the higher the carbon stock." [ABSTRACT FROM AUTHOR]

To reveal the effect pattern of expressways on regional soil moisture, in this study, using trend analysis and buffer zone analysis methods, the data of VSWI (vegetation supply water index) in central Zhejiang Province from 2005 to 2016 were extracted from landsat7 satellite data using a single window algorithm, and spatial analysis was used to investigate the law of its differentiation. The results show that the multi-year average is 0.01879, between 0.01035–0.02774, showing a gentle decreasing trend, and there are obvious regional variations in space. We found that the impact of the new expressway and interchange on the VSWI in the buffer zone lasted for more than two years, and the VSWI increased in space farther away from the road, and this trend returned to normal at 8 km. Finally, the development patterns of the VSWI in the buffer zone of the newly established expressway and the interchange are approximately the same. [ABSTRACT FROM AUTHOR]

The in‐depth study of electrochemical (EC) synthesis can require a powerful mass spectrometry (MS) analytical platform which can discover and identify fleeting intermediates in EC reactions. Here we report a floating electrolytic electrospray ionization (FE‐ESI) strategy that can perform EC processes in a floating electrolytic cell and monitor intermediates by high‐resolution MS. Compared with previous EC‐MS methods, a significant advantage of FE‐ESI‐MS is that it allows one to modulate the electrolytic and electrospray process individually, ensuring its high sensitivity in discovering intermediates and universality to investigate redox reactions in different scenarios. This powerful platform has been successfully used to investigate the EC reductive coupling of p‐tolylboronic acid and p‐nitrotoluene. A series of nitrene intermediates were discovered and identified by FE‐ESI‐MS, indicating that a hidden mechanism involving nitrene formation might play a key role in EC reductive coupling process. [ABSTRACT FROM AUTHOR]

The in‐depth study of electrochemical (EC) synthesis can require a powerful mass spectrometry (MS) analytical platform which can discover and identify fleeting intermediates in EC reactions. Here we report a floating electrolytic electrospray ionization (FE‐ESI) strategy that can perform EC processes in a floating electrolytic cell and monitor intermediates by high‐resolution MS. Compared with previous EC‐MS methods, a significant advantage of FE‐ESI‐MS is that it allows one to modulate the electrolytic and electrospray process individually, ensuring its high sensitivity in discovering intermediates and universality to investigate redox reactions in different scenarios. This powerful platform has been successfully used to investigate the EC reductive coupling of p‐tolylboronic acid and p‐nitrotoluene. A series of nitrene intermediates were discovered and identified by FE‐ESI‐MS, indicating that a hidden mechanism involving nitrene formation might play a key role in EC reductive coupling process. [ABSTRACT FROM AUTHOR]

Background: This study aimed to assess the survival outcomes among patients with out-of-hospital cardiac arrest (CA) who received cardiopulmonary resuscitation (CPR) in China. Methods: Relevant studies, published between January 1, 2010 and September 5, 2022, were retrieved from databases, including EMBASE, PubMed, Cochrane Library, the China Biology Medicine disk, China National Knowledge Infrastructure, and Wanfang databases. We included clinical studies in which all patients were diagnosed with CA and underwent out-of-hospital CPR, and the outcome variables were at least one of the following: return of spontaneous circulation (ROSC), survival to admission, survival to hospital discharge, 1-month survival, achieved good neurological outcomes, and 1-year survival. Two investigators independently extracted the study data and assessed its quality using a modified Newcastle–Ottawa Scale tool. The data were pooled using random-effects models. Results: Of the 3620 identified studies, 49 (63,378 patients) were included in the meta-analysis. The pooled ROSC rate was 9.0% (95% confidence interval [CI] 7.5–10.5%, I2 = 97%), the pooled survival to admission rate was 5.0% (95% CI 2.7–8.0%, I2 = 98%), and the pooled survival to discharge rate was 1.8% (95% CI 1.2–2.5%, I2 = 95%). Additionally, the ROSC rate of patients with bystander CPR was significantly higher than that of those without bystander CPR, and the pooled odds ratio (OR) was 7.92 (95% CI 4.32–14.53, I2 = 85%). The ROSC rate of participants who started CPR within 5 min was significantly higher than that of those who started CPR after 5 min, and the pooled OR was 5.92 (95% CI 1.92–18.26, I2 = 85%). The ROSC rate of participants with defibrillation was significantly higher than that of those without defibrillation, and the pooled OR was 8.52 (95% CI 3.72–19.52, I2 = 77%). Conclusion: The survival outcomes of out-of-hospital CPR in China are far below the world average. Therefore, the policy of providing automated external defibrillators (AEDs) in public places and strengthening CPR training for healthcare providers and public personnel should be encouraged and disseminated nationwide. Trial registration This study was registered in PROSPERO (CRD42022326165) on 29 April 2022. [ABSTRACT FROM AUTHOR]

To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model. [ABSTRACT FROM AUTHOR]

With the continuous increase in motor vehicle ownership in recent times, traditional transportation has been unable to meet people's travel needs. Research on autonomous driving technology will help solve a series of problems associated with driving, such as traffic accidents, traffic congestion, energy consumption, and environmental pollution. In this paper, an improved artificial potential field method is proposed to complete the planning of automatic driving trajectories by adding the distance adjustment factor, dynamic road repulsive field, velocity repulsive field, and acceleration repulsive field. The invasive weed algorithm is introduced to solve the defects associated with the traditional artificial potential field method. The prediction model—for which corresponding constraint variables were set and an optimal objective function was established to build up the MPC model controller to achieve the goal of trajectory tracking—was linearized and discretized from a vehicle dynamics model. Finally, co-simulation based on MATLAB and CarSim was used to verify the practicability of the model. [ABSTRACT FROM AUTHOR]

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time. [ABSTRACT FROM AUTHOR]

In this study, the phase-structure and rheological response-behavior of multi-walled carbon nanotube (MWCNTs) modified asphalt-binder (MWCNTs-MA) were measured and quantified in the laboratory. The changes in the molecular dynamics due to MWCNTs modification were simulated and quantified based on the intermolecular interaction energy computations, electrostatic potential surface analyses and phase-structure modeling of the asphalt-binder matrix. The rheological properties such as the asphalt-binder viscosity and complex modulus, of both the base and modified asphalt-binders, were determined using the standard Brookfield viscometer (BV) and dynamic shear rheology (DSR) test devices, respectively. In comparison to the base asphalt-binder, the corresponding BV-DSR test results exhibited higher viscosity and complex modulus for the MWCNTs modified asphalt-binder, with reduced sensitivity and susceptibility to temperature variations. From the study results, it was observed that MWCNTs significantly improved the rheological properties and high-temperature performance of the asphalt-binder. Overall, the study has demonstrated that MWCNT modified asphalt-binder has great promising potential for application and usage as a road-pavement material, particularly with respect to mitigating the high temperature related distresses such as rutting. [ABSTRACT FROM AUTHOR]

Summary: Zero watermark has no intervention on the carrier image, which is invisible in nature, and completely solves the problem of mutual constraint between robustness and invisibility in the traditional digital watermark technology. In order to improve the robustness of the zero watermark, a zero watermark algorithm based on Gabor transform and discrete cosine transform (DCT) is proposed in this paper. The algorithm performs Gabor transformation on the carrier image to obtain the directional characteristics of the image. Then it performs DCT and blocks, and performs singular value decomposition (SVD) on each subblock. The feature matrix is constructed using the largest eigenvalue of the subblock, and then XORed with the encrypted watermark to form a zero watermark. The algorithm utilizes the advantages of Gabor transform in the adjustment of scale and direction and selects the optimized parameters to extract image features. At the same time, the information concentration and decorrelation of DCT and SVD are utilized to improve the robustness and antiattack capability of the algorithm. The experimental results show that the proposed algorithm is robust to filtering, JPEG compression, noise, shearing, rotation, and other attacks, especially effective in antishear attack and rotation attack. [ABSTRACT FROM AUTHOR]

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model's parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance. [ABSTRACT FROM AUTHOR]

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain. [ABSTRACT FROM AUTHOR]

Artificial neural network algorithm has strong nonlinear mapping function. BP neural network algorithm is the representative of artificial neural network algorithm. Due to the relatively small scale of small, medium, and micro enterprises and lack of mortgage assets, the biggest market risk they usually face is credit risk. In order to avoid huge economic losses for banks, the impact of risk analysis on credit decision-making is the key. Therefore, the establishment of a scientific and complete credit risk assessment system has become the primary task. This paper mainly uses BP neural network algorithm to analyze the risk of small and medium-sized enterprises and solve the problem of credit decision-making. First, analyze the various influencing factors that cause credit risk, and layer the factors according to the degree of risk; secondly, use the fuzzy comprehensive evaluation method in mathematical modeling to rank each factor; then, use the fuzzy analytic hierarchy process to calculate the weight of each indicator factor as calculated by establishing the risk value function, using the function expression of the annual interest rate and the total credit formula to calculate the total credit, which solves the problem of the loan line of each enterprise, and use the optimal linear programming to give priority to the selection of the line and the interest rate. Enterprises: finally, for enterprises that do not have a credit rating, reasonable adjustments are made to the quota by establishing lending qualification expression, taking into account the impact of unexpected external factors, establishing an improved annual interest rate function, and achieving an optimized credit strategy through the establishment and solution of the model. The goal of optimizing credit strategy has been achieved. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Twenty‐two new limonoids, mufolinoids A—V (1—22), including six rings A, B‐seco limonoids (1—6), twelve ring A‐seco limonoids (7—18), four ring‐intact limonoids (19—22), together with thirteen known compounds (23—35) were isolated from Munronia unifoliolata. Their structures including absolute configurations were elucidated by combination of NMR, HR‐MS, single‐crystal X‐ray diffraction and calculations of ECD and NMR technologies. Compounds 24, 25, 33, 34 could be significantly reversed the multidrug resistance of MCF‐7/doxorubicin (DOX) cells, and the reversal fold (RF) was much higher than that of positive drug Verapamil. Compounds 24, 28, and 29 exhibited significant anti‐inflammatory activity with the IC50 values in the range of 17.7—39.4 μmol/L. Furthermore, compound 29 could markedly inhibit the release of IL‐1β by inhibiting the initiation and assembly of NLRP3 inflammasome, which demonstrates the great potential of limonoids as an anti‐inflammatory agent. [ABSTRACT FROM AUTHOR]

At present, person reidentification based on attention mechanism has attracted many scholars' interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID. [ABSTRACT FROM AUTHOR]

Main observation and conclusion: Sarcaglarols A—D (1—4), two pairs of lindenane−monoterpene heterodimers fused by a 1,2‐dioxane moiety, were discovered and isolated from the leaves of Sarcandra glabra guided by MS/MS molecular networking‐based strategy. Their planar structures, absolute configurations of basic skeleton and flexible polyhydric side chain were established by analysis of HRESIMS, NMR spectroscopic data, ECD spectrum, and the X‐ray diffraction study of isopropylidene derivatives. An intermolecular [2+2+2] cycloaddition may play a key role in the biosynthesis pathway of the 1,2‐dioxane moiety fused lindenane−monoterpene heterodimer skeleton, which can be recognized as the biogenetic precursors of our previous reported lindenane−normonoterpene conjugates. In addition, compounds 1, 3 and 4 exhibited moderate inhibitory effects of lipid accumulation in free fatty acid‐exposed L02 cells. [ABSTRACT FROM AUTHOR]

In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self‐organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two‐dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering. [ABSTRACT FROM AUTHOR]

Disputes over Ovid's genuine attitudes towards Roman emperor Octavian have never ceased among Western scholars of classics. Never a motivated dissident, Ovid was unfortunately an adherent to the idea of artistic autonomy, which, against the backdrop of a unified Augustan cultural order, was interpreted by the imperial family as a political stance. In order to return to Rome or suffer less severely, the banished poet would have to appease the emperor with servile penitence, but Ovid was frequently impelled by his obstinacy as an artist and indignation at his unmerited torments to overlook his real-life needs and give his works an ironic twist. While in some poems, his venom of criticism is ingeniously dissimulated beyond easy detection, his defiance is alarmingly obvious in others. Although Octavian, Livia, and Tiberius usually appear as gods in Tristia and Ex Ponto, they are revealed, through various means of artistry, to be no more than ruthless rulers on earth whose self-imposed apotheosis is constantly ridiculed. [ABSTRACT FROM AUTHOR]

Glycogen synthase kinase-3 (GSK-3) dysregulation has been implicated in nigral dopaminergic neurodegeneration, one of the main pathological features of Parkinson's disease (PD). The two isoforms, GSK-3α and GSK-3β, have both been suggested to play a detrimental role in neuronal death. To date, several studies have focused on the role of GSK-3β on PD pathogenesis, while the role of GSK-3α has been largely overlooked. Here, we report in situ observations that both GSK-3α and GSK-3β are dephosphorylated at a negatively acting regulatory serine, indicating kinase activation, selectively in nigral dopaminergic neurons following exposure of mice to 1-methyl-4-pheny-1,2,3,6-tetrahydropyridine (MPTP). To identify whether GSK-3α and GSK-3β display functional redundancy in regulating parkinsonian dopaminergic cell death, we analysed dopaminergic neuron-specific Gsk3a null (Gsk3a Δ Dat ) and Gsk3b null (Gsk3b Δ Dat ) mice, respectively. We found that Gsk3b Δ Dat , but not Gsk3a Δ Dat , showed significant resistance to MPTP insult, revealing non-redundancy of GSK-3α and GSK-3β in PD pathogenesis. In addition, we tested the neuroprotective effect of tideglusib, the most clinically advanced inhibitor of GSK-3, in the MPTP model of PD. Administration of higher doses (200 mg/kg and 500 mg/kg) of tideglusib exhibited significant neuroprotection, whereas 50 mg/kg tideglusib failed to prevent dopaminergic neurodegeneration from MPTP toxicity. Administration of 200 mg/kg tideglusib improved motor symptoms of MPTP-treated mice. Together, these data demonstrate GSK-3β and not GSK-3α is critical for parkinsonian neurodegeneration. Our data support the view that GSK-3β acts as a potential therapeutic target in PD and tideglusib would be a candidate drug for PD neuroprotective therapy. [ABSTRACT FROM AUTHOR]

The effects of the warm forming temperature on the axial-pushed incremental rolling process were investigated through finite element analysis and experimental studies. Firstly, the principle of warm axial-pushed incremental rolling process of spline shaft was introduced. Next, the material properties of 42CrMo steel at different forming temperatures were studied to discuss the effects of the warm forming temperature. Through finite element analysis, the simulations of the axial-pushed incremental rolling process were carried out to investigate the effects of the warm forming temperature on rolling forces. The results indicated that the axial and radial forces on the rolling dies were both reduced at the warm forming temperature. Finally, the experiment studies were carried out on a warm axial-pushed incremental rolling equipment. The dimensional precision, microstructure, and hardness of the formed spline shafts at warm temperature were compared with those of the formed spline shafts at room temperature. The results indicated that the spline shafts, which were formed at the warm temperature, possesses of a good dimensional precision and excellent performance. The results in this paper demonstrated that the warm forming temperature has the positive effects on the performance improvement of the axial-pushed incremental rolling process of spline shaft. [ABSTRACT FROM AUTHOR]

According to the characteristics of news events, the clustering classification method is suitable such news events. However, traditional Single-Pass methods have two major drawbacks: low-level time efficiency and accuracy. In this paper, we focus on the clustering characteristics of news events, and propose a new method based on staging incremental Single-Pass. Firstly, the Vector Space Model (VSM) is used to represent the text vectors, and regression analysis is performed by using the Term Frequency and Inverse Documentation Frequency TFIDF weighting equation to establish the weighted calculation model. Meanwhile, the centroid vectors are used to represent news events models. Secondly, segmented lists are applied to news events models. Lastly, the cosine values of the angles are used to calculate the levels of similarity, and a similarity-based approach is used to realize the clustering of news events, and to selectively and dynamically update the segmented lists and centroid vectors so that the timeliness and accuracy of clustering could be significantly improved. Empirical experiments have demonstrated that the proposed method is effective, and could be used to improve clustering accuracy and reduce event complexity. [ABSTRACT FROM AUTHOR]

With the development of manufacturing industry, metal tubes and rods are used more and more widely in automobile, aviation, and aerospace fields. Rotary swaging, a competitive plastic-forming technology for manufacturing tubes and rods, can realize net-shape forming for high-quality workpiece. However, in order to control the forming precision of tube or bar workpiece, the complex mechanical system was needed in the conventional swaging machine. In this paper, a rotary swaging process utilizing energy-controlled method is proposed, which can improve the flexibility of the process and control forming diameter of the tubes by changing the forming energy. In the swaging machine, a servo motor and its control system were used to change the speed of the rotary swaging spindle. And then, the swaging energy of the hammer die can be controlled. Rotary swaging experiments of pure copper tubes were conducted on the developed rotary swaging machine. These tubes with outer diameter of 8.0 mm and wall thickness of 1.0 mm were formed to necking tubes with different outer diameters. Meanwhile, the relationship between the speed of the spindle and the forming diameter of the formed tube was obtained. The metallographs of the formed tubes showed the compact structure and continuous streamline. To investigate the metal behavior under different energy of the hammer die, Transvalor Forge 2011 was used to simulate the rotary swaging process of tubes based on energy-controlled swaging method. The metal flows in radial, axial, and circumference directions, emerging axial elongation and necking deformation. Comparing the experimental results with the simulated ones, the deformation condition and thickness distribution are in good agreement. [ABSTRACT FROM AUTHOR]

Hydraulic overpressure can induce fractures and increase permeability in a range of geological settings, including volcanological, glacial and petroleum reservoirs. Here we consider an example of induced hydraulic fracture stimulation in a tight-gas sandstone. Successful exploitation of tight-gas reservoirs requires fracture networks, either naturally occurring, or generated through hydraulic stimulation. The study of seismic anisotropy provides a means to infer properties of fracture networks, such as the dominant orientation of fracture sets and fracture compliances. Shear wave splitting from microseismic data acquired during hydraulic fracture stimulation allows us to not only estimate anisotropy and fracture properties, but also to monitor their evolution through time. Here, we analyse shear wave splitting using microseismic events recorded during a multistage hydraulic fracture stimulation in a tight-gas sandstone reservoir. A substantial rotation in the dominant fast polarization direction (ψ) is observed between the events of stage 1 and those from later stages. Although large changes in ψ have often been linked to stress-induced changes in crack orientation, here we argue that it can better be explained by a smaller fracture rotation coupled with an increase in the ratio of normal to tangential compliance (ZN/ZT) from 0.3 to 0.6. ZN/ZT is sensitive to elements of the internal architecture of the fracture, as well as fracture connectivity and permeability. Thus, monitoring ZN/ZT with shear wave splitting can potentially allow us to remotely detect changes in permeability caused by hydraulic stimulation in a range of geological settings. [ABSTRACT FROM AUTHOR]

Compared with the conventional single-roller spinning process, the double-roller clamping spinning(DRCS) process can effectively prevent the sheet metal surface wrinkling and improve the the production efficiency and the shape precision of final spun part. Based on ABAQUS/Explicit nonlinear finite element software, the finite element model of the multi-pass DRCS for the sheet metal is established, and the material model, the contact definition, the mesh generation, the loading trajectory and other key technical problems are solved. The simulations on the multi-pass DRCS of the ordinary Q235A steel cylindrical part with the arc-shaped surface flange are carried out. The effects of number of spinning passes on the production efficiency, the spinning moment, the shape error of the workpiece, and the wall thickness distribution of the final part are obtained. It is indicated definitely that with the increase of the number of spinning passes the geometrical precision of the spun part increases while the production efficiency reduces. Moreover, the variations of the spinning forces and the distributions of the stresses, strains, wall thickness during the multi-pass DRCS process are revealed. It is indicated that during the DRCS process the radical force is the largest, and the whole deformation area shows the tangential tensile strain and the radial compressive strain, while the thickness strain changes along the generatrix directions from the compressive strain on the outer edge of the flange to the tensile strain on the inner edge of the flange. Based on the G-CNC6135 NC lathe, the three-axis linkage computer-controlled experimental device for DRCS which is driven by the AC servo motor is developed. And then using the experimental device, the Q235A cylindrical parts with the arc-shape surface flange are formed by the DRCS. The simulation results of spun parts have good consistency with the experimental results, which verifies the feasibility of DRCS process and the reliability of the finite element model for DRCS. [ABSTRACT FROM AUTHOR]