Search Results (134)

By testing the creep properties of a Re/Ru-containing single-crystal alloy specimen and examining the microstructural evolution of the allow at different stages of creep using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the deformation and damage mechanisms of the alloy under ultra-high temperature conditions were investigated. It was observed that a dislocation network forms before the rafting of the γ′ phase. As creep progresses, this network becomes increasingly dense and complete. Moreover, the dislocation network undergoes a transformation from the <110>-type to the <100>-type configuration, with a hybrid <110>-<100>-type network representing an intermediate state during the transition. Stacking faults were also identified within the γ′ phase, suggesting that the stacking fault energy of this alloy is lower compared to that of other alloys. During creep, dislocations that penetrate the γ′ phase can undergo cross slip from the {111} plane to the {100} plane under applied stress, resulting in the formation of Kear–Wilsdorf (K–W) immobile dislocation locks. These locks hinder further dislocation movement within the γ′ phase. It is concluded that the damage mechanism of the alloy at the later stage of creep under 120 MPa/1160 °C involves initial crack formation at the interface of the twisted raft-like γ/γ′ two-phase structure. As creep continues, the crack propagates in a direction perpendicular to the applied stress axis. Full article

It has been demonstrated that when a low-molecular-weight salt solution flows through a polyelectrolyte gel, an electromotive force is generated, and its polarity depends on the sign of the polyelectrolyte network’s charge. A mathematical model proving the possibility of developing a device for separating a solution of low-molecular salt into enriched and depleted phases under the influence of gravitational forces has been developed. Such a device contains a system of parallel columns filled with different kinds of cross-linked polyelectrolyte networks. The proposed mathematical model is grounded in the theory of double electrical layers forming at the hydrogel/solution interface; these layers deform under non-equilibrium conditions, specifically during the flow of the solution through the cross-linked polyelectrolyte network. An analogous model is proposed describing the case of an analogous device based on an electric current passing through two oppositely charged contacting networks, which provides the possibility of separating the initial solution into enriched and the depleted phases too. The practical applications of the found effect are discussed. In particular, it is demonstrated that a wide number of measurement electronic devices can be created on such a base, including devices to be used within the investigation of polyelectrolyte hydrogels of different types. Full article

Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expressions, no AI models have been developed to estimate these affective states from facial images based on empirical data. We developed a recurrent neural network-based AI model to estimate subjective valence and arousal states from facial images. We trained our model using a database containing participant valence/arousal states and facial images. Leave-one-out cross-validation supported the validity of the model for predicting subjective valence and arousal states. We further validated the effectiveness of the model by analyzing a dataset containing participant valence/arousal ratings and facial videos. The model predicted second-by-second valence and arousal states, with prediction performance comparable to that of FaceReader, a commercial AI model that estimates dimensional affective states based on a different approach. We constructed a graphical user interface to show real-time affective valence and arousal states by analyzing facial video data. Our model is the first distributable AI model for sensing affective valence and arousal from facial images/videos to be developed based on an empirical database; we anticipate that it will have many practical uses, such as in mental health monitoring and marketing research. Full article

Maintaining a fast, low-latency network must be balanced in the demanding world of High-Performance Computing (HPC). Any compromise in network performance can severely affect distributed HPC applications, leading to bottlenecks that undermine the entire system’s efficiency. This paper highlights the critical need for precise and consistent evaluation of Kubernetes Container Network Interfaces (CNIs) to ensure that HPC workloads can operate at their full potential. Traditional manual methods for evaluating network bandwidth and latency are time-consuming and prone to errors, making them inadequate for the rigorous demands of HPC environments. To address this, we introduce a novel approach that leverages Ansible to automate and standardize the evaluation process across diverse CNIs, performance profiles, and configurations. By eliminating human error and ensuring replicability, this method significantly enhances the reliability of performance assessments. The Ansible playbooks we developed enable the efficient deployment, configuration, and execution of CNIs and evaluations, providing a robust framework for ensuring that Kubernetes-based infrastructures can meet the stringent performance requirements of HPC applications. This approach is vital for safeguarding the performance integrity of HPC workloads, ensuring that inadequate network configurations do not cripple them. Full article

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI). Full article

Interface dislocation networks have a great influence on the mechanical properties of the new Ni-based single-crystal alloy (NSC) containing Re, but it is difficult to find out the structural evolution behaviors at the micro-level. Thus, molecular dynamics (MD) simulation is used to analyze the atomic potential energy change and dislocation evolution mechanism, and non-linear characteristic parameters are used to analyze the microstructure evolution of NSC. First, a new model of Ni-Al-Re that is closer to the real properties of the material is established using the MD method according to the optimal volume ratio of matrix phase to precipitate phase. Then, the MD models of NSC with different contents of Re are calculated and analyzed under compressive and tensile loads. The results show that with an increase in Re atoms, the atomic potential energy at the interface dislocation networks is reduced; thus, the stability of the system is enhanced, and the hindrance of the interface dislocation networks to the dislocation movement of the matrix phase is strengthened. At the same time, the number of HCP structures and OISs formed by the destruction of the intact FCC structures also decreases. In the non-linear ultrasonic experiment, with the increase in Re atoms, the non-linear enhancement of the microstructure of the NSC leads to an increase in the corresponding non-linear characteristic parameters. Accordingly, the microstructural evolution behaviors of the phase interface of the new NSC can be effectively explored using the combination of MD simulation and non-linear ultrasonic experimentation. The results of this study lay a foundation for the subsequent research of the microscopic defects of NSCs by using ultrasonic phased-array technology. Full article

Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading. Full article

We revealed that the encapsulation of enzyme-immobilized silica particles in hollow-type spherical bacterial cellulose (HSBC) gels enables the use of the inside of HSBC gels as a reaction field. The encapsulation of horseradish peroxidase (HRP)-immobilized silica particles (Si-HRPs, particle size: 40–50 μm) within HSBC gels was performed by using a BC gelatinous membrane produced at the interface between Komagataeibacter xylinus suspension attached onto an alginate gel containing Si-HRPs and silicone oil. After the biosynthesis of the BC gelatinous membrane, formed from cellulose nanofiber networks, the alginate gel was removed via immersion in a phosphate-buffered solution. Si-HRP encapsulated HSBC gels were reproducibly produced using our method with a yield of over 90%. The pore size of the network structure of the BC gelatinous membrane was less than 1 μm, which is significantly smaller than the encapsulated Si-HRPs. Consequently, the encapsulated Si-HRPs could neither pass through the BC gelatinous membrane nor leak from the interior cavity of the HSBC gel. The activity of the encapsulated HRPs was detected using the 3,3′,5,5′-tetramethylbenzidine (TMB)-H₂O₂ system, demonstrating that this method can encapsulate the enzyme without inactivation. Since HSBC gels are composed of a network structure of biocompatible cellulose nanofibers, immune cells cannot enter the hollow interior, thus, the enzyme-immobilized particles encapsulated inside the HSBC gel are protected from immune-cell attacks. The encapsulation technique demonstrated in this study is expected to facilitate the delivery of enzymes and catalysts that are not originally present in the in vivo environment. Full article

This study presents a comprehensive bibliometric analysis of studies on financial information/accounting manipulation. The dataset of research includes 1.266 studies from the Web of Science database for the period 1991–2023. All studies included in the research contain either the term ‘financial information manipulation’ or ‘accounting manipulation’ in the topic (title, abstract, or keywords). The bibliometric network mapping technique was used for the analysis of the data. The analysis was conducted utilizing the Biblioshiny interface of the R package programs Bibliometrix and Vosviewer. The results pointed out a notable upward trend in the publication and citation rates of financial information/accounting manipulation studies over the last two decades. Several key findings were identified. Firstly, a substantial rise in research output on financial information/accounting manipulation was observed, particularly after 2000, driven by global financial scandals. Secondly, prolific contributors to this field include authors such as Valaskova and Durana. Thirdly, the United States leads in research output, with significant contributions from institutions like the State University System of Florida and the State University System of Ohio. Lastly, The Accounting Review was identified as the most prolific journal in this domain, with the Journal of Accounting Economics being the most impactful based on citations. The most frequently used keywords indicate that the research topics focus on earnings management as a method of manipulation, fraudulent financial reporting, and the relationship with corporate governance. The comprehensiveness of the bibliometric data lends itself to a further examination of how financial information/accounting manipulation has progressed as a subject in the literature since the 2000s. In addition, this study reveals the social and intellectual structures of the issue, the key research streams, and potential research directions for future research. Full article

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces. Full article

The effect of interface dislocation networks on the mechanical properties of new Ni–based single crystal alloys containing Rhenium (Re) is very large. Because the interface dislocations are microscopic in the nano–scale range, this has not been investigated, and it is very difficult to prepare new Ni–based single crystal alloys containing Re. Therefore, six kinds of new Ni–based single crystal alloys containing Re were prepared, and the hardness tests and nonlinear ultrasonic lamb wave tests were performed on the samples. It was found that the density of interface dislocation networks increases with the increase in the content of Re, which improves the blocking ability of matrix phase dislocation cutting into precipitated phase and enhances the inhibition of dislocation movement. The nonlinear ultrasonic lamb wave tests showed that the materials exhibit better mechanical properties when the density of the interface dislocation networks increases. Meanwhile, a new molecular dynamics model which is closer to the real state of an Ni–based single crystal alloy was constructed to reveal the evolution mechanism of interface dislocation networks. The results showed that the potential energy of Re atoms at the interface is the lowest, which affects the reduction of the potential energy of other atoms at the interface, and thus the stability of the model is improved. In addition, according to the change in the total length of dislocation loops in the model system, with the increase in the content of Re atoms, the inhibition of dislocation movement by dislocation networks at the interface is strengthened. Full article

This study investigates the rheological, morphological, and mechanical properties of melt-processed polylactide/ethylene vinyl alcohol (70PLA/30EVOH) blend composites containing 0.25, 0.5, and 1 wt.% of graphene oxide (GO) nanoplates. Thermodynamic-based suggested the localization of nanoparticles in EVOH, SEM studies showed that the introduction of GO to the blend increased dispersed droplet size, which was attributed to the localization of GO within EVOH, as confirmed by TEM. The rheology results indicated a decrease in the elasticity for the composite containing 0.25 wt.% of GO compared to the neat blend, which was attributed to the sliding effect of the added GO nanoplatelets. However, samples containing higher amounts of GO nanoplatelets exhibited more excellent elasticity than the neat blend. The increased elasticity was suggestively attributed to the dominance of hydrodynamic interactions, the physical network of added nanoplatelets, and polymer/GO interactions over the sliding role of the GO nanoplatelets at higher loadings. In addition, the effect of the order of mixing was investigated, and the premixing of PLA and GO exhibited a decrease in the droplet radius compared to the neat blend. It was ascribed to the localization of GO nanosheets in the PLA and interface, which was confirmed by rheological results and mechanical assessments. Full article

Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to facilitate the use of the model by healthcare professionals. Methods: The CNN model was trained and tested using a dataset of high-resolution microscopic images derived from 162 whole-mount slide images of breast cancer specimens. These images were meticulously scanned at 40× magnification using a state-of-the-art digital slide scanner to capture detailed information. Each image was then divided into 277,524 patches of 50 × 50 pixels, resulting in a diverse dataset containing 198,738 IDC-negative and 78,786 IDC-positive patches. Results: The model achieved an accuracy of 98.24% in distinguishing between benign and malignant cases, demonstrating its effectiveness in cancer detection. Conclusions: This study suggests that the developed CNN model has promising potential for clinical applications in breast cancer diagnosis and personalized treatment strategies. Our study further emphasizes the importance of accurate and reliable cancer detection methods for timely diagnosis and treatment. This study establishes a foundation for utilizing deep learning models in future cancer treatment research by demonstrating their effectiveness in analyzing large and complex datasets. This approach opens exciting avenues for further research and potentially improves our understanding of cancer and its treatment. Full article

mRNAs containing premature stop codons are responsible for various genetic diseases as well as cancers. The truncated proteins synthesized from these aberrant mRNAs are seldom detected due to the nonsense-mediated mRNA decay (NMD) pathway. Such a surveillance mechanism detects most of these aberrant mRNAs and rapidly destroys them from the pool of mRNAs. Here, we implemented chemical cross-linking mass spectrometry (CLMS) techniques to trace novel biology consisting of protein–protein interactions (PPIs) within the NMD machinery. A set of novel complex networks between UPF2 (Regulator of nonsense transcripts 2), SMG1 (Serine/threonine-protein kinase SMG1), and SMG7 from the NMD pathway were identified, among which UPF2 was found as a connection bridge between SMG1 and SMG7. The UPF2 N-terminal formed most interactions with SMG7, and a set of residues emerged from the MIF4G-I, II, and III domains docked with SMG1 or SMG7. SMG1 mediated interactions with initial residues of UPF2, whereas SMG7 formed very few interactions in this region. Modelled structures highlighted that PPIs for UPF2 and SMG1 emerged from the well-defined secondary structures, whereas SMG7 appeared from the connecting loops. Comparing the influence of cancer-derived mutations over different CLMS sites revealed that variants in the PPIs for UPF2 or SMG1 have significant structural stability effects. Our data highlights the protein–protein interface of the SMG1, UPF2, and SMG7 genes that can be used for potential therapeutic approaches. Blocking the NMD pathway could enhance the production of neoantigens or internal cancer vaccines, which could provide a platform to design potential peptide-based vaccines. Full article

Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain–computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node–edge interaction and a multi–scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node–edge interaction (NEI–GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi–stage reconstruction network that restricts the reconstructed images in a coarse–to–fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state–of–the–art methods in terms of accuracy in the commonly used n–way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n–way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n–way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual–decoding brain–computer interfaces. Full article