bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamical analysis of a diffusive malaria model with fixed latent period in the human and vector populations
Authors: Qu Hao , Jiang Tianli , Wang Jinliang , Zhao Jiantao
Published: April 2022

To investigate the impact of the fixed latent periods in the human and vector populations on the disease transmission in heterogenous environment, we formulate a nonlocal and time-delayed reaction-diffusion (NLTD-RD) system. By appealing to the next generation operator (NGO), we define the basic reproduction number (BRN) [Formula: see text], and prove it as a threshold parameter for indicating whether disease persists or not. Specifically, if [Formula: see text], the disease-free equilibrium is globally asymptotically stable, while if [Formula: see text], the disease is shown to be uniformly persistent. In the homogeneous case that all parameters are assumed to be constants, the explicit expression of [Formula: see text] is obtained. We further achieved the global attractivity of the constant equilibria by utilizing Lyapunov functionals. Numerical simulations are performed to verify the theoretical results and the effects of the diffusion rate on disease transmission.
High efficient and stable photocatalyst CeO2/ZnO for tetracycline degradation
Authors: Mou Zhen , Zhou Hongyan , Wu Shuang , Tang Wenqi , Zhang Yiyu , Zhang Kejie , Zhou Zhipin
Published: April 2022

Three kinds of CeO2/ZnO nanocomposites were successfully prepared by the hydrothermal method. The morphology and composition tests show that the CeO2 cubic nanoparticles with a side length of about 10 nm are uniformly loaded on the irregular ZnO nanosheets. Under ultraviolet light irradiation, CeO2/ZnO (1:5) has the best photocatalytic performance and the degradation efficiency of tetracycline (TC) is 97.2% within 140 min. In addition, with CeO2/ZnO (1:5) as photocatalyst, the degradation rate of TC only decreased by 1.3% after 6 cycles, indicating that CeO2/ZnO (1:5) has excellent photocatalytic stability. Therefore, the CeO2/ZnO (1:5) nanocomposite has a bright future for industrial application in wastewater treatment.
Enhanced Magnetoresistance Effect in Graphene Coupled to a Ferromagnetic Oxide with Charge Orbital Ordering
Authors: Fan Yujing , Coileáin Cormac Ó , Arora Sunil K. , Chang Ching-Ray , Wu Han-Chun
Published: April 2022

In this paper, we fabricated graphene/Fe3O4 heterostructure devices by stacking monolayer graphene on magnetite (Fe3O4) substrate and investigated their magneto-transport properties. Interestingly, graphene/Fe3O4 heterostructure devices exhibit a giant magnetoresistance (MR) of 70% at a low magnetic field of 0.65[Formula: see text]T and at 11[Formula: see text]K, which is three times greater than that of graphene on SiO2. Based on standard two-fluid model and LDA[Formula: see text]U simulation, we showed that the observed enhanced MR effect is due to the increased disorder in graphene induced through the charge polarization via the alignment of C atoms of graphene over the charge ordered B-site cations of Fe3O4. Our results demonstrate a potential way to enhance graphene MR effect through coupling graphene with a suitable substrate with charge orbital ordering.
Critical Properties of 2D Anisotropic Ising Model with More Than Nearest Neighbor Interaction
Authors: El Ghazrani A. , Elidrysy A. , Lahbibi A. , Harir S. , Drissi L. B.
Published: April 2022

Using the finite cluster approximation (FCA) based on a single-site cluster theory and Monte Carlo simulation (MCS) under the Metropolis algorithm, we study the critical properties of two-dimensional (2D) spins-½ system by analyzing the effect of the anisotropic parameter [Formula: see text] and next-nearest-neighbor (NNN) interaction [Formula: see text] on the critical temperature [Formula: see text] of the 2D anisotropic square lattice. It is found that the MCS is more reliable than the FCA method in the resolution of the Ising model. Moreover, MC calculations reveal that [Formula: see text] is strongly correlated with the external magnetic field [Formula: see text], [Formula: see text], and [Formula: see text], where the competition between these parameters can generate several rich phase diagrams.
In Silico Screening for Novel Tyrosine Kinase Inhibitors with Oxindole Scaffold as Anti-Cancer Agents: Design, QSAR Analysis, Molecular Docking and ADMET Studies
Authors: Zangeneh Jalalaldin , Shirvani Pouria , Etebari Mahmoud , Saghaie Lotfollah
Published: April 2022

Recently, anti-cancer targeting drugs are directed against specific molecules and signaling pathways. These targeting agents have reasonable specificity, efficacy and less side effects. Tyrosine kinases, which play an essential role in growth factor signaling regulation, are significant targets in this type of therapy. Synthesized numerous tyrosine-kinase inhibitors (TKIs), such as substituted indolin-2-ones, are effective as anti-tumor and anti-leukemia agents. In this study, a series of novel substituted indolin-2-ones were studied as kinase inhibitor analogs through quantitative structure–activity relationship (QSAR) analysis. Two chemometrics methods, such as multiple linear regression (MLR) and partial least squares combined with genetic algorithm for variable selection (GA-PLS), were employed to establish relationships between structural characteristics and kinase inhibitory activity of used oxindole analogs. The GA-PLS was developed as the best predictor and validated QSAR model. The data set compounds were also studied by molecular docking to investigate their binding mechanism in the active site of tyrosine kinase enzyme. According to the information obtained from QSAR models and molecular docking analysis, 40 new potent lead compounds with novel structural features were introduced. Molecular docking, drug-likeness rules, ADMET analysis, bioavailability, toxicity prediction and target identification were carried out on the newly designed oxindoles to elucidate fundamental structural properties that affect their inhibitory activity.
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
Authors: Stripelis Dimitris , Thompson Paul M. , Ambite José Luis
Published: April 2022

There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient
Semi-Synchronous Federated Learning
protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in
data and computationally heterogeneous environments
.
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
Authors: Yang Jie , Mo Kaichun , Lai Yu-Kun , Guibas Leonidas J. , Gao Lin
Published: April 2022

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structures, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured & geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
Multi-document Summarization via Deep Learning Techniques: A Survey
Authors: Ma Congbo , Zhang Wei Emma , Guo Mingyu , Wang Hu , Sheng Quan Z.
Published: April 2022

Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep-learning-based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state of the art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field.
Few-shot Aspect Category Sentiment Analysis via Meta-learning
Authors: Liang Bin , Li Xiang , Gui Lin , Fu Yonghao , He Yulan , Yang Min , Xu Ruifeng
Published: April 2022

Existing aspect-based/category sentiment analysis methods have shown great success in detecting sentiment polarity toward a given aspect in a sentence with supervised learning, where the training and inference stages share the same pre-defined set of aspects. However, in practice, the aspect categories are changing rather than keeping fixed over time. Dealing with unseen aspect categories is under-explored in existing methods. In this article, we formulate a new few-shot aspect category sentiment analysis (FSACSA) task, which aims to effectively predict the sentiment polarity of previously unseen aspect categories. To this end, we propose a novel Aspect-Focused Meta-Learning (AFML) framework that constructs aspect-aware and aspect-contrastive representations from external knowledge to match the target aspect with aspects in the training set. Concretely, we first construct two auxiliary contrastive sentences for a given sentence with the incorporation of external knowledge, enabling the learning of sentence representations with a better generalization. Then, we devise an aspect-focused induction network to leverage the contextual sentiment toward a given aspect to refine the label vectors. Furthermore, we employ the episode-based meta-learning algorithm to train the whole network, so as to learn to generalize to novel aspects. Extensive experiments on multiple real-life datasets show that our proposed AFML framework achieves the state-of-the-art results for the FSACSA task.
Time Series Prediction Using Deep Learning Methods in Healthcare
Authors: Morid Mohammad Amin , Sheng Olivia R. Liu , Dunbar Joseph
Published: April 2022

Traditional machine learning methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, machine learning methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent deep learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally processed healthcare data.

In this article, we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM Digital Library for relevant publications through November 4, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in 10 identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data.
Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing
Authors: Liu Genggeng , Zhou Ruping , Xu Saijuan , Zhu Yuhan , Guo Wenzhong , Chen Yeh-Cheng , Chen Guolong
Published: April 2022

As timing delay becomes a critical issue in chip performance, there is a burning desire for IC design under smart manufacturing to optimize the delay. As the best connection model for multi-terminal nets, the wirelength and the maximum source-to-sink pathlength of the Steiner minimum tree are the decisive factors of timing delay for routing. In addition, considering that X-routing can get the utmost out of routing resources, this article proposes a Timing-Driven X-routing Steiner Minimum Tree (TD-XSMT) algorithm based on two-stage competitive particle swarm optimization. This work utilizes the multi-objective particle swarm optimization algorithm and redesigns its framework, thus improving its performance. First, a two-stage learning strategy is presented, which balances the exploration and exploitation capabilities of the particle by learning edge structures and pseudo-Steiner point choices. Especially in the second stage, a hybrid crossover strategy is designed to guarantee convergence quality. Second, the competition mechanism is adopted to select particle learning objects and enhance diversity. Finally, according to the characteristics of the discrete TD-XSMT problem, the mutation and crossover operators of the genetic algorithm are used to effectively discretize the proposed algorithm. Experimental results reveal that TSCPSO-TD-XSMT can obtain a smooth trade-off between wirelength and maximum source-to-sink pathlength, and achieve distinguished timing delay optimization.
A Contactless Authentication System Based on WiFi CSI
Authors: Lin Chi , Wang Pengfei , Ji Chuanying , Obaidat Mohammad S. , Wang Lei , Wu Guowei , Zhang Qiang
Published: April 2022

The ubiquitous and fine-grained features of WiFi signals make it promising for realizing contactless authentication. Existing methods, though yielding reasonably good performance in certain cases, are suffering from two major drawbacks: sensitivity to environmental dynamics and over-dependence on certain activities. Thus, the challenge of solving such issues is how to validate human identities under different environments, even with different activities. Toward this goal, in this article, we develop WiTL, a transfer learning–based contactless authentication system, which works by simultaneously detecting unique human features and removing the environment dynamics contained in the signal data under different environments. To correctly detect human features (i.e., human heights used in this article), we design a Height EStimation (HES) algorithm based on Angle of Arrival (AoA). Furthermore, a transfer learning technology combined with the Residual Network (ResNet) and the adversarial network is devised to extract activity features and learn environmental independent representations. Finally, experiments through multi-activities and under multi-scenes are conducted to validate the performance of WiTL. Compared with the state-of-the-art contactless authentication systems, WiTL achieves a great accuracy over 93% and 97% in multi-scenes and multi-activities identity recognition, respectively.
A Rare Case of Midostaurin-Associated Sweet’s Syndrome
Authors: Yasin Hesham , Laytem Tessa , Sutamtewagul Grerk , Ayyappan Sabarish
Published: April 2022

Acute febrile neutrophilic dermatosis which is referred as Sweet’s syndrome (SS) is a dermatological condition characterized by fever, erythematous rash, and leukocytosis. SS can be idiopathic or associated with malignancies or medications. We present a rare case of SS which developed shortly after starting midostaurin in a patient with acute myelogenous leukemia (AML).
Pigment Epithelium-Derived Factor-Loaded PEGylated Nanoparticles as a New Antiangiogenic Therapy for Neovascularization
Authors: Zhao Feng , Fei Wenlei , Li Zhouyue , Yu Hanyang , Xi Lei
Published: April 2022

Background. Pathological neovascularization, which involves a disruption in the balance between angiogenic and antiangiogenic factors under pathological conditions, is the basis of many intraocular diseases. Pigment epithelium-derived factor (PEDF) is a potent natural, endogenous inhibitor of neovascularization because of its antiangiogenic and neuroprotective benefits. However, its application is restricted by its instability and short half-life. The present study is aimed at investigating the cytotoxicity and antiangiogenic effects of PEDF-loaded PEGylated nanoparticles (NP-PEG-PEDF) on high glucose-stimulated human umbilical vein endothelial cells (HUVECs). Methods. In this study, NP-PEG-PEDF were fabricated using the multiple emulsion method for the first time. HUVECs were cultured in a high concentration of glucose (30 mmol/L D-glucose), simulating diabetic conditions. The antiangiogenic effects of vascular endothelial growth factor (VEGF), pure PEDF, and NP-PEG-PEDF on proliferation, migration, and tube formation were evaluated. VEGF secretion in high glucose-stimulated HUVECs was further tested in vitro. Results. NP-PEG-PEDF exhibited low cytotoxicity in HUVECs. Our results indicated that in vitro, NP-PEG-PEDF attenuated diabetes-induced HUVEC proliferation, migration, and tube formation and suppressed VEGF secretion. The apoptosis of diabetes-induced HUVECs occurred in a dose-dependent manner, which showed a statistically significant difference compared with the PEDF treatment group. Conclusion. Our study is the first to demonstrate that NP-PEG-PEDF exert antiangiogenic effects on high glucose-stimulated HUVECs and have the potential to alleviate microvascular dysfunction. These data suggest that the NP-PEG-PEDF delivery system may offer an innovative therapeutic strategy for preventing neovascularization of the fundus.
Diagnosis and Prognostic Analysis of Mycoplasma pneumoniae Pneumonia in Children Based on High‐Resolution Computed Tomography
Authors: Leng Jiangang , Yang Zemin , Wang Wenhui
Published: April 2022

Mycoplasma pneumoniae (MP) is defined as a common cause of pulmonary infections and accounts for up to four over ten of pneumonia in children over age 5. This study was aimed to explore the diagnosis and prognosis of mycoplasma pneumoniae pneumonia (MPP) in children using high‐resolution computed tomography (CT) (HRCT). 71 children hospitalized with MPP were undertaken as the research objects to observe the incidence rate, occurrence time, and duration of the clinical symptoms and pathological signs. The chest HRCT and pulmonary ventilation function (PVF) were examined in the acute phase, the second phase re‐examination period, and the third phase re‐examination period. Relevant indicators were statistically analyzed to determine the change rules of chest HRCT and PVF and correlation between the two. Clinically, the children with MPP suffered from fever, cough, and sore throat. In addition to the above symptoms, children with MPP had different degrees of PVF impairment. Compared with the group with normal HRCT results, the forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), forced expiratory flow at 25% forced expiratory volume (FEF25), forced expiratory flow at 50% forced expiratory volume (FEF50), forced expiratory flow at 75% forced expiratory volume (FEF75), and maximum mid‐expiratory flow (MMEF75/25) of children in bronchopneumonia group, segmental pneumonia group, and lobar pneumonia group were obviously reduced, showing statistically great differences (P < 0.05). Compared with the case in acute phase, the PVF indicators of children in the re‐examination phases were much higher, with greatly statistical differences (P < 0.05). In children with MPP, both the large and small airways were affected, but the recovery of the small airways was slow. Pulmonary HRCT and PVF can be undertaken as important indicators to judge the severity and prognosis of MPP in school‐age children.
Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications
Authors: Math Sa , Tam Prohim , Kim Dae-Young , Kim Seokhoon
Published: April 2022

The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).
A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases
Authors: Wang Huanhuan , Yin Hongsheng , Wu Xiang
Published: April 2022

Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy.
Analysis of Risk Factors for Adjacent Segment Degeneration after Minimally Invasive Transforaminal Interbody Fusion at Lumbosacral Spine
Authors: Dong Shengtao , Li Jie , Jia Xiaomeng , Zhu Jieyang , Chen Yaoning , Yuan Bo
Published: April 2022

Background. Adjacent segment degeneration (ASD) has been considered as a serious complication from changes in the biological stress pattern after spinal fusion. The sagittal balance significantly associated with lumbar loading is largely dependent on L5-S1 segment. However, the evidence indicating risk factors for radiological and symptomatic ASD after minimally invasive transforaminal interbody fusion (MIS-TLIF) remains insufficient. Methods. This single-central retrospective study recruited patients with lumbosacral degeneration receiving MIS-TLIF at the L5-S1 level from January 2015 to December 2018. The targeted variables included demographic information, radiological indicators, surgery-related parameters, and patient-reported outcomes (PROs) extracted from the electronic medical system by natural language processing. In these patients, a minimum of 3-year follow-up was done. After reviewing the preoperative and postoperative follow-up digital radiographs, patients were assigned to radiological ASD group (disc height narrowing ≥3 mm, progressive slipping ≥3 mm, angular motion >10°, and osteophyte formation >3 mm), symptomatic ASD group, and control group. We identified potential predictors for radiological and symptomatic ASD with the service of stepwise logistic regression analysis. Results. Among the 157 consecutive patients treated with MIS-TLIF in our department, 16 cases (10.2%) were diagnosed with radiological ASD at 3-year follow-up. The clinical evaluation did not reveal suspicious risk factors, but several significant differences were confirmed in radiological indicators. Multivariate logistic regression analysis showed postoperative PI, postoperative DA, and ∆PI-LL in radiological ASD group were significantly different from those in control group. Nevertheless, for patients diagnosed with simultaneously radiological and symptomatic ASD, postoperative DA and postoperative PT as risk factors significantly affected the clinical outcome following MIS-TLIF. Conclusion. In this study, while approximately 10% of lumbosacral degenerations develop radiographic ASD, prognosis-related symptomatic ASD was shown not to be a frequent postoperative complication. Postoperative PI, postoperative DA, and mismatched PI-LL are risk factors for radiological ASD, and postoperative DA and postoperative PT are responsible for the occurrence of symptomatic ASD following MIS-TLIF. These radiological risk factors demonstrate that restoration of normal sagittal balance is an effective measure to optimize treatment strategies for secondary ASD prevention.
Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition
Authors: Naeem Muhammad Rehan , Amin Rashid , Alshamrani Sultan S. , Alshehri Abdullah
Published: April 2022

The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware’s variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types.
Immune Algorithm to Suppress Rumor Propagation Based on Influence Maximization
Authors: Chen Jing , Wei Nana , Yang Hongbo
Published: April 2022

In order to effectively reduce the spread range of rumor information, an immune method based on two-stage influence maximization to suppress rumor propagation is proposed. Firstly, User and Clustering Influence Maximizing (UCIM) algorithm is proposed to obtain the most influence node set at the current moment in the initial stage of event evolution based on the network topology and user characteristics. Secondly, the node set is identified and classified based on the RNN rumor detection model, and Immunosuppression Strategy considering the Average Path Weights and User-Clustering (IS-APWUC) strategy is proposed for the identified rumor nodes. In this strategy, rumor node set is taken as root nodes, and neighbor nodes with weak influence are pruned to construct an effective rumor path tree. Thirdly, considering the total probability of being infected by the rumors and the comprehensive influence factors, the nodes with high influence in the propagation stage are calculated as immune nodes so as to block the spread of the rumor information. Finally, the proposed method is verified by experiments on four real-world datasets. The results show that the IS-APWUC method has a better rumor suppression effect than the similar correlation algorithms.
Effect of TaC/Ti/Si3N4 Hard Ceramics on Mechanical and Microstructural Behaviour of AA7075 Processed through Stir Casting Process
Authors: Pradeep Kumar J. , Robinson Smart D. S. , Manova Stephen , Ummal Salmaan N.
Published: April 2022

A liquid metallurgical stir casting route was utilized for developing aluminium alloy (7075) matrix composites blended with fluctuating wt% of silicon nitride (Si3N4), tantalum carbide (TaC), and titanium (Ti) for sake of generating a profitable composite with enhanced properties. The physical, microstructural, and mechanical characteristics such as density, high temperature tensile, fatigue, and time-dependent creep experiments were conducted and evaluated. SEM microstructural examinations were performed on the composite samples and proved that there was a good interfacial bonding and uniform dissemination among the reinforcement particulates in the matrix. Physical characteristics were analysed and the outcomes showed that AA7075 blended with 1 wt% TaC, 8 wt% Si3N4, and 0.5 wt% Ti proved higher experimental and theoretical densities of 3.2464 g/cm3 and 3.3038 g/cm3, respectively, with maximum porosity of 1.7758%. At 30°C, the tensile properties of the developed MMCs showed improved ultimate tensile strength (UTS) of 137.64 N/mm2. Beyond 100°C, AA7075 reinforced with 0.75 wt% TaC, 6 wt% Si3N4, and 1 wt% Ti proved to have a higher strength of 232.17 N/mm2. Fatigue properties of the developed MMCs discovered were found to be reformed when proportionate to the base alloy by operating at 14 × 103 cycles at the stress of 42.72 MPa. Time-dependent creep analysis of the developed MMCs found to be advanced due to the addition of tough ceramic particulates.
Investigation of the Generalized Proportional Langevin and Sturm–Liouville Fractional Differential Equations via Variable Coefficients and Antiperiodic Boundary Conditions with a Control Theory Application Arising from Complex Networks
Authors: Boutiara Abdellatif , Kaabar Mohammed K. A. , Siri Zailan , Samei Mohammad Esmael , Yue Xiao-Guang
Published: April 2022

This article studies the existence theory of an innovation type of generalized proportional fractional differential equations with the assistance of the technique of Kuratowski measure on noncompactness combined with the fixed-point theorem of Mönch. Also, we use Lebesgue’s dominated convergence theorem and Arzelá–Ascoli fixed point theorem on existence and uniqueness results. An application is also presented by employing two illustrative iks, which enrich our outcomes.
Clock Synchronization Algorithm for Structural Modal Measurement
Authors: Hu Junliang , Li Kai , Yan Chaoxian , Guo Huawei , Cui Nannan , Huang Shiping , Ding Xiaoyan
Published: April 2022

The modal of a structure is the natural vibration characteristic of the structure, which is very important for damage identification of the system. To accurately measure the structural modal, it is necessary to perform clock synchronization operations on the vibration sensors at each measuring point of the structure. This paper uses an improved TSPN algorithm for clock synchronization. It is assumed that the delay of the data in the transmission process obeys normal distribution. The fitting degree of a normal distribution is tested by fitting the delay distribution curve. Then, an interval estimation method is used to estimate the delay. The synchronization of multiple sensors and onsite structural modal testing verifies the correctness of the process after synchronization. The method in this paper is suitable for MCU‐based vibration sensors to perform clock synchronization for structural modal measurements.
Beer Potomania: Why Initial Fluid Resuscitation May Be Harmful
Authors: Yu Zhuo Lin , Fisher Lisa
Published: April 2022

Beer potomania is one of the less common causes of hyponatremia that we encounter. Patients usually have a recent history of binge drinking along with poor diet. The low solute content in alcoholic beverages limits daily urine output, and ingestion of extra fluid will cause dilutional hyponatremia as a result. Blindly providing intravenous fluid without an underlying cause of the hyponatremia can be detrimental, such as in patients with beer potomania. In our case, a patient presented to the emergency department due to poor oral intake from jaw pain and was found to be hyponatremic from alcohol intake. He initially received 2 liters of fluid, which caused overcorrection of his sodium, requiring more free water to lower his sodium as a result.
Application Research of Fake News and Rumors Detection in Complex Network Environment
Authors: Huang Meng-Zhe , Yin Rong-Wang
Published: April 2022

Nowadays, the network environment is very complicated, and so is the information transmission in the network. False news and rumors have become a big problem in the network environment. How to detect the effective information content in the complex network environment? Interest in effective detection techniques has also grown rapidly in recent years. There is an urgent need to develop effective tools to address this challenge by employing advanced Artificial Intelligence (AI) technologies. In this article, we analyze and study the current state of fake news and rumors in the complex network environment, summarize different methods of detecting fake news and rumors, and point out the important directions for the application of intelligent models in the detection of false information sources. The main purpose is to show possible solutions on the one hand, and on the other hand to determine the main challenges and methodological inadequacy to stimulate future research.
Multitone Piano Transcription Analysis and Evaluation Relying on Hierarchical Analysis High-Performance Computing Algorithms
Authors: Zhu Wenjie
Published: April 2022

Hierarchical analysis refers to the method of dividing the elements related to the result into different levels such as goals, methods, and processes and then performing quantitative and qualitative analysis according to different levels. It can hierarchize complex methods and make the solution process more scientific and reasonable. High-performance computing refers to computing systems and environments that usually use many processors or several computers organized in a cluster. In fact, there are many different types of high-performance computing systems on the market, most of which are used in conjunction with the network. For example, Ford built an online market with high-performance computers and connected to its more than 30,000 suppliers through the network. This kind of online procurement can not only lower prices and reduce procurement costs, but also shorten the procurement time. Ford estimates that this can save approximately $8 billion in procurement costs. In addition, fields such as manufacturing, logistics, and market research are also areas where high-performance computers show their talents. This article aims to study the analysis and evaluation of multitone piano transcription that rely on hierarchical analysis high-performance computing algorithms and hopes to use high-performance computing algorithms to make piano recordings more harmonious. This paper proposes a differential variable-multivariate curve resolution for accurate analysis of GC-MS overlapping peaks, mainly to solve the problem of inaccurate results caused by the difficulty in determining the components during the analysis of overlapping peaks; carrying out matrix calculation on the piano tone characteristic matrix, it realizes the electronic synthesis of the 25th harmonic of the piano tone. The experimental results in this paper show that when the number of Gaussians in the recording process becomes 5, the gap between the sound pressure amplitude and the sound pressure phase is significantly reduced; when the number of Gaussians is 10, the value of the sound pressure phase has an overshoot phenomenon; when the number of Gaussians is 15, the difference between the sound pressure amplitude and the sound pressure phase is the smallest, indicating that the error at this time is the smallest, and it is the most ideal recording environment.
Existential Properties of Algebraic Integrals of a Rigid Body
Authors: Ismail A. I. , Amer W. S.
Published: April 2022

In this article, we consider kinematical considerations of a rigid body rotating around a given fixed point in a Newtonian force field exerted by an attractive center with a rotating couple about their principal axes of inertia. The kinematic equations and their well-known three elementary integrals of the problem are introduced. The existence properties of the algebraic integrals are considered. Besides, we search as a special case of the fourth algebraic integral for the problem of the rigid body’s motion around a fixed point under the action of a Newtonian force field with an orbiting couple. Lagrange’s case and Kovalevskaya’s one are obtained. The large parameter is used for satisfying the existing conditions of the algebraic integrals. The comparison between the obtained results and the previous ones is arising. The numerical solutions of the regulating system of motion are obtained utilizing the fourth-order Runge-Kutta method and are plotted in some figures to illustrate the positive impact of the imposed forces and torques on the behavior of the body at any time.
Design and Application of Chinese English Machine Translation Model Based on Improved Bidirectional Neural Network Fusion Attention Mechanism
Authors: Qin Qin
Published: April 2022

In order to improve the effect of Chinese-English machine translation, this paper combines attention mechanism and neural network algorithm and applies it to Chinese-English machine learning translation. Moreover, this paper uses Gaussian distribution instead of chi-square distribution to analyze the approximate error introduced by the Chinese and English speech energy detection method. In addition, this paper studies the overall and specific approximation errors by establishing the normalized mean square error function and the absolute error function, respectively. Finally, this paper proposes a new model for machine translation based on logarithmic position representation and self-attention mechanism. Through the experimental research, it can be seen that the Chinese-English machine translation model integrating attention mechanism and bidirectional neural network proposed in this paper has a good practical translation effect.
Fault Recognition Method Based on Attention Mechanism and the 3D-UNet
Authors: Yu Ting , Wang Xin , Chen Tong Jun , Ding Chang Wei
Published: April 2022

Oil and gas reservoirs are of great significance for economic benefits. Faults act as important conduits for transporting hydrocarbons and as essential sealing conditions. The location and morphology of faults reflect changes in the shape of the strata, so fault interpretation of seismic data has been an essential task in oil and gas exploration and development. The traditional fault identification method is time-consuming and inaccurate with large uncertainties. This paper proposed a fault recognition method based on 3D-UNet that added attention mechanisms to a convolutional neural network (CNN). This approach takes advantage of the UNet end-to-end architecture and the attention mechanism to focus on essential areas and suppress irrelevant information, allowing the model to focus on more valuable features. A fault identification network for seismic data was proposed by combining the 3D-UNet architecture with the Squeeze-and-Excitation (SE) attention mechanism. The 3D-UNet architecture comprises two parts: encoding and decoding, and the network architecture realizes end-to-end training. At the same time, SE was used to focus on the advantages of feature channels, further improving the accuracy of the network. The model performance was evaluated using the synthetic dataset provided by Wu. Experimental results show that the proposed model has better prediction performance in terms of accuracy, better recognition continuity, and richer fault detail.
Shall We Dance: Evolving Partnerships of Targeted Therapies for AML
Authors: Perl Alexander E. , Vyas Paresh
Published: April 2022

Two publications detailing the clinical outcomes of patients with acute myeloid leukemia and mutations in IDH1, IDH2, or FLT3 who received initial therapy with venetoclax and azacitidine provide new insights into risk stratification and set the stage for future trials integrating molecularly targeted therapy with this new standard regimen.

See related articles by Konopleva et al., p. 2744 and Pollyea et al., p. 2753
Methodological Quality of Fetal Brain Structure Charts for Screening Examination and Targeted Neurosonography: A Systematic Review
Authors: Di Mascio Daniele , Buca Danilo , Rizzo Giuseppe , Khalil Asma , Timor-Tritsch Ilan E. , Odibo Anthony , Mappa Ilenia , Flacco Maria Elena , Giancotti Antonella , Liberati Marco , D’Antonio Francesco
Published: April 2022

Introduction: Several fetal brain charts have been published in the literature and are commonly used in the daily clinical practice. However, the methodological quality of these charts has not been critically appraised. Material and Methods: MEDLINE, EMBASE, CINAHL, and the Web of Science databases were searched electronically up to December 31, 2020. The primary outcome was to evaluate the methodology of the studies assessing the growth of fetal brain structures throughout gestation. A list of 28 methodological quality criteria divided into three domains according to “study design,” “statistical and reporting methods,” and “specific relevant neurosonography aspects” was developed in order to assess the methodological appropriateness of the included studies. The overall quality score was defined as the sum of low risk of bias marks, with the range of possible scores being 0–28. This quality assessment was applied to each individual study reporting reference ranges for fetal brain structures. Furthermore, we performed a subgroup analysis according to the different brain structures (ventricular and periventricular, fore-brain and midbrain cerebral and posterior fossa). Results: Sixty studies were included in the systematic review. The overall mean quality score of the studies included in this review was 51.3%. When focusing on each of the assessed domains, the mean quality score was 53.7% for “study design,” 54.2% for “statistical and reporting methods,” and 38.6% for “specific relevant neurosonography aspects.” The sample size calculation, the correlation with a postnatal imaging evaluation, and the whole fetal brain assessment were the items at the highest risk of bias for each domain assessed, respectively. The subgroup analysis according to different anatomical location showed the lowest quality score for ventricular and periventricular structures and the highest for cortical structures. Conclusions: Most previously published studies reporting fetal brain charts suffer from poor methodology and are at high risk of biases, mostly when focusing on neurosonography issues. Further prospective longitudinal studies aiming at constructing specific growth charts for fetal brain structures should follow rigorous methodology to minimize the risk of biases, guarantee higher levels of reproducibility, and improve the standard of care.
Desidustat in Anemia due to Non-Dialysis-Dependent Chronic Kidney Disease: A Phase 3 Study (DREAM-ND)
Authors: Agrawal Dhananjai , Varade Deepak , Shah Hardik , Nazar Alm , Krishnan Jayakumar , Shukla Vineet , Ramakrishna Chinta , Bandara Galahitiyawa Mahel Chinthana , Mavani Sidhharth B. , Rajanna Sunil , Jikki Petkar , De Silva Shamila , Ruhela Vivek , Koradia Parshottam , Kansagra Kevinkumar , Kanani Pooja , Sharma Nitin , Zala Kuldipsinh , Parmar Deven ,   Study Investigator Group
Published: April 2022

Background: Desidustat, an oral hypoxia-inducible factor prolyl hydroxylase inhibitor, is being developed to treat anemia in patients with chronic kidney disease (CKD) without dialysis dependency. Methods: In total, 588 patients with a clinical diagnosis of anemia due to CKD without dialysis need and with baseline hemoglobin of 7.0–10.0 g/dL (inclusive) were randomized in a 1:1 ratio to receive either desidustat 100 mg oral tablets thrice a week for 24 weeks or biosimilar darbepoetin subcutaneous injection 0.75 μg/kg once in 2 weeks for 24 weeks. The primary outcome was the change from baseline in hemoglobin to evaluation period of Weeks 16–24. Key secondary outcomes included the number of patients with hemoglobin response, changes in the hepcidin levels, changes in the vascular endothelial growth factor (VEGF) levels, and changes in the lipid and lipoprotein profiles. Results: Hemoglobin change from baseline to Weeks 16–24 was 1.95 g/dL in the desidustat group and 1.83 g/dL in the darbepoetin group (difference: 0.11 g/dL; 95% CI: −0.12, 0.34), which met prespecified non-inferiority margin (−0.75 g/dL). The hemoglobin responders were significantly higher (p = 0.0181) in the desidustat group (196 [77.78%]) compared to the darbepoetin group (176 [68.48%]). The difference of change in hepcidin from baseline to Week 12 and Week 24 (p = 0.0032 at Week 12, p = 0.0016 at Week 24) and the difference of change in low-density lipoprotein from baseline to Week 24 (p value = 0.0269) between the two groups was statistically significant. The difference of change from baseline in VEGF to Weeks 12 and 24 between the two groups was not statistically significant. Conclusion: Desidustat is non-inferior to darbepoetin in the treatment of anemia due to non-dialysis dependent CKD and it is well-tolerated.