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Abstract Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

Abstract High speed side-view videos of sliding drops enable researchers to investigate drop dynamics and surface properties. However, understanding the physics of sliding requires knowledge of the drop width. A front-view perspective of the drop is necessary. In particular, the drop’s width is a crucial parameter owing to its association with the friction force. Incorporating extra cameras or mirrors to monitor changes in the width of drops from a front-view perspective is cumbersome and limits the viewing area. This limitation impedes a comprehensive analysis of sliding drops, especially when they interact with surface defects. Our study explores the use of various regression and multivariate sequence analysis (MSA) models to estimate the drop width at a solid surface solely from side-view videos. This approach eliminates the need to incorporate additional equipment into the experimental setup. In addition, it ensures an unlimited viewing area of sliding drops. The Long Short Term Memory (LSTM) model with a 20 sliding window size has the best performance with the lowest root mean square error (RMSE) of 67 µm. Within the spectrum of drop widths in our dataset, ranging from 1.6 to 4.4 mm, this RMSE indicates that we can predict the width of sliding drops with an error of 2.4%. Furthermore, the applied LSTM model provides a drop width across the whole sliding length of 5 cm, previously unattainable.

Accurate forecasting of the coronavirus disease 2019 (COVID-19) spread is indispensable for effective public health planning and the allocation of healthcare resources at all levels of governance, both nationally and globally. Conventional prediction models for the COVID-19 pandemic often fall short in precision, due to their reliance on homogeneous time-dependent transmission rates and the oversight of geographical features when isolating study regions. To address these limitations and advance the predictive capabilities of COVID-19 spread models, it is imperative to refine model parameters in accordance with evolving insights into the disease trajectory, transmission rates, and the myriad economic and social factors influencing infection. This research introduces a novel hybrid model that combines classic epidemic equations with a recurrent neural network (RNN) to predict the spread of the COVID-19 pandemic. The proposed model integrates time-dependent features, namely the numbers of individuals classified as susceptible, infectious, recovered, and deceased (SIRD), and incorporates human mobility from neighboring regions as a crucial spatial feature. The study formulates a discrete-time function within the infection component of the SIRD model, ensuring real-time applicability while mitigating overfitting and enhancing overall efficiency compared to various existing models. Validation of the proposed model was conducted using a publicly available COVID-19 dataset sourced from Italy. Experimental results demonstrate the model’s exceptional performance, surpassing existing spatiotemporal models in three-day ahead forecasting. This research not only contributes to the field of epidemic modeling but also provides a robust tool for policymakers and healthcare professionals to make informed decisions in managing and mitigating the impact of the COVID-19 pandemic.

In higher plants, the regulatory roles of cAMP (cyclic adenosine 3′,5′-monophosphate) signaling remain elusive until now. Cellular cAMP levels are generally much lower in higher plants than in animals and transiently elevated for triggering downstream signaling events. Moreover, plant adenylate cyclase (AC) activities are found in different moonlighting multifunctional proteins, which may pose additional complications in distinguishing a specific signaling role for cAMP. Here, we have developed rapeseed (Brassica napus L.) transgenic plants that overexpress an inducible plant-origin AC activity for generating high AC levels much like that in animal cells, which served the genetic model disturbing native cAMP signaling as a whole in plants. We found that overexpression of the soluble AC activity had significant impacts on the contents of indole-3-acetic acid (IAA) and stress phytohormones, i.e. jasmonic acid (JA), abscisic acid (ABA), and salicylic acid (SA) in the transgenic plants. Acute induction of the AC activity caused IAA overaccumulation, and upregulation of TAA1 and CYP83B1 in the IAA biosynthesis pathways, but also simultaneously the hyper-induction of PR4 and KIN2 expression indicating activation of JA and ABA signaling pathways. We observed typical overgrowth phenotypes related to IAA excess in the transgenic plants, including significant increases in plant height, internode length, width of leaf blade, petiole length, root length, and fresh shoot biomass, as well as the precocious seed development, as compared to wild-type plants. In addition, we identified a set of 1465 cAMP-responsive genes (CRGs), which are most significantly enriched in plant hormone signal transduction pathway, and function mainly in relevance to hormonal, abiotic and biotic stress responses, as well as growth and development. Collectively, our results support that cAMP elevation impacts phytohormone homeostasis and signaling, and modulates plant growth and development. We proposed that cAMP signaling may be critical in configuring the coordinated regulation of growth and development in higher plants.

Abstract The incidence of hyponatremia after spinal cord injury was reported to be between 25 and 80%. Hyponatremia can lead to a variety of clinical symptoms, from mild to severe and even life-threatening. Hyponatremia is often associated with diabetes insipidus, which refers to insufficient arginine vasopressin (AVP) secretion or defective renal response to AVP, with clinical manifestations of syndromes such as hypoosmolality, polydipsia, and polydipsia. Recent mechanistic studies on hyponatremia and diabetes insipidus after acute spinal cord injury have been performed in isolation, without integrating the above two symptoms into different pathological manifestations that occur in the same injury state and without considering the acute spinal cord injury patient’s condition as a whole. The therapeutic principles of CSWS and SIADH are in opposition to one another. It is not easy to identify the mechanism of hyponatremia in clinical practice, which makes selecting the treatment difficult. According to the existing theories, treatments for hyponatremia and diabetes insipidus together are contraindicated, whether the mechanism of hyponatremia is thought to be CSWS or SIADH. In this paper, we review the mechanism of these two pathological manifestations and suggest that our current understanding of the mechanisms of hyponatremia and diabetes insipidus after high acute cervical SCI is insufficient, and it is likely that there are other undetected pathogenetic mechanisms.

To study the evolution law of water and mud inrush disasters in the fractured zones of water-rich faults in deep buried tunnels, a self-developed 3D physical model test system was used to conduct experimental research about the evolution process. Additionally, MIDAS GTS NX 2022 version was used to analyze the evolution laws of displacement, stress, pore water pressure, and seepage flow velocity during the excavation process. The findings indicate that in the model testing, tunnel excavation caused different changes in the stress magnitude of the surrounding rock at different positions. The pore water pressure increases correspondingly with the loading water pressure at the same location. The function relationship between the relative water pressure coefficient of any point in the outburst-prevention rock mass, and the vertical distance from that point to the upper boundary of the fault, was obtained through nonlinear fitting. In numerical simulation, excavation affects the vertical displacement of the arch vault more than the arch ring, while it has a greater impact on the horizontal displacement of the arch ring compared to the arch vault. The maximum and minimum principal stresses show significant changes; the pore water pressure at each monitoring point decreases with the increase in excavation distance. The flow velocity of seepage shows a trend of first increasing and then decreasing. The research results can provide relevant references for the prevention of water and mud inrush disasters in fault areas.

This paper proposes transforming actual monitoring data into risk quantities and establishing a Long Short-Term Memory (LSTM) safety risk warning model for predicting the deformation of super-large and ultra-deep foundation pits in river–round gravel strata based on safety evaluation methods. Using this model, short-term deformation predictions at various monitoring points of the foundation pits are made and compared with monitoring data. The results from the LSTM safety risk warning model indicate an absolute error range between the predicted deformation values and on-site monitoring values of −0.24 to 0.16 mm, demonstrating the model’s accuracy in predicting pit deformation. Additionally, calculations reveal that both the overall risk level based on on-site monitoring data and the overall safety risk level based on predicted data are classified as level four. The acceptance criteria for the overall risk level of the foundation pit are defined as “unacceptable and requiring decision-making”, with the risk warning control scheme being “requiring decision-making, formulation of control, and warning measures”. These research findings offer valuable insights for predicting and warning about safety risks in similar foundation pit engineering projects.

Coverage-guided fuzzing has been widely applied in software error and security vulnerability detection. The fuzzing technique based on AFL (American Fuzzy Loop) is a common coverage-guided fuzzing method. The code coverage during AFL fuzzing is highly dependent on the quality of the initial seeds. If the selected seeds’ quality is poor, the AFL may not be able to detect program paths in a targeted manner, resulting in wasted time and computational resources. To solve the problems that the seed selection strategy in traditional AFL fuzzing cannot quickly and effectively generate high-quality seed sets and the mutated test cases cannot reach deeper paths and trigger security vulnerabilities, this paper proposes an attention mechanism-based generative adversarial network (GAN) seed generation approach for vulnerability mining, which can learn the characteristics and distribution of high-quality test samples during the testing process and generate high-quality seeds for fuzzing. The proposed method improves the GAN by introducing fully connected neural networks to balance the competitive adversarial process between discriminators and generators and incorporating attention mechanisms, greatly improving the quality of generated seeds. Our experimental results show that the seeds generated by the proposed method have significant improvements in coverage, triggering unique crashes and other indicators and improving the efficiency of AFL fuzzing.

The q-Rung Orthopair Fuzzy Soft Set (q-ROFSS) theory is a significant extension of Pythagorean fuzzy soft set and intuitionistic fuzzy soft set theories for dealing with the imprecision and uncertainty in data. The purpose of this study is to improve and apply this theory in decision-making. To achieve this purpose, we firstly propose some Bonferroni Mean (BM) and Weighted Bonferroni Mean (WBM) aggregation operators for aggregating the data. Some desired properties are presented in detail and the existing aggregation operators are used as distinct cases of our proposed operators. Further, a decision-making analysis is presented based on our proposed operations and applied to decision-making in COVID-19 diagnosis. The preferred way is discussed to protect maximum human lives from COVID-19. A numerical example is given to support the claim. The experimental results demonstrate the proposed operators have an ability to make a precise decision with imprecision and uncertain information which will find a broad application in the decision-making area.

The classic molecular dynamics (MD) simulation approach has been used to investigate the microstructure change in polycrystalline magnesium (Mg) during compressive deformation at various temperatures. At low temperatures, there exists a competition between the sliding of Shockley partial dislocation (SPD) and perfect dislocation. Abundant dislocation behaviors such as dislocation bundle and double cross slipping are observed. With a temperature increase, the dislocation sliding is hindered by the newly formed grain boundaries (GBs). The grain reorientation should be the compensatory mechanism for plastic deformation at high temperatures. Furthermore, dynamic recrystallization (DRX) is found at the highest temperature investigated. For all the temperature cases studied, twinning is unsensitive against applied compressive stress. The results of this work may help to understand the temperature effect on the mechanism in polycrystalline Mg under compressive deformation.

BackgroundInfectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction.MethodsWe extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).ResultsDuration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper.ConclusionsThe experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases.

Breast cancer is a leading cause of death in women worldwide, and early detection is crucial for successful treatment. Computer-aided diagnosis (CAD) systems have been developed to assist doctors in identifying breast cancer on ultrasound images. In this paper, we propose a novel fuzzy relative-position-coding (FRPC) Transformer to classify breast ultrasound (BUS) images for breast cancer diagnosis. The proposed FRPC Transformer utilizes the self-attention mechanism of Transformer networks combined with fuzzy relative-position-coding to capture global and local features of the BUS images. The performance of the proposed method is evaluated on one benchmark dataset and compared with those obtained by existing Transformer approaches using various metrics. The experimental outcomes distinctly establish the superiority of the proposed method in achieving elevated levels of accuracy, sensitivity, specificity, and F1 score (all at 90.52%), as well as a heightened area under the receiver operating characteristic (ROC) curve (0.91), surpassing those attained by the original Transformer model (at 89.54%, 89.54%, 89.54%, and 0.89, respectively). Overall, the proposed FRPC Transformer is a promising approach for breast cancer diagnosis. It has potential applications in clinical practice and can contribute to the early detection of breast cancer.

Multiple deformed substructures including dislocation cells, nanotwins (NTs) and martensite were introduced in super austenitic stainless steels (SASSs) by cryogenic rolling (Cryo-R, 77 K/22.1 mJ·m−2). With the reduction increasing, a low stacking fault energy (SFE) and increased flow stress led to the activation of secondary slip and the occurrence of NTs and martensite nano-laths, while only dislocation tangles were observed under a heavy reduction by cold-rolling (Cold-R, 293 K/49.2 mJ·m−2). The multiple precursors not only possess variable deformation stored energy, but also experience competition between recrystallization and reverse transformation during subsequent annealing, thus contributing to the formation of a heterogeneous structure (HS). The HS, which consists of bimodal-grained austenite and retained martensite simultaneously, showed a higher yield strength (~1032 MPa) and a larger tensile elongation (~9.1%) than the annealed coarse-grained Cold-R sample. The superior strength–ductility and strain hardening originate from the synergistic effects of grain refinement, dislocation and hetero-deformation-induced hardening.

The aim of this paper is to investigate different definitions of soft points in the existing literature on soft set theory and its extensions in different directions. Then limitations of these definitions are illustrated with the help of examples. Moreover, the definition of soft point in the setup of fuzzy soft set, intervalvalued fuzzy soft set, hesitant fuzzy soft set and intuitionistic soft set are also discussed. We also suggest an approach to unify the definitions of soft point which is more applicable than the existing notions.

ObjectiveWe aim to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of follicular thyroid carcinoma (FTC) on thyroid ultrasonography.MethodsA dataset of 1159 images, consisting of 351 images from 138 FTC patients and 808 images from 274 benign follicular-pattern nodule patients, was divided into a balanced and unbalanced dataset, and used to train and test the CAD system based on a transfer learning of a residual network. Six radiologists participated in the experiments to verify whether and how much the proposed CAD system helps to improve their performance.ResultsOn the balanced dataset, the CAD system achieved 0.892 of area under the ROC (AUC). The accuracy, recall, precision, and F1-score of the CAD method were 84.66%, 84.66%, 84.77%, 84.65%, while those of the junior and senior radiologists were 56.82%, 56.82%, 56.95%, 56.62% and 64.20%, 64.20%, 64.35%, 64.11% respectively. With the help of CAD, the metrics of the junior and senior radiologists improved to 62.81%, 62.81%, 62.85%, 62.79% and 73.86%, 73.86%, 74.00%, 73.83%. The results almost repeated on the unbalanced dataset. The results show the proposed CAD approach can not only achieve better performance than radiologists, but also significantly improve the radiologists’ diagnosis of FTC.ConclusionsThe performances of the CAD system indicate it is a reliable reference for preoperative diagnosis of FTC, and might assist the development of a fast, accessible screening method for FTC.

Alkaline-earth metal nitrides have attracted widespread attention due to their promising potential for high energy–density materials. We predicted the new structures of N-rich BaNx (x = 3–5) compounds under high pressure and analyze their potential properties in this paper. The P21/c-BaN3 was discovered for the first time and it can exist stably when the pressure exceeds 100 GPa. Besides, both the new structures of P1¯-BaN4 and P1¯-BaN5 have lower energy values than the previously predicted structures at pressures of 90 GPa and 70 GPa, respectively. The three novel N-rich barium–nitrogen compounds are dynamically stable under high pressure conditions. Especially, it was found that the phase of P21/c-BaN3 can maintain dynamic stability at ambient condition by the AIMD. So, the P21/c-BaN3 is a metastable structure and it is not only synthesized at high pressure, but also can be quenched to ambient conditions. Moreover, the energy densities of P1¯-BaN4 and P1¯-BaN5 are equivalent to that of TATB, RDX, and the detonation velocity and detonation pressure of the two structures are second only to TNT. This paper provides important help for the research of N-rich alkaline earth metal Ba–N Compounds.

Background: Omega-3 polyunsaturated fatty acids (PUFAs) play beneficial roles in metabolism and health. Little is known about the effects of different doses of omega-3 PUFAs on gut microbiota. Objective: In this study, we focus on the effects of different doses of omega-3 PUFAs on gut microbiota and immunity. Design: BALB/c mice was first treated with ceftriaxone sodium for 7 days, and then they received saline or different doses of omega-3 PUFAs (30, 60 and 90 mg omega-3 PUFAs) via daily gavage for 21 days. Alterations of cecum microbiota; the tight junction proteins, zonula occludens 3 (ZO3) and occludin, in the ileal wall; serum lipopolysaccharide (LPS); Interleukin-10 (IL-10), interleukin-1β (IL-1β), and Tumour Necrosis Factor α (TNF-α) ; mucus SIgA levels were measured. Results: Compared with the ceftriaxone sodium administration group, significant increases in bacterial richness and diversity were observed in the 60- and 90-mg omega-3 PUFA groups, while only a slight increase was observed in the 30-mg omega-3 PUFA group. A higher percentage of several genera, including Lactobacillus, Helicobacter, and Ruminococcus, and a lower percentage of Bacteroides, Clostridium, and Prevotella were observed in the 60- and 90-mg omega-3 PUFA groups when compared with those in the 30-mg group. The expression of ZO3 and occludin proteins increased in 60- and 90-mg omega-3 PUFA groups compared with the natural recovery group. The mucus SIgA and serum IL-10 levels were increased, and serum levels of LPS, IL-1β, and TNF-α were decreased in the 60- and 90-mg omega-3 PUFA groups when compared with those in the ceftriaxone sodium-treated group. Conclusion: Different doses of omega-3 PUFAs have different therapeutic effects on the intestinal microbiota. The 60- and 90-mg omega-3 PUFA supplementation had better recovery effects on the gut microbiota and immunity than those of the 30 mg omega-3 PUFAs supplementation.

The stability of large-scale landslide in the reservoir area of hydropower station has an important impact on the safety of dam structure and surrounding people's life and property. Large-scale real-time accurate observation of the stability and deformation trend of this type of landslide can provide reliable early warning and control information for the landslide, which is of great significance. The traditional monitoring methods of landslide in the reservoir area of hydropower station are mainly GNSS monitoring and total station monitoring. In this study, the advanced GB-InSAR system LKR-05-KU-S100 is applied to the monitoring of Cangjiangqiao-Yingpan landslide and Dahua landslide of Dahuaqiao hydropower station on Lancang River. Field monitoring tests show that the system has high precision, and it can carry out long-distance, all-day, all-weather, and large-scale monitoring. It has unique advantages and broad application prospects for the monitoring of large and super-large landslides.

BackgroundThe detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images.Materials and MethodsWe retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models.ResultsThe proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models.ConclusionThe ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.

Abstract In teledermoscopy, images are transmitted through a communication channel to the medical facility for medical consultation. This yields to bandwidth congestion and consumption of large storage size which impairs the transmission of the high‐resolution dermoscopy image. This study proposes a novel technique by generating a diagnostic feature map, named diagonal compressive sensing (CS) features map. This proposed map is generated using the significant diagnostic features by aligning the feature vector in a diagonal map, which is then compressed using CS technique. Eventually, the recovered feature map at the receiver side is applied to the classification system for decision‐making. In addition, physicians at the receiver side who were trained to read the feature maps can verify the classification decision, and then provide feedback to the patient. The results demonstrated that the proposed diagnostic map achieved less transmission time due to the small size of the feature map along with the compression process. Furthermore, the feature map has drastically improved the classification performance metrics, including the accuracy, which increased, for example, from 88.6% to 98% at 80% compression ratio compared to the traditional method on the compressed whole image.

Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by attackers to consume a large amount of heap memory and consequently trigger crashes. There have been few works on the vulnerability fuzzing of heap consumption. Most of them, such as MemLock and PerfFuzz, have failed to consider the influence of data flow. We proposed a heap memory consumption guided fuzzing model named MemConFuzz. It extracts the locations of heap operations and data-dependent functions through static data flow analysis. Based on the data dependency, we proposed a seed selection algorithm in fuzzing to assign more energy to the samples with higher priority scores. The experiment results showed that the MemConFuzz has advantages over AFL, MemLock, and PerfFuzz with more quantity and less time consumption in exploiting the vulnerability of heap memory consumption.

To solve the problems associated with micron-sized aluminum (Al), including sintering, agglomeration, and slag deposition during the combustion of aluminized propellants, aluminum–lithium (Al-Li) alloy, prepared by introducing a small amount of Li (1.0 wt.%) into Al, was used in place of Al. Then, the ignition and combustion characteristics of single micron-sized Al-Li alloy particles were investigated in detail using a self-built experimental apparatus and multiple characterization methods. The ignition probability, ignition delay time, flame propagation rate, burn time, combustion temperature, flame radiation spectra, and microexplosion characteristics were obtained. The TG-DSC results demonstrated that, as compared to the counterpart Al, the Al-Li alloy had a lower ignition temperature. The emission lines of AlO revealed the gas-phase combustion of the Al-Li alloy, and thus the Al-Li alloy exhibited a mixed combustion mode, including surface combustion and gas-phase combustion. Moreover, during combustion, a microexplosion occurred, which increased the combustion rate and reduced the burn lifetime. The ambient pressure had a significant effect on the ignition and combustion characteristics of the Al-Li alloy, and the ignition delay time and burn time exponentially decreased as the ambient pressure enhanced. The combustion temperature of the Al-Li alloy at atmospheric pressure was slightly higher than those at elevated pressures. The Al-Li alloy burned in N2, but no microexplosion was observed. Finally, the ignition and combustion mechanism of the Al-Li alloy in air was demonstrated by combining SEM, EDS, and XRD analyses of the material and residues. The results suggest that the addition of Li promoted the combustion performance of Al by changing the surface structure of the oxide film and the combustion mode.

Laser shock processing (LSP) is an innovative approach, which effectively improves the mechanical behavior of metallic structures by introducing compressive residual stress. To evaluate the residual stress evolution in low-carbon 13Cr4Ni martensitic stainless steel multi-pass butt-welded joints induced by LSP, a two-step numerical simulation including welding analysis, at first, followed by LSP calculation with the simulated welding stress results being taken into account, was performed based on ABAQUS software. Effects of LSP parameters such as power density, spot size, overlapping rate and numbers of laser shock on the residual stress variations, were systematically investigated. To validate the reliability and accuracy of the numerical simulation, experiments of welding and LSP were conducted in sequence. The residual stress after welding and LSP were investigated by x-ray diffraction method. Results demonstrate that the simulated results show a good agreement with the experimental datas. The welding residual stress distribution is uneven. Larger tensile stresses appear on the weld surface and its adjacent heat-affected zone, which could be converted into high-level compressive stress after LSP. Furthermore, an ideal residual stress field can be obtained after two successive laser shocks with an overlap rate of 75% when the power density, spot diameter, and pulse width are 7.6 GW cm ^−2 , 4 mm, and 25 ns, respectively.

For the study of the driving forces behind fault activation and its influencing factors on the barrier effect of rock mass movement under the influence of mining, the discrete element numerical simulation software 3DEC was used for the analysis of the impact on the distance to mining area from fault, the buried depth of the upper boundary of the fault, the dip angle of fault, the size of the mining area and the thickness of the fault zone respectively. The results show that the mining areas are closer to the fault as distances decrease, the burial depth of the upper boundary of the fault increases, and the size of the mining area increases, the fault is easier to activate, and fault activation has a stronger barrier impact on displacement field and stress field propagation. When the fault is cut into the goaf, the difference of rock displacement in both directions of the fault increases when the dip of the fault increases, and the fault is more susceptible to instability and activation. The barrier strength grows with the increase of the thickness of the fault fracture zone. The results of this study have important implications for the guard against and control of deep mining-related fault activation disasters.

In order to study the law and mechanism of fault activation induced by deep continuous mining, this paper takes the fault activation caused by underground mining of Shizishan Copper Mine as the research object. On the basis of field investigation and theoretical analysis of fault activation, the law and mechanism of fault group activation induced by deep continuous mining in the Shizishan mining area are analyzed in detail based on the three-dimensional discrete element method software 3DEC. The results show that the fault effect of rock movement in the footwall of the main orebody in the mine area is obvious. The en echelon steep dipping fault group in the footwall of the goaf will move each fault block in the direction of the goaf and slip along the fault plane when the balance between mining and gravity is disrupted. The fault activation has a domino effect in time and space, and the mechanism of this effect is revealed. The research results have great significance for the deep mining area development, mining alignment arrangement, and disaster prevention and control caused by fault activation.

In order to explore the evolution mechanism of water and mud inrush, based on the fluid-solid coupling similarity theory and a large number of matching tests, fault similar materials with mountain sand, gravel, and red clay as raw materials and surrounding similar rock materials with mountain sand, red clay, cement, and water as raw materials are developed. Similar materials’ physical and mechanical properties and hydraulic properties with different ratios are tested and analyzed. The results show that the red clay content influences the mechanical properties of similar materials and their hydraulic properties, and the gravel substrate mainly influences the fault permeability coefficient. Similar material can be adjusted within a certain range of mechanical parameters. The material is simple and suitable for developing similar materials for different low and medium strength rock masses. Finally, a similar material was used in a model test of the tunnel fault fracture zone to reveal the mechanism of water and mud bursts in the tunnel. The study results can be used as a reference for the development of similar materials for tunnel fracture zone surrounding rocks.

Parkinson's disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/impact of the top-selected PCA- and ECFS- features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94% accuracy for voice-loss detection in PD, while, with the same classifier, 88% accuracy was achieved without using the proposed selection method.

In the present study, a multi-step (MS) cyclic rolling and intercritcal annealing process was proposed and applied for dual-phase (DP) steel. The MS process performed three times with 27% deformations and intercritical annealing, while the single-step (SS) process performed an 81% rolling, along with intercritical annealing. A microstructure with an average grain size of 3 μm and a martensite content of ~40% was obtained after MS treatment, which is similar to results obtained from the SS treatment. However, the distribution exhibits significant differences between the two different routes. A more homogenous distribution of ferrite–martensite was achieved after the multi-step compared with the single-step treatment. The yield strength of MS is slightly smaller than that of SS, while the ultimate tensile strength is better, which results in a decrease in yield ratio. Furthermore, the ductility was greatly improved after MS, which is mainly attributed to the uniform chain-like distribution of martensite.

In order to study the deformation evolution law of fault activation caused by deep mining in Shizishan Copper Mine, China, a monitoring system for fault activation slip is designed and implemented on the basis of the field investigation of footwall fault activation of the main orebody in the mining area. The displacement and stress of the fault are monitored by the multipoint displacement meter, bolt stress meter, and borehole stress meter. According to the measured results, the activation deformation laws of fault F2, fault F3, and fault F4 during deep continuous mining are analyzed in detail. The results show that, when the influence range of underground mining spreads to the fault, the increase in the additional tensile stress on the fault plane will reduce the shear strength of the fault and increase the slip of the fault. When the shear stress exceeds the shear strength of the fault plane, the shear failure of the fault plane occurs, the rock mass on both sides of the fault loses stability, and the fault becomes active; when the orebody in the deep sublevel 14 and sublevel 15 were continuously stoped, the development of the mining influence area to fault F2 leads to fault F2’s activation. When stoping the orebody in sublevel 16, fault F3 also activates. With the continuous downward mining of the deep part, the slip amount increases continuously. The fault activation sequence is from fault F2 to fault F3, and then to fault F4.

Abstract Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement.

A concept map is an important knowledge visualization tool in adaptive learning systems. The weak concepts of students can be identified by analyzing the concept maps to provide learning guidance and teaching suggestions to students and teachers. However, in the current researches, it is difficult to analyze effective information from the concept maps when the number of concepts is large and the associations between concepts are complicated. It rarely reflects the learning performance of different student groups, which do not reflect the characteristics of the adaptive learning systems. A learning path automatic generation algorithm, Learning paths generation (LPG) algorithm, for adaptive learning systems is proposed. The LPG algorithm fully considers the learning performance of different student groups. The concept maps with different learning features are generated by the clustering algorithm and association rules mining, and several simplified learning paths are generated by using the topological sorting algorithm. Experiments show that the LPG algorithm has a good discrimination for the student groups and can generate different learning paths according to the students’ learning performance.

Low-light image enhancement is an important challenge in computer vision. Most of the low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). First, we show that multiscale retinex (MSR) can be considered as a convolutional neural network with Gaussian convolution kernel, and blending the result of DWT can improve the image produced by MSR. Second, we propose our pipeline neural network, consisting of denoising net and low-light image enhancement net, which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in the synthetic dataset and public dataset. The experiments reveal that in comparison with other state-of-the-art methods, our methods achieve a better performance in the perspective of qualitative and quantitative analyses.

Breast tumor segmentation in ultrasound is important for breast ultrasound (BUS) quantitative analysis and clinical diagnosis. Even this topic has been studied for a long time, it is still a challenging task to segment tumor in BUS accurately arising from the difficulties of speckle noise and tissue background inconsistence. To overcome these difficulties, we formulate breast tumor segmentation as a classification problem in the neutrosophic set (NS) domain, which has been previously studied for removing speckle noise and enhancing contrast in BUS images. The similarity set score and homogeneity value for each pixel have been calculated in the NS domain to characterize each pixel of BUS image. Based on that, the seed regions are selected by an adaptive Otsu-based thresholding method and morphology operations, then an adaptive region growing approach is developed for obtaining candidate tumor regions in NS domain. The direction of region growing depends on the differences of similarity set score, texture homogeneity value, and distance value between the seed region and the candidate growing points. A deep convolutional neural network, based on VGG-16 network, is applied to each candidate tumor region for false positive reduction. A testing dataset with pathology proof includes 35 images without tumor, 36 images with benign tumors, and 50 images with malignant tumors. The numerical experiments show that the proposed method is effective to segment breast tumor in BUS images with average 81.6% and 84.4% percent dice coefficient, average 77.0% and 84.3% true positive ratio, average 11.2% and 15.2% false positive ratio, and average 57.5 pixels and 52.8 pixels Hausdorff distance for benign and malignant images, respectively.

Dermoscopic images suffer from irregular and vague boundaries. New directions established the neutrosophic set (NS) approaches for clustering, and segmenting the dermoscopic images. In this work, an accurate segmentation process was developed by mapping initially the dermoscopic images to the NS domain. Thus, the neutrosophic image was defined by three subsets, namely True (T), Indeterminacy (I) and False (F). For accurate boundary detection and segmentation, different high pass (HP) filter types were used in the definition of I subset and low pass (LP) filter types in the definition of T. These filters form a new way to obtain an NS image for segmenting dermoscopy images. A comparative study was carried on the ISIC2016 skin lesion dermoscopic images dataset using different combinations of NS filter types and sizes. The results depicted the superiority of using an unsharp filter in implementing the I subset and an average filter for the T subset. 96% segmentation accuracy was reported using the proposed design compared to 92% accuracy using the default NS definition.

Android app clone detection has been extensively studied in our community, and a number of effective approaches and frameworks were proposed and released. However, there still remains one open challenge that has not been well addressed in previous work, i.e., the authorship attribution for the detected app clones. Although state-of-the-art approaches could accurately identify repackaged apps in one way or another, no convincing method has been proposed to identify the original app and the authentic author from the repackaged app pairs, which greatly limits the usage scenario of app clone detection techniques. For example, app market maintainers have to manually confirm the identified repackaged app pairs, while in most cases, it is challenging for them to make an accurate decision. In this paper, we propose AppAuth, a novel learning-based approach to predict the authorship of app clones. To be specific, for a given Android app clone pair (or a group of repackaged apps identified), AppAuth could accurately infer the original author of the plagiarized apps. Our approach is motivated by the traditional authorship attribution studies on binary files. AppAuth first extracts a number of coding-style-related features from the executable .apk files, and then relies on machine learning techniques to train a classification model. We have conducted extensive experiments to evaluate the effectiveness of AppAuth. The experiment results suggest that we are able to infer the authorship for Android app clones with high precision. Our work is the first one that tackles the problem systematically and we believe our efforts could positively contribute to the research community and boost the research of app repacking detection and authorship attribution studies.

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

The monitoring and early warning of a landslide in the reservoir area of a hydropower station are of great significance in the dam structure of the hydropower station and in the safety of people’s life and property on the reservoir bank. In this study, a new ground-based interferometric synthetic aperture radar system LKR-05-KU-S100 was used to carry out field monitoring tests on Lagu landslide and Xiaozhaju landslide of Dahuaqiao hydropower station and No. 1 landslide on the left bank of Xiaowan hydropower station on the Lancang river. The results show that, during the monitoring period, Lagu landslide of Dahuaqiao hydropower station and No. 1 landslide on the left bank of Xiaowan hydropower station are basically stable, and the deformation trend of Xiaozhaju landslide is obvious so it should undergo continuous monitoring. At the same time, the field monitoring test also shows that the new ground-based interferometric synthetic aperture radar system LKR-05-KU-S100 has the advantages of high precision and long-distance, all-day, all-weather, and large-scale monitoring and has unique advantages and broad application prospects for the overall deformation monitoring of large landslides in the reservoir area.

Time and water-to-cement ratio have significant influences on rheology of cement grouts. In order to study effects of time and water-to-cement ratio on rheology of Bingham cement grouts, taking Bingham cement grouts widely used in practical engineering (cement grouts with water-to-cement ratio of 0.75–1.25) as research object, some rheological experiments of five cement grouts with water-to-cement ratio of 0.8, 0.9, 1.0, 1.1, and 1.25 were carried out at six moments of 0, 5, 10, 20, 30, and 60 minutes, respectively. Combining theoretical discussion with numerical analysis, influence of coupling effects of time and water-to-cement ratio on rheological properties of Bingham cement grouts was discussed. Results show that at the level of α = 0.05, time has a significant influence on plastic viscosity but has no significant influence on the yield stress of Bingham cement grouts. Water-to-cement ratio has a significant influence on both plastic viscosity and yield stress. Exponential models obtained by comprehensive analysis from statistical theory, practical applicability, and accuracy are the optimal models to describe quantitative change in the relationship of coupling effects of time and water-to-cement ratio on plastic viscosity and yield stress of Bingham cement grouts. The rheological equation considering coupling effects of time and water-to-cement ratio of Bingham cement grouts is constructed. Research achievements not only have certain theoretical significance to the development and improvement of fluid mechanics and theoretical system of penetration grouting but also provide theoretical support and technical reference for practical grouting engineering and also have certain practical significance for solving or improving the practical engineering problems.

Materials under high pressure usually exhibit unique chemical and physical properties. Polynitrogen compounds have received widespread attention as potential high energy density materials. This paper uses CALYPSO crystal structure prediction method to study the structures of ScN _6 and ScN _7 in 0–100 GPa. Theoretical calculations show that ScN _6 is thermodynamically stable above 80 GPa, while ScN _7 is thermodynamically stable from 30 GPa to 90 GPa. Furthermore, ScN _7 is metastable under ambient conditions, demonstrating that it can be quenched to ambient conditions after high pressure synthesis. The P $\bar{1}$ -ScN _6 is a three-dimensional extended fold multi-nitrogen network, and the P $\bar{1}$ -ScN _7 contains a five-membered ring and a curved N4 molecular unit. Both P $\bar{1}$ -ScN _6 and P $\bar{1}$ -ScN _7 contain a lot of N–N single bonds and N=N double bonds. The energy densities of P $\bar{1}$ -ScN _6 and P $\bar{1}$ -ScN _7 are 3.97 kJ g ^−1 and 3.12 kJ g ^−1 , respectively. The detonation velocity and detonation pressure of the P $\bar{1}$ -ScN _6 phase and P $\bar{1}$ -ScN _7 phase are also higher than that of TNT. Excellent energy storage properties and detonation performance show that they can be used as potential high-energy materials. These results opened up a new way for the synthesis of nitrogen-rich compounds.

In the present study, an austenitic stainless steel (2Cr17Ni13Mo5) was processed by severe cold rolling (SCR) and subsequent annealing. The grain size was severely refined to ∼100 nm after SCR. The yield strength (YS) and ultimate tensile strength (UTS) increased to 1510.5 MPa and 1660.4 MPa, respectively. Nevertheless, the ductility decreased significantly. Full recrystallization was completed after annealing at 800 °C for 1h. However, the ultrafine-grained structure was maintained with the grain size of 780 nm, which is mainly attributed to the pinning effect of χ phase precipitated at the grain boundary during annealing. Good comprehensive mechanical properties with high strength (YS = 977.6 MPa, UTS = 1197.7 MPa) and good ductility (elongation = 17%) were obtained. The high strength is mainly attributed to the ultrafine-grained structure maintaining, while the annihilation of dislocation contributes to the ductility improvement.

BackgroundAccurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.MethodsAutoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).ResultsBy analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively.ConclusionsComparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number.

In order to mitigate investments, stock price forecasting has attracted more attention in recent years. Aiming at the discreteness, non-normality, high-noise in high-frequency data, a support vector machine regression (SVR) algorithm is introduced in this paper. However, the characteristics in different periods of the same stock, or the same periods of different stocks are significantly different. So, SVR with fixed parameters is difficult to satisfy with the constantly changing data flow. To tackle this problem, an adaptive SVR was proposed for stock data at three different time scales, including daily data, 30-min data, and 5-min data. Experiments show that the improved SVR with dynamic optimization of learning parameters by particle swarm optimization can get a better result than compared methods including SVR and back-propagation neural network.

Explosive growth of applications in hyperspectral image (HSI) has made HSI classification a hot topic in the remote sensing community. The key to improve classification accuracy is how to make full use of the spectral and spatial information. We combine k-nearest neighbor (KNN) algorithm with guided filter which can extract spatial context information and denoise the classification results by edge-preserving filtering. To solve the problem of dimension disaster, we also take dimensionality reduction into account for HSI classification. To verify the feasibility of our proposed methods, we evaluate the performance over four widely used hyperspectral data sets. The experimental results show that with only 5% of samples, our method obtained better performance than improved support vector machine and KNN methods.

The time-dependent behavior of power-law fluid has a significant influence on the grouting effects of reinforcing loose gravel soil. In this paper, based on basic rheological equations and the time-dependent behavior of rheological parameters (consistency coefficient and rheological index), rheological equations and penetration equations of time-dependent power-law fluid are proposed. Its penetration grouting diffusion mechanism for reinforcing loose gravel soil was then theoretically induced. A set of indoor experimental devices for simulating penetration grouting was designed to simulate the penetration grouting of power-law fluid with different time-dependent behaviors for reinforcing loose gravel soil. Then, relying on the COMSOL Multiphysics platform and Darcy’s law, three-dimensional numerical calculation programs for this mechanism were obtained using secondary-development programming technology. Thus, the numerical simulations of the penetration grouting process of power-law fluid with different time-dependent behaviors for reinforcing loose gravel soil were carried out. This theoretical mechanism was validated by comparing results from theoretical analyses, indoor experiments, and numerical simulations. Research results show that the three-dimensional numerical calculation programs can successfully simulate the penetration diffusion patterns of a time-dependent power-law fluid in loose gravel soil. The theoretical calculation values and numerical simulation values of the diffusion radius obtained from this mechanism are closer to indoor experimental values than those obtained from the penetration grouting diffusion theory of power-law fluid without considering time-dependent behavior. This mechanism can better reflect the penetration grouting diffusion laws of a power-law fluid in loose gravel soil than the theory, which can provide theoretical support and guidance for practical grouting construction.

Porosity and particle arrangement are important parameters affecting soil tortuosity, so it is of great significance to determine the intrinsic relationship between them when studying soil permeability characteristics. Theoretical derivation and geometric analysis methods are used to derive a two-dimensional geometric tortuosity model. The model is a function of particle arrangement parameters (m and θ) and porosity. An analysis of the model and its parameters shows that: (1) The arrangement of particles is one of the reasons for the different functional relationship between tortuosity and porosity, which proved that the tortuosity is not only related to the porosity but also affected by the particle arrangement. (2) The greater the anisotropy parameter m is, the greater the tortuosity is, indicating m varies when fluid passes through the soil from different sides resulting in different values of permeability. (3) The tortuosity increases with the increase in the blocking parameters θ. (4) With increasing porosity, the influence of the parameters m and θ on the tortuosity gradually decreases, suggesting that the influence of particle arrangement on tortuosity gradually decreases. The results presented here increase the understanding of the physical mechanisms controlling tortuosity and, hence, the process of fluid seepage through soil.

Cyclic AMP (cAMP) is a pivotal signaling molecule existing in almost all living organisms. However, the mechanism of cAMP signaling in plants remains very poorly understood. Here, we employ the engineered activity of soluble adenylate cyclase to induce cellular cAMP elevation in Arabidopsis thaliana plants and identify 427 cAMP-responsive genes (CRGs) through RNA-seq analysis. Induction of cellular cAMP elevation inhibits seed germination, disturbs phytohormone contents, promotes leaf senescence, impairs ethylene response, and compromises salt stress tolerance and pathogen resistance. A set of 62 transcription factors are among the CRGs, supporting a prominent role of cAMP in transcriptional regulation. The CRGs are significantly overrepresented in the pathways of plant hormone signal transduction, MAPK signaling, and diterpenoid biosynthesis, but they are also implicated in lipid, sugar, K+, nitrate signaling, and beyond. Our results provide a basic framework of cAMP signaling for the community to explore. The regulatory roles of cAMP signaling in plant plasticity are discussed.

The reactions between o-hydroxylphenyl-functionalized enaminones and sulfonyl hydrazines providing 3-sulfenylated chromones via domino chromone ring construction and C(sp2)–H bond sulfenylation have been achieved under transition-metal-free conditions by using KIO3 as the only catalyst.

With the sharp increase in mobile apps, modular design and functional reuse are commonly adopted. The inter-component communication (ICC) mechanism in Android allows apps to exchange data with other apps and components, resulting in large amounts of security issues, such as component hijacking vulnerabilities, privilege escalation and spoofing attacks. Although ICC has been extensively studied in previous work, none of the previous approaches is practically scalable to simultaneously analyze a large number of Android apps, giving the combinational explosion of possible inter-component (and inter-app) communications. In this paper, we first propose an explorative study to analyze the ICC-based interaction for a large amount of Android apps. Then we propose CRSPR, a PageRank-like topic-aware app ranking approach to highlight influential Android apps for ICC analysis. The experimental results show that CRSPR is better than the basic counting approach as well as the traditional PageRank-based approach, which further demonstrate that CRSPR is useful for highlighting influential Android apps.

In the present work, the microstructure and texture of non-oriented 3.3% Si steel processed by asymmetric rolling (ASR) and subsequent annealing at different temperatures were compared with those obtained when using traditional symmetric rolling (SR). This work aims to reveal the effect of shear bands introduced by the ASR on the microstructure and texture evolution. The ASR sample reaches a recrystallization fraction of 62% at an annealing temperature of 650 °C, which is 32% higher than that of the SR sample annealed at the same temperature. This can be attributed to the abundant shear bands introduced by the ASR, which serve as the heterogeneous nucleation sites for the recrystallized grains. When increasing the annealing temperature to 750 °C, complete recrystallization could be observed in both asymmetric- and symmetric-rolled samples. When using an annealing temperature of 650 °C, the γ-oriented grains were dominant in the surface layer, while strong Goss-oriented grains could be observed in the center in the ASR sample. This is due to the fragmented small subgrains with different orientations in the surface layer inhibiting the nucleation of Goss- and cube-oriented grains during the annealing. In contrast, numerous Goss- and cube-oriented grains were formed in the surface layer after complete recrystallization when the ASR sample was annealed at a temperature of 750 °C. This may be related to the higher thermal energy, which benefits the nucleation of the Goss- and cube-oriented grains. In addition, ASR significantly increased the strength of η-fiber after complete recrystallization when compared with SR. This work might be helpful to design the rolling and the subsequent annealing processes.