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Magnesium (Mg)-based amorphous alloys hold significant potential for applications in the automotive, aerospace, and biomedical industries. However, they are limited by their smaller size compared to other amorphous alloys. A higher reduced glass transition temperature (Trg) is associated with larger sizes in Mg-based amorphous alloys. Yet, due to the vast chemical space involved, designing Mg-based amorphous alloys with higher Trg using traditional ‘trial and error’ method is a challenging endeavor. In this work, we developed a property-driven active learning strategy to customize Mg-based amorphous alloys with enhanced Trg. After just two iterations, we successfully tailored four amorphous alloys with high Trg values. Under identical experimental conditions, two of these alloys exhibited Trg values surpassing that of Mg0·65Ag0·19Cu0.06Gd0.1, the alloy with best Trg value in the reported references. SHAP analysis revealed that Trg tends to be higher when the Ag atomic ratio exceeds 0.045, the Cu atomic ratio is below 0.18, the Ni atomic ratio is below 0.025, and the Mg atomic ratio is below 0.665. Our work offers a reliable strategy for designing Mg-based amorphous alloys with higher Trg and provides valuable insights for the rational design of these alloys.

ABSTRACTUrban traffic anomaly diagnosis is crucial for urban road management and smart city construction. Most existing methods perform anomaly detection from a data-driven perspective and ignore the unique spatiotemporal characteristics of traffic anomalies, resulting in reduced accuracy or incorrect extraction of anomalies. In this study, we integrate geographic methods with high-dimensional tensor theory and develop a comprehensive framework for traffic anomaly diagnosis. The framework is divided into three parts: road traffic status acquisition, traffic anomaly detection, and urban anomaly analysis. First, we perform filtering and map matching on the trajectory data to obtain the traffic states of urban roads and construct them into a tensor. For traffic anomaly detection, a novel Spatio-Temporal constrained Low-Rank Sparse Tensor (ST-LRST) method is constructed to decompose urban traffic data into normal and anomalous components. Specifically, ST-LRST introduces a truncated nuclear norm, as a tighter nonconvex alternative to the tensor low-rank function, to more accurately estimate daily traffic patterns, thus improving the extraction accuracy of the sparse anomaly tensor. The spatiotemporal features of traffic anomalies are embedded into the sparse tensor to reduce the fragmentation and error rate of urban anomaly identification. To analyze anomalies, we study the characteristics of the anomalies detected by ST-LRST, such as duration, impact scope, and impact intensity. Subsequently, combined with crowdsourced geographic information, we comprehensively analyze the anomalous spatiotemporal variations in the road network and explain the factors that induce these urban anomalies. Experiments are performed using traffic data from Xi’an, China, and the results indicate that ST-LRST achieves the most accurate and comprehensive diagnosis of urban anomalies.

Although existing research on road intersection detection has been widely conducted using sensor data, mapping grade-separated road intersections in three-dimensions is still lacking. In this study, we propose a novel strategy to obtain detailed three-dimensional structures on grade-separated road intersections at turning levels using crowdsourced trajectories. Using the preprocessed trajectories, we identified grade-separated road intersections and obtained their boundary information based on the designed ECPC (Elevation Changing Points Clustering) algorithm. The three-dimensional structures of these identified grade-separated road intersections, e.g., centerline and vertical order of road segments at turning level, and positions of on-ramp and off-ramp, were extracted through trajectory data incremental integration and elevation trend analysis. We applied the tracking data collected by crowds in Beijing and Shanghai to estimate the effectiveness of the proposed method. The experimental results showed that the average precision, recall, and F1-score of grade-separated road intersection identification in the cities of Beijing and Shanghai were (83.31%, 80.67%, 81.97%) and (75%, 71.43%, and 73.17%), respectively. The accuracy of road centerline, slopes, vertical order of road segments, and positions of on-ramp and off-ramp in Beijing and Shanghai were (95%, 95.9%, 96.7%, 89.6%) and (94%, 87.3%, 87.1%, 87.1%), respectively.

Single-port robotic technique combines the benefits of minimally invasive outcome, improved visualization and fine surgical movement. This case report describes a 33-year-old patient with bilateral ovarian cysts suspected of teratoma who received single-port robotic trans-umbilical endoscopic bilateral cystectomy. This case shows that single-port robotic-assisted laparoscopic surgery is feasible and could be safe using the Shurui® system in select patients.

Abstract Superplastic metals that exhibit exceptional ductility (>300%) are appealing for use in high‐quality engineering components with complex shapes. However, the wide application of most superplastic alloys has been constrained due to their poor strength, the relatively long superplastic deformation period, and the complex and high‐cost grain refinement processes. Here these issues are addressed by the coarse‐grained superplasticity of high‐strength lightweight medium entropy alloy (Ti43.3V28Zr14Nb14Mo0.7, at.%) with a microstructure of ultrafine particles embedded in the body‐centered‐cubic matrix. The results demonstrate that the alloy reached a high coarse‐grained superplasticity greater than ≈440% at a high strain rate of 10−2 s−1 at 1173 K and with a gigapascal residual strength. A consecutively triggered deformation mechanism that sequences of dislocation slip, dynamic recrystallization, and grain boundary sliding in such alloy differs from conventional grain‐boundary sliding in fine‐grained materials. The present results open a pathway for highly efficient superplastic forming, broaden superplastic materials to the high‐strength field, and guide the development of new alloys.

E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original system, the first enhancement is a disordered speech classification module; this module is driven by the MFCC-CNN model, which aims to distinguish disordered speech and nondisordered speech. The accuracy of the classification is about 83%. The second enhancement is a grapheme-to-phoneme converter. This converter is based on the transformer model and designed for teachers to better generate IPA words from the regular written text. Compared with other G2P models, our transformer-based G2P model provides outstanding PER and WER performance. The last part of this paper focuses on a Tacotron2-based IPA-to-speech synthesis system, this deep learning-based TTS system can help teacher generate high-quality speech sounds from IPA characters which significantly improve the functionality of our original system. All of these three enhancements are related to the phonetic transcription process. and this work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.

Hydroelectricity is a major source of renewable electricity originating from a turbine driven by dammed large-volume water via a penstock or a drop shaft. Shafts suffer from risks of collapse due to the pressure from exterior structures and the erosion from inner water flow. Vertical shafts are an important part of hydroelectric power generation systems, and detecting defects in shafts guarantees the stable operation of hydropower stations. However, shaft defect detection is a great challenge due to the poor conditions, large drop in height, limited entrance size, lack of light, and damp air, where suitable technology and equipment are not available. Aiming at defect detection for vertical shafts, we have developed a defect-detecting system based on unmanned airships, integrated panoramic CCD cameras, three-dimensional laser scanners, inertial measurement units, barometric altimeters, illumination sensors, and control modules. Shaft defect detection methods are proposed by fusing the multimodal image features to extract typical defects on concrete surfaces. Compared with machine learning methods, the proposed method achieves the highest overall accuracy of 90.90% for defect detection. Our system was validated by experiments in the shafts of the Nuozhadu hydropower station to be functional for defect detection, which demonstrates its capability of reducing the risk of collapse and improving safety.

Abstract Protein stability is not only fundamental for experimental, industrial, and therapeutic applications, but is also the baseline for evolving novel protein functions. For decades, stability engineering armed with directed evolution has continued its rapid development and inevitably poses challenges. Generally, in directed evolution, establishing a reliable link between a genotype and any interpretable phenotype is more challenging than diversifying genetic libraries. Consequently, we set forth in a small picture to emphasize the screening or selection techniques in protein stability-directed evolution to secure the link. For a more systematic review, two main branches of these techniques, namely cellular or cell-free display and stability biosensors, are expounded with informative examples.

Influenced by both dry-wet cycles and salt weathering, the loess will exhibit significant changes in microstructure and permeability, which threatens the stability of loess slopes. Triaxial permeability tests and industrial computed tomography (CT) scans were carried out on saline intact loess with sodium sulfate. The relationship between permeability and pore structure of the loess after dry-wet cycles was discussed. Results show that the permeability coefficient of loess increases after dry-wet cycles, with the increment declining. After specified dry-wet cycles, the permeability coefficient increases approximately linearly with sodium sulfate content. However, the permeability coefficient significantly declines at higher confining pressures, while its attenuation rate decreases. An empirical relationship based on log101+e− log10k was proposed to estimate the permeability coefficient of saline intact loess considering dry-wet cycles and salt content. Comparisons of measured and calculated results proved its rationality. CT scan images imply the damage to soil microstructure induced by dry-wet cycles and salt weathering, corresponding to the decline of the mean CT value (ME) and the increase of both crack ratio and fractal dimension of crack network.

In this study, we investigate the urban landscape patterns in Wuhan, China based on the land use data in the vector format. Using the approach of landscape metric analysis, we calculate forty-four vector-based landscape metrics and then reduce redundant ones through a combination of Spearman correlation analysis and factor analysis, in order to extract a core set of characterizing landscape metrics. We find that the urban landscape can be depicted by six factors including the overall shape and diversity, mean proximity, overall area variation, fragmentation variation, elongation variation, and mean shape complexity. After analyzing typical patterns indicated by the core metrics and the spatial distribution of land use patterns, we compare our findings with other studies and discuss how the core metrics coincide and differ.

Roads are closely related to people’s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and information flow transfer, and a residual dilated convolution module was introduced to extract road network information at different scales. The experimental results showed that the method proposed in this study outperformed the compared algorithms in overall accuracy. This method had fewer false detections, and the extracted roads were closer to ground truth. Ablation experiments showed that the proposed modules could effectively improve road extraction accuracy.

The acquisition of human trajectories facilitates movement data analytics and location-based services, but gaps in trajectories limit the extent in which many tracking datasets can be utilized. We present a model to estimate place visit probabilities at time points within a gap, based on empirical mobility patterns derived from past trajectories. Different from previous models, our model makes use of prior information from historical data to build a chain of empirically biased random walks. Therefore, it is applicable to gaps of varied lengths and can be fitted to empirical data conveniently. In this model, long gaps are broken into a chain of multiple episodes according to past patterns, while short episodes are estimated with anisotropic location transition probabilities. Experiments show that our model is able to hit almost 60% of the ground truth for short gaps of several minutes and over 40% for longer gaps up to weeks. In comparison, existing models are only able to hit less than 10% and 1% for short and long gaps, respectively. Visit probability distributions estimated by the model are useful for generating paths in data gaps, and have potential for disaggregated movement data analysis in uncertain environments.

Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis−Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

GL-V9 is a prominent derivative of wogonin with a wide therapeutic spectrum and potent anti-tumor activity. The metabolism characteristics of GL-V9 remain unclear. This study aimed to clarify the metabolic pathway of GL-V9 and investigate the generation of its glucuronidation metabolites in vitro and in vivo. HPLC-UV-TripleTOF was used to identify metabolites. The main metabolite that we found was chemically synthesized and the synthetic metabolite was utilized as standard substance for the subsequent metabolism studies of GL-V9, including enzyme kinetics in liver microsomes of five different species and reaction phenotyping metabolism using 12 recombinant human UDP-glucuronosyltransferase (UGT) isoforms. Results indicated that the glucuronidation reaction occurred at C5-OH group, and 5-O-glucuronide GL-V9 is the only glucuronide metabolite and major phase II metabolite of GL-V9. Among 12 recombinant human UGTs, rUGT1A9 showed the strongest catalytic capacity for the glucuronidation reaction of GL-V9. rUGT1A7 and rUGT1A8 were also involved in the glucuronidation metabolism. Km of rUGT1A7-1A9 was 3.25 ± 0.29, 13.92 ± 1.05, and 4.72 ± 0.28 μM, respectively. In conclusion, 5-O-glucuronide GL-V9 is the dominant phase II metabolite of GL-V9 in vivo and in vitro, whose formation rate and efficiency are closely related to isoform-specific metabolism profiles and the distribution of UGTs in different tissues of different species.

The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction.

With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis in Wuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy.

Background Cryptococcal meningitis (CM) is a severe central nervous system infection. In patients with HIV infections and coexisting CM, elevated baseline cerebrospinal fluid (CSF) lactate levels can predict increased mortality. However, the CSF lactate level's significance in patients with CM who are HIV negative remains unclear, necessitating further investigation to elucidate the potential distinctions and enhance patient management. This study investigated the significance of CSF lactate levels in patients with CM who were HIV negative. Methods This retrospective study utilized data from the clinical databases of patients who underwent lumbar punctures at a medical center in Kaohsiung City, southern Taiwan. Demographic data, CSF lactate levels, routine CSF analyses, and hematologic and neurologic findings were evaluated. The optimal CSF lactate threshold value was determined by the Youden index. Results This retrospective study included 70 patients with CM, among whom 44 (63%) and 26 (37%) tested negative and positive for HIV, respectively. The group without HIV exhibited higher CSF lactate levels, with an optimal CSF lactate cutoff point of 7.935 mmol/L for predicting 90-day mortality, resulting in significant predictive accuracies (area under the curve, 0.755; sensitivity, 57.1%; specificity, 100%); this value was an independent mortality predictor in patients who were HIV negative. In patients with CM who were HIV negative, CSF lactate levels ≥7.935 mmol/L correlated with higher mortality rates but without statistical significance. All patients with CM who were HIV negative and had CSF lactate levels ≥7.935 mmol/L died within 3 months of admission. Conclusions Patients with CM who were HIV negative had elevated CSF lactate levels that correlated with adverse outcomes, enabling early identification of high-risk individuals. [ABSTRACT FROM AUTHOR]

The goal of this study was to clarify the protective role of the Wnt/[beta]-catenin pathway agonist SKL2001 in a rat model of Caerulein-induced acute pancreatitis. AR42J cells and rats were divided into 4 groups: control, Caerulein, SKL2001+ Caerulein, and SKL2001 + control. Cell apoptosis was examined using flow cytometry. Hematoxylin-eosin staining was performed to observe pathological changes in pancreatic and small intestinal tissues. Inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA), while genes related to the Wnt/[beta]-catenin pathway were quantified using quantitative real-time PCR. In vitro results showed that Caerulein promoted cell necrosis, inhibited the Wnt/[beta]-catenin pathway, and increased the level of inflammatory cytokines. However, SKL2001 reduced cell necrosis and inflammatory cytokines and activated the Wnt/[beta]catenin pathway. Additionally, in vivo results demonstrated the accumulation of fluid (i.e., edema), hemorrhage, inflammation and necrosis of the pancreatic acini occurred 6 h after the final Caerulein induction, with the damage reaching a maximal level 12 h after the final Caerulein induction; meanwhile, the Wnt/[beta]-catenin pathway was evidently inhibited with an enhanced level of inflammatory cytokines. The aforementioned damage was further aggravated 12 h later. Nevertheless, the pancreatic and small intestinal tissue damages were alleviated in Caerulein-induced rats treated with SKL2001. In conclusion, activation of the Wnt/[beta]-catenin pathway could inhibit Caerulein-induced cell apoptosis and inflammatory cytokine release, thus improving pancreatic and intestinal damage in rats with acute pancreatitis.Key words: acute pancreatitis, Wnt/[beta]-catenin pathway, Caerulein, SKL2001, AR42J cells.Cette etude avait pour but de clarifier le role protecteur du SKL2001, un agoniste de la voie de signalisation Wnt/[beta]catenine, dans la pancreatite aigue engendree par la caeruleine. Des cellules AR42J et des rats ont ete repartis dans les groupes suivants : temoin, caeruleine, SKL2001 + caeruleine et SKL2001 + temoin. Nous avons etabli le degre d'apoptose cellulaire a l'aide de la cytometrie en flux. Nous avons observe les modifications pathologiques des tissus du pancreas et du petit intestin a l'aide de la coloration a l'hematoxyline-eosine. Nous avons etabli la presence de cytokines inflammatoires a l'aide de la technique d'ELISA (pour << enzyme-linked immunosorbent assay >>) et des genes lies a la voie de signalisation Wnt/[beta]-catenine a l'aide de la technique de PCR quantitative en temps reel. Les resultats in vitro ont montre que la caeruleine favorisait la necrose des cellules avec une inhibition de la voie de signalisation Wnt/[beta]-catenine et une hausse des cytokines inflammatoires. Cependant, le SKL2001 entrainait une baisse de la necrose cellulaire et des cytokines inflammatoires avec une activation de la voie de signalisation Wnt/[beta]-catenine. De plus, les resultats in vivo ont permis d'observer 6 h apres la stimulation par la caeruleine une accumulation de liquide ou de l'aedeme, des hemorragies, de l'inflammation et de la necrose dans les acini pancreatiques jusqu'a un maximum 12 h apres la stimulation finale par la caeruleine; entre temps, la voie de signalisation Wnt/[beta]-catenine etait manifestement inhibee, avec une intensification des cytokines inflammatoires, et les dommages precedents s'aggravaient davantage 12 h plus tard. Neanmoins, les dommages aux tissus pancreatiques et du petit intestin s'attenuaient avec le SKL2001 chez les rats soumis a la caeruleine. En conclusion, l'activation de la voie de signalisation Wnt/[beta]-catenine pourrait inhiber l'apoptose cellulaire engendree par la caeruleine, ainsi que la liberation de cytokines inflammatoires, et donc permettre d'attenuer les dommages pancreatiques et intestinaux chez les rats atteints de pancreatite aigue. [Traduit par la Redaction]Mots-cles : pancreatite aigue, voie de signalisation Wnt/[beta]-catenine, caeruleine, SKL2001, cellules AR42J., IntroductionThe global incidence of acute pancreatitis (AP), a common gastrointestinal disease requiring hospitalization, has increased steadily in recent decades (Sun et al. 2017). Although AP is generally considered a mild [...]

[Objectives] To investigate the mechanisms and pharmacologic effects of Citri Reticulatae Pericarpium against keloids by network pharmacology systematically. [Methods] TCMSP, Uniprot and BATMAN-TCM databases were used to obtain the active constituents and targets of Citri Reticulatae Pericarpium. "Keloid" was used as key word to search for related therapeutic targets from Drug Bank, OMIM, TTD, and GEO databases. The Chinese medicine compound-target network was constructed by Cytoscape software. Besides, gene ontology (GO) and Kyoto Encyclopedia of genes and genome enrichment analysis were also performed. Afterward, Discovery Studio software was used to assess the interaction of key components and genes. [Results] Five active components of Citri Reticulatae Pericarpium, 773 compound targets and 676 keloid treatment targets were obtained in the databases. After the intersection, there are 47 targets of Citri Reticulatae Pericarpium for treating keloids. Hub genes were identified such as MMP9, IL6, TNF, TP53, and VEGFA, which were enriched in tumor necrosis factor-α, nuclear factor kappa-B, and other signaling pathways. The molecular docking stimulation confirmed the interaction between the MMP9 and three components of Citri Reticulatae Pericarpium. [Conclusions] Citri Reticulatae Pericarpium may play an important role in treating keloids through modulating genes and signaling pathways. The present study sheds light on the mechanisms of active compounds of Citri Reticulatae Pericarpium for the treatment of keloids. [ABSTRACT FROM AUTHOR]