bluuebunny/crossref_metadata_2025 huggingface dataset:
The Set1 Histone H3K4 Methyltransferase Contributes to Azole Susceptibility in a Species-Specific Manner by Differentially Altering the Expression of Drug Efflux Pumps and the Ergosterol Gene Pathway
Authors: Baker Kortany M. , Hoda Smriti , Saha Debasmita , Gregor Justin B. , Georgescu Livia , Serratore Nina D. , Zhang Yueping , Cheng Lizhi , Lanman Nadia A. , Briggs Scott D.
Published: April 2022

Fungal infections are a major health concern because of limited antifungal drugs and development of drug resistance.
Candida
can develop azole drug resistance by overexpression of drug efflux pumps or mutating
ERG11
, the target of azoles. However, the role of epigenetic histone modifications in azole-induced gene expression and drug resistance is poorly understood in
Candida
glabrata
.
Precision Probiotics in Agroecosystems: Multiple Strategies of Native Soil Microbiotas for Conquering the Competitor Ralstonia solanacearum
Authors: Yin Jiakang , Zhang Ziliang , Guo Yixiong , Chen Yue , Xu Yang , Chen Wenxuan , Shao Yanan , Yu Youfeng , Zhu Lixia , Chen Lingling , Ruan Lifang
Published: April 2022

In the current work, the relationship between the soil microbiota and tomato bacterial wilt on a large scale offered us a comprehensive understanding of the disease. The delicate strategy of the microbiota in soil used for growing tomatoes to conquer the strong competitor, Rs, was revealed by microbiome research.
Supplemental calcium and vitamin D and long-term mortality in aortic stenosis
Authors: Kassis Nicholas , Hariri Essa H , Karrthik Antonette K , Ahuja Keerat R , Layoun Habib , Saad Anas M , Gad Mohamed M , Kaur Manpreet , Bazarbashi Najdat , Griffin Brian P , Popovic Zoran B , Harb Serge C , Desai Milind Y , Kapadia Samir R
Published: April 2022

Objective

Calcium metabolism has long been implicated in aortic stenosis (AS). Studies assessing the long-term safety of oral calcium and/or vitamin D in AS are scarce yet imperative given the rising use among an elderly population prone to deficiency. We sought to identify the associations between supplemental calcium and vitamin D with mortality and progression of AS.

Methods

In this retrospective longitudinal study, patients aged ≥60 years with mild-moderate native AS were selected from the Cleveland Clinic Echocardiography Database from 2008 to 2016 and followed until 2018. Groups were stratified into no supplementation, supplementation with vitamin D alone and supplementation with calcium±vitamin D. The primary outcomes were mortality (all-cause, cardiovascular (CV) and non-CV) and aortic valve replacement (AVR), and the secondary outcome was AS progression by aortic valve area and peak/mean gradients.

Results

Of 2657 patients (mean age 74 years, 42% women) followed over a median duration of 69 months, 1292 (49%) did not supplement, 332 (12%) took vitamin D alone and 1033 (39%) supplemented with calcium±vitamin D. Calcium±vitamin D supplementation was associated with a significantly higher risk of all-cause mortality (absolute rate (AR)=43.0/1000 person-years; HR=1.31, 95% CI (1.07 to 1.62); p=0.009), CV mortality (AR=13.7/1000 person-years; HR=2.0, 95% CI (1.31 to 3.07); p=0.001) and AVR (AR=88.2/1000 person-years; HR=1.48, 95% CI (1.24 to 1.78); p<0.001). Any supplementation was not associated with longitudinal change in AS parameters in a linear mixed-effects model.

Conclusions

Supplemental calcium with or without vitamin D is associated with lower survival and greater AVR in elderly patients with mild-moderate AS.
Wood ash amendments as a potential solution to widespread calcium decline in eastern Canadian forests
Authors: Kim Natalie , Watmough Shaun A. , Yan Norman D.
Published: April 2022

Decades of acidic deposition have depleted soil calcium (Ca) stocks over large areas of eastern Canada. The recovery of soil Ca levels has been limited despite substantial reductions in acidic deposition and will likely take many decades because rates of loss (owing to soil leaching, timber harvesting, and forest regeneration) may equal or exceed those of supply (via atmospheric input and natural mineral weathering). As low soil Ca levels may adversely affect local biota with relatively high Ca requirements, affected sites may benefit from supplementation with an alternative Ca source. A growing body of evidence suggests that the application of wood ash to Ca-deficient forest soils can help counteract the loss of Ca and other nutrients from the soil while boosting forest productivity. Yet the use of wood ash as a forest soil amendment is currently restricted in Canada, and the costs of obtaining permits and transporting/applying the ash make landfilling a more economically viable option. Here, we explore the potential of wood ash amendments in terms of their risks and benefits, dose and application frequency, time to see benefits, and longevity of benefits. After considering these topics in the context of Ca-deficient, acidified forest soils across eastern Canada, we propose that the potential benefits of ash amendments in these areas likely outweigh the risks. Future studies are needed to clarify both the short- and long-term effects of wood ash addition on different tree species in both natural and managed forests, as well as the potential benefits for carbon capture and implications for Ca-deficient aquatic ecosystems.
Covariant singularities: A brief review
Authors: Kuntz Iberê , Casadio Roberto , Kamenshchik Alexander
Published: April 2022

The Hawking–Penrose theorem is not covariant under field redefinitions. Should the invariance under such transformations be a true principle in nature, spacetime singularities become dubious objects. We here review the concept of covariant singularities, that is, singularities that are invariant under both spacetime diffeomorphisms and field redefinitions.
Heterogeneous willingness induced by different states promotes the evolution of cooperation
Authors: Zhang Fan , Wang Juan , Gao Hongyu , Li Xiaopeng , Xia Chengyi
Published: April 2022

In reality, the spread of strategies is often affected by individual willingness, which may be further influenced by the individual state. Herein, based on the spatial prisoner’s dilemma game, we propose a novel co-evolutionary model to explore the role of heterogeneous willingness induced by different states in the evolution of cooperation. In detail, we randomly set players in free or busy states, their states will remain constant throughout the evolution once being set. Within our model, the busy player has a quite small probability to teach one of his neighbors, and meanwhile, the busy neighbor will consider his learning willingness to imitate this strategy. However, the willingness of free player to teach or learn is not affected. Furthermore, we mainly discuss the impact of two different update schemes on the collective cooperation. For the Scheme I, the focal player [Formula: see text] will randomly select one of his neighbors [Formula: see text] as the teaching object, while for the Scheme II, the focal player [Formula: see text] tends to choose one of his neighbors with the lower payoff to teach his strategy. Through lots of numerical simulations, we find that there exists an optimal parameter [Formula: see text] and [Formula: see text] to promote the evolution of cooperation most effectively. In addition, the Scheme II performs better in alleviating social dilemmas.
Multi-Content Merging Network Based on Focal Loss and Convolutional Block Attention in Hyperspectral Image Classification
Authors: Yang Lina , Zhang Fengqi , Wang Patrick Shen-Pei , Li Xichun , Luo Huiwu
Published: April 2022

Simultaneous extraction of spectral and spatial features and their fusion is currently a popular solution in hyperspectral image (HSI) classification. It has achieved satisfactory results in some research. Because the scales of objects are often different in HSI, it is necessary to extract multi-scale features. However, this aspect was not taken into account in many spectral-spatial feature fusion methods. This causes the model to be unable to get sufficient features on scales with a large difference range. The model (MCMN: Multi-Content Merging Network) proposed in this paper designs a multi-branch fusion structure to extract multi-scale spatial features by using multiple dilated convolution kernels. Considering the interference of the surrounding heterogeneous objects, the useful information from different directions is also fused together to realize the merging of multiple regional features. MCMN introduces a convolution block attention mechanism, which fully extracts attention features in both spatial and spectral directions, so that the network can focus on more useful parts, which can effectively improve the performance of the model. In addition, since the number of objects in each class is often discrepant, it will have some impact on the training process. We apply the focal loss function to eliminate the negative factor. The experimental results of MCMN on three data sets have a breakthrough compared with the other comparison models, which highlights the role of MCMN structure.
Inverse Representation Inspired Multi-Resolution Dictionary Learning Method for Face Recognition
Authors: Yan Chunman , Zhang Yuyao
Published: April 2022

Face recognition is widely used and is one of the most challenging tasks in computer vision. In recent years, many face recognition methods based on dictionary learning have been proposed. However, most methods only focus on the resolution of the original image, and the change of resolution may affect the recognition results when dealing with practical problems. Aiming at the above problems, a method of multi-resolution dictionary learning combined with sample reverse representation is proposed and applied to face recognition. First, the dictionaries associated with multiple resolution images are learnt to obtain the first representation error. Then different auxiliary samples are generated for each test sample, and a dictionary consisted of test sample, auxiliary samples, and other classes of training samples is established to sequentially represent all training samples at this resolution, and to obtain the second representation error. Finally, a weighted fusion scheme is used to obtain the ultimate classification result. Experimental results on four widely used face datasets show that the proposed method achieves better performance and is effective for resolution change.
Dynamical Analysis of Border Collision Bifurcation in One-Cycle Controlled Single-Inductor Dual-Output Boost DC-DC Converters
Authors: Zhang Hao , Ren Han , Cui Donggang
Published: April 2022

In this paper, the evolution of three types of border collision bifurcations are investigated thoroughly for a high-order single-inductor double-output DC-DC converter with one-cycle control (OCC). Multi-piecewise inductor current with more than one border and cross-coupling effect between different output ports are prominent features of the system, resulting in unexpected period incrementing cascade and transition from periodicity to chaos as a parameter is varied. A high-order nonlinear iterative map model is proposed, which reveals the underlying mechanisms of those complicated nonstandard and unsmooth bifurcations in the multiple-output system. Additionally, theoretical analysis shows that those nonstandard bifurcations arise from the boundary collisions, and more specifically, a fixed point suddenly appears in the borderline between various regions. Besides eigenvalue loci changes abruptly at the bifurcation point, the characteristic of border collision bifurcation is the topology sequence changes. Furthermore, the parameter boundaries are given to provide useful guidance for design. Finally, PSpice circuit experiments are performed to verify the above theoretical and numerical results.
Complex Network Dynamics of Multiscroll Chaotic Attractors and Their Output-Feedback Pinning Synchronization
Authors: Lv Yuezu , Gao Zhen , Fu Ying , Wen Guanghui , Ogorzałek Maciej
Published: April 2022

The saturated function series have been successfully used to generate multiscroll chaotic attractors. In this paper, we revisit multiscroll chaotic attractors via saturated function series. We find that with a small constant drift acting on the saturated function, the number of scrolls will greatly decrease. This phenomenon brings extra difficulty in pinning synchronization of networked multiscroll chaotic attractors. A new output-feedback pinning controller is designed based on the unknown-input distributed observer to estimate the synchronization error, which has the advantage of saving the communication cost as the transmission of the observer information is not needed.
Frequency Map Analysis of Spatiotemporal Chaos in the Nonlinear Disordered Klein–Gordon Lattice
Authors: Skokos Charalampos , Gerlach Enrico , Flach Sergej
Published: April 2022

We study the characteristics of chaos evolution of initially localized energy excitations in the one-dimensional nonlinear disordered Klein–Gordon lattice of anharmonic oscillators, by computing the time variation of the fundamental frequencies of the motion of each oscillator. We focus our attention on the dynamics of the so-called “weak chaos” and “strong chaos” spreading regimes [Laptyeva et al., 2010], for which Anderson localization is destroyed, as the initially restricted excitation at the central region of the lattice propagates in time to more lattice sites. Based on the fact that large variations of the fundamental frequencies denote strong chaotic behavior, we show that in both regimes chaos is more intense at the central regions of the wave packet, where also the energy content is higher. On the other hand, the oscillators at the wave packet’s edges, through which the energy propagation happens, exhibit regular motion up until the time they gain enough energy to become part of the highly excited portion of the wave packet. Eventually, the percentage of chaotic oscillators remains practically constant, despite the fact that the number of excited sites grows as the wave packet spreads, but the portion of highly chaotic sites decreases in time. Thus, albeit the number of chaotic oscillators is constantly growing the strength of their chaotic behavior decreases, indicating that although chaos persists it is becoming weaker in time. We show that the extent of the zones of regular motion at the edges of the wave packet in the strong chaos regime is much smaller than in the weak chaos case. Furthermore, we find that in the strong chaos regime the chaotic component of the wave packet is not only more extended than in the weak chaos one, but in addition the fraction of strongly chaotic oscillators is much higher. Another important difference between the weak and strong chaos regimes is that in the latter case a significantly larger number of frequencies is excited, even from the first stages of the evolution. Moreover, our computations confirmed the shifting of fundamental frequencies outside the normal mode frequency band of the linear system in the case of the so-called “selftrapping” regime where a large part of the wave packet remains localized.
A Constructive Heuristic for Automated Parallel Tests Assembly
Authors: Ignjatović Miroslava M. , Tartalja Igor I.
Published: April 2022

Parallel tests contain different items but have the same measurement properties. They are administered at the same or different time slots and their measurement results must be comparable. The problem of automated parallel tests assembly is studied for a long time, and many (mostly improvement) heuristic solutions are proposed and elaborated in literature. Such approaches frequently suffer from algorithms of unpredictable execution time, forcing the methods to terminate execution when some time limit or solution quality is reached. This paper proposes an efficient method of polynomial complexity, as a complete solution to the automated parallel tests assembly problem. The method uses the idea of Nawaz, Enscore, and Ham constructive heuristic algorithm to reduce the number of examined permutations, originally exploited for solving the permutation flow-shop sequencing problem. We compared the experimental results of the proposed method with two methods based on improvement heuristics that solve the same problem formulation, simulated annealing and variable neighborhood search. The main advantages of the proposed method are predictable execution time and implementation simplicity. Achieved quality of assembled tests, combined with predictable test assembly execution time, may be of particular interest in cases when computational resources for test assembly and administering are overloaded.
Vortex-Induced Vibration Control of Long Stay Cables by Using Inerter-Based Dampers
Authors: Xu Kun , Song Junyan , Bi Kaiming
Published: April 2022

With the increment of cable length, long stay cables are prone to experience high-mode vortex-induced vibrations (VIVs) at normal wind velocities, and the VIV-prone mode range also becomes wider. Existing dampers cannot supply sufficient damping for such a wide range of high-modes. Inerter-based dampers (IBDs), which take advantage of the two-terminal inertial device dubbed inerter, have been proved to have a better control performance than conventional dampers. However, existing studies on IBDs only focused on the first several cable modes, which cannot cover the wide range of VIV-prone modes of long stay cables. The high-mode and multi-mode VIV control by using IBDs is investigated in this study. The governing equations of the cable-IBD systems under VIV are first established. The control efficiency of the IBDs and the influence of optimum design strategies are compared. The use of two IBDs to further enhance the control efficiency is also discussed. The results show that a three-element IBD in this study is quite effective for high-mode and multi-mode VIV control of long stay cables. Moreover, the multi-mode control efficiency can be further improved through using two IBDs. The results in this study can guide the design of IBDs for VIV control of long stay cables.
Amplitude equations for SPDEs driven by fractional additive noise with small hurst parameter
Authors: Blömker Dirk , Neamţu Alexandra
Published: April 2022

We study stochastic partial differential equations (SPDEs) with potentially very rough fractional noise with Hurst parameter [Formula: see text]. Close to a change of stability measured with a small parameter [Formula: see text], we rely on the natural separation of time-scales and establish a simplified description of the essential dynamics. Up to an error term bounded by a power of [Formula: see text] depending on the Hurst parameter we can approximate the solution of the SPDE in first order by an SDE, the so-called amplitude equation, which describes the amplitude of the dominating pattern changing stability. In second order the approximation is given by a fast infinite-dimensional Ornstein–Uhlenbeck process. To this aim, we need to establish an explicit averaging result for stochastic integrals driven by rough fractional noise for small Hurst parameters.
Multi-Response Optimisation of Process Parameters in Pocket Milling Using Artificial Neural Networks and Genetic Algorithms
Authors: Rajyalakshmi M. , Venkateswara Rao M.
Published: April 2022

In the plastic industry for mold making, pocket milling is applied. The surface finish of the mold affects the quality of the plastic product, especially for toys. This can be achieved by minimising the surface roughness of the mold. To get a good quality product with a better production rate, the selection of the best combination of parameters in pocket milling is necessary. Multi-response optimisation can be applied for selecting such parameters which are suited for fulfilling the objective. In this study, one of the toy mold designs is selected as a pocket profile on which, two tool trajectories, viz Follow Periphery (FP) and Zigzag (ZZ), are applied for generation of pocket by varying Speed (S), Feed (F) and Step Over (SO). Box–Behnken Response Surface Methodology is applied to find the experimental runs. Two conflicting objectives minimising Surface Roughness (SR) and maximising Material Removal Rate (MRR) are obtained by applying Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithm (MOGA). Conformational experiments were conducted for the random set of Pareto results obtained from MOGA for both the tool trajectories to validate the model. From the analysis, it is observed that the FP tool path strategy is well suited to generate the pocket to get minimum SR and maximum MRR as the error percentage between the predicted and test results observed is 0.8085% for SR and 0.9236% for MRR.
Denoising of scanning electron microscope images for biological ultrastructure enhancement
Authors: Chang Sheng , Shen Lijun , Li Linlin , Chen Xi , Han Hua
Published: April 2022

Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. Source code is available on GitHub ( https://github.com/VictorCSheng/VSID-Net ).
Computing the lowest eigenstate of tight-binding Hamiltonians using quantum walks
Authors: Varsamis Georgios D. , Karafyllidis Ioannis G.
Published: April 2022

Finding or estimating the lowest eigenstate of quantum system Hamiltonians is an important problem for quantum computing, quantum physics, quantum chemistry, and material science. Several quantum computing approaches have been developed to address this problem. The most frequently used method is variational quantum eigensolver (VQE). Many quantum systems, and especially nanomaterials, are described using tight-binding Hamiltonians, but until now no quantum computation method has been developed to find the lowest eigenvalue of these specific, but very important, Hamiltonians. We address the problem of finding the lowest eigenstate of tight-binding Hamiltonians using quantum walks. Quantum walks is a universal model of quantum computation equivalent to the quantum gate model. Furthermore, quantum walks can be mapped to quantum circuits comprising qubits, quantum registers, and quantum gates and, consequently, executed on quantum computers. In our approach, probability distributions, derived from wave function probability amplitudes, enter our quantum algorithm as potential distributions in the space where the quantum walk evolves. Our results showed the quantum walker localization in the case of the lowest eigenvalue is distinctive and characteristic of this state. Our approach will be a valuable computation tool for studying quantum systems described by tight-binding Hamiltonians.
Curcumin/Graphene Quantum Dot-Loaded Bacterial Nanocellulose Platform for Drug Delivery and Wound Dressing
Authors: Ebadati Arefeh , Ghalandari Behafarid , Hasanzadeh Akbar , Karimi Mahdi
Published: April 2022

The present study outlines a straightforward approach for designing a novel drug delivery system based on bacterial nanocellulose composites containing curcumin-loaded graphene quantum dots (BNC/CUR/GQDs) for antibacterial and wound healing applications. The nanocomposite was made of interconnected plates with uniform thicknesses around 2[Formula: see text]mm. The scanning electron microscope (SEM) image of the prepared BNC nanocomposite showed a uniform and porous morphology composed of the microfibrils having an average diameter of 120[Formula: see text]nm, which contributes to both drug inclusion and drug release in a controllable fashion. The designed system biosynthesized by Acetobacter xylinum demonstrated an optimum drug loading capacity and controlled release profile. The drug loading content and drug release efficiency were calculated around 31% and 61%. Agar diffusion test indicated that the introduction of GQDs into the BNC matrix conspicuously improved the growth inhibition of bacteria, and gram-negative and gram-positive bacterial strains were measured 21.6[Formula: see text]mm and 21.5[Formula: see text]mm, respectively. The cell viability of 92.3% was obtained for the BNC, while the cell viability of the designed system was measured at around 88.07%. Consequently, the incorporation of curcumin-loaded graphene quantum dots into bacterial nanocellulose matrices can open up a new insight into the production of high-performance wound dressing supplies.
In Situ Reduction of Gold Nanoparticle-Decorated Ti3C2 MXene for Ultrasensitive Electrochemical Detection of MicroRNA-21 with a Cascaded Signal Amplification Strategy
Authors: Yang Xiao , Zhao Linlin , Lu Lin , Feng Minghui , Xia Jianfei , Zhang Feifei , Wang Zonghua
Published: April 2022

Sensitive detection of miRNA-21 provides remarkable results for the diagnosis of early breast cancer. Here, we fabricated a novel electrochemical biosensor for the ultrasensitive detection of miRNA-21 via in situ reduction of gold nanoparticles (AuNPs)-decorated Ti3C2 MXene, combined with a cascaded signal amplification strategy, that is, strand displacement of DNA walker-induced multi-DNA (product DNA) release and cyclic enzymatic signal amplification. First, in situ reduction of AuNPs on a Ti3C2 MXene layer was not only used as a carrier of capture DNA (C-DNA) and a way to make DNA hybridization accessible but also to provide a predominant (111) facet with high electrocatalytic activity of AuNPs that significantly ameliorated the electrochemical signal, in which MXene acted as both reductant and stabilizer. In addition, in the presence of miRNA-21, multi-DNA fragments (p-DNA) that were produced by strand displacement of the DNA walker could be captured by C-DNA on the fabricated biosensor and trigger Exo III cyclic digestion for further amplification of electrochemical signals. By cascaded signal amplification, the changes in peak signal currents (ΔI) using differential pulse voltammetry were amplified. Under optimal conditions, the electrochemical biosensor achieved a detection limit of 50 aM (S/N = 3) with a linear range from 100 aM to 1.0 nM. With its excellent analytical performance, this biosensor may have the potential to be used in early diagnosis and biomedical applications.
The Global Burden of Leukemia and Its Attributable Factors in 204 Countries and Territories: Findings from the Global Burden of Disease 2019 Study and Projections to 2030
Authors: Du Mengbao , Chen Weiwei , Liu Ke , Wang Limengmeng , Hu Yihan , Mao Yingying , Sun Xiaohui , Luo Yi , Shi Jimin , Shao Keding , Huang He , Ye Ding
Published: April 2022

Background. Leukemia is a common malignancy that has four main subtypes and is a threat to human health. Understanding the epidemiological status of leukemia and its four main subtypes globally is important for allocating appropriate resources, guiding clinical practice, and furthering scientific research. Methods. Average annual percentage changes (AAPCs) were calculated to estimate the change trends of age-standardized rates (ASRs) from 1990 to 2019 in 204 countries and territories. The risk factors for leukemia death and disability-adjusted life-year (DALY) were also analyzed. In addition, the future trends in ASRs were projected through 2030. Results. The total number of incident cases, deaths, and DALYs from leukemia in 2019 was 0.64, 0.33, and 11.66 million, respectively. Decreasing trends in age-standardized incidence rate (ASIR), the age-standardized death rate (ASDR), and age-standardized DALY rate were detected on a global level while increasing trends in ASIR were detected in the high-sociodemographic index (SDI) regions. The leukemia burden was heavier in males than in females. By cause, acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL) were more likely to impose a burden on the elderly, while acute lymphoblastic leukemia (ALL) showed a greater impact in the younger population. A significant positive correlation was observed between SDI and AAPC in ASIR, while SDI was negatively correlated with AAPCs in both ASDR and age-standardized DALY rate. Smoking remained the most significant risk factor associated with leukemia-related death and DALY, especially in males. Similar deaths and DALYs were caused by smoking and high body mass index (BMI) in females. Future projections through 2030 estimated that ASIR and ASDR will continue to increase, while the DALY rate is predicted to decline. Conclusions. Patterns and trends of leukemia burden are correlated with SDI. The estimated contributions to leukemia deaths indicate that timely measures are needed to reduce smoking and obesity.
An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network
Authors: Wang Tichun , Wang Jiayun
Published: April 2022

In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the ability to quickly and fully learn boundary information in small samples, the extension neural network-type 2 algorithm (ENN-2) has the potential in imbalanced data clustering and has been gradually applied in fault diagnosis. Therefore, in order to improve the unstable clustering performance of ENN-2 caused by its heavy dependence on input order of samples, a novel algorithm called linked extension neural network (LENN) is developed by redesigning the correlation function and its iterative method, which greatly reduces the clustering iteration epochs of the algorithm. In addition, an evaluation index of clustering quality for this novel algorithm, extension density, is also proposed. After that, a bearing fault diagnosis model of variational mode decomposition (VMD) based denoising and LENN is proposed. Firstly, VMD is used to get intrinsic mode functions (IMFs), and the correlation coefficients of IMFs are calculated for signal denoising. Secondly, the features are extracted from denoised signals and selected by PCA algorithm, and the fault diagnosis is finally completed by LENN. Compared with ENN-2, K-means, FCM, and DBSCAN based models, the proposed model identifies the faults with different severities more accurately and achieves superior diagnostic ability on different imbalance degrees of datasets, which can further lay a foundation for clustering fault diagnosis based on vibration signals.
A Comprehensive Prognostic and Immune Analysis of Ferroptosis-Related Genes Identifies SLC7A11 as a Novel Prognostic Biomarker in Lung Adenocarcinoma
Authors: Qian Li , Wang Fu , Lu Shu-min , Miao Hua-jie , He Xin , Feng Jia , Huang Hua , Shi Rong-feng , Zhang Jian-guo
Published: April 2022

Lung adenocarcinoma (LUAD) is still one of the illnesses with the greatest mortality and morbidity. As a recently identified mode of cellular death, the activation of ferroptosis may promote the effectiveness of antitumor therapies in several types of tumors. However, the expression and clinical significance of Ferroptosis-associated genes in LUAD are still elusive. The RNA sequencing data of LUAD and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) datasets. Subsequently, potential prognostic biomarkers were determined by the use of biological information technology. The R software package “ggalluvial” was applied to structure Sanguini diagram. Herein, our team screened 14 dysregulated ferroptosis-associated genes in LUAD. Among them, only four genes were associated with clinical outcome of LUAD patients, including ATP5MC3, FANCD2, GLS2, and SLC7A11. In addition, we found that high SLC7A11 expression predicted an advanced clinical progression in LUAD patients. Additionally, 8 immune checkpoint genes and 7 immune cells for LUAD were recognized to be related to the expression of SLC7A11. KEGG assays indicated that high expression of SLC7A11 might participate in the modulation of intestinal immune network for IgA generation and Staphylococcus aureus infection. Overall, our findings revealed that SLC7A11 might become a potentially diagnostic biomarker and SLC7A11 might serve as an independent prognosis indicator for LUAD.
Research Progress in Environmental Response of Fiber Concrete and Its Functional Mechanisms
Authors: Ma Yanxuan , Yu Xia , Zhao Fei , Liu Jin , Zhu Pengfei , Zhang Peng , Zhang Jian , Wu Lei
Published: April 2022

Smart fiber reinforced concretes (FRC) are good in intelligent environmental response. With the smart FRC being used in buildings such as bridges and tunnels, the real-time online distributed monitoring of concrete state can be realized. It provides an excellent solution to reduce the emergencies caused by the accumulation of damages, resistance attenuation, etc. This paper analyzes smart FRC’s self-sensing and self-healing response characteristics under different environmental response fields. Furthermore, the intelligent response mechanism of smart carbon fiber reinforced concrete (CFRC), optical fiber reinforced concrete (OFRC), glass fiber reinforced concrete (GFRC), and nanofiber reinforced concrete (NFRC) would be summarized. The response fields include the stress field, temperature field, electromagnetic field, chemical field, and humidity field, among which the field response mechanism of smart NFRC is classified and summarized. Finally, the advantages and disadvantages, the intelligent response parameters, and the applications of smart FRC under different environmental fields are compared.
Mathematical Model and Algorithm of Multi-Index Transportation Problem in the Background of Artificial Intelligence
Authors: Cao Junfang
Published: April 2022

The development of artificial intelligence has brought rapid changes to human life and brought great convenience to human activities. The development of various modes of transportation has also brought convenience to people’s travel and commodity transactions, but it has also added more issues that need to be carefully considered. Because of the diversification of transportation methods, transportation problems also arise many fields, such as air transportation, water transportation, and land transportation. The development of mathematical models and algorithms for transportation problems is also in full swing, and it is a major trend to introduce mathematical models and algorithms into the solution of transportation problems. This paper deals with the multi-index transportation problem by establishing a multi-index mathematical model and algorithm to find a scientific transportation method for the goods to be transported, so as to save the cost and time of transportation. Experiments show that the mathematical model established in this paper has high efficiency for solving the multi-index transportation problem. At the same time, the most suitable transportation method can also be selected for the transportation of goods, and the route planned by the mathematical model and algorithm can reduce the risk to 12.34%.
Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework
Authors: Shah Syed Muhammad Saqlain , Farooq Rehan , Alharbi Abdullah , Alyami Hashem , Zada Islam , Shah Faiz Ali
Published: April 2022

It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.
RGS6 Drives Spinal Cord Injury by Inhibiting AMPK Pathway in Mice
Authors: Dao Wenxin , Xiao Zhe , Yang Weize , Luo Xiaomin , Xia Hongxia , Lu Zuneng
Published: April 2022

Objective. Oxidative stress and inflammation play critical roles in the pathogenesis of spinal cord injury (SCI). Regulator of G protein signaling 6 (RGS6) is involved in controlling ROS generation and inflammatory response under different contexts. This study is aimed at investigating its role and underlying mechanism in SCI. Methods. Contusive SCI mouse models were generated, and lentiviral vectors were injected to silence or overexpress RGS6 in the spinal cord. To inhibit AMP-activated protein kinase (AMPK) activity, SCI mice were intraperitoneally injected with compound C (20 mg/kg) every two days. Oxidative and inflammatory markers were detected. Results. Spinal RGS6 expression was elevated upon SCI stimulation. RGS6 knockdown suppressed, while RGS6 overexpression aggravated oxidative stress, inflammation, and SCI in mice. Mechanistically, RGS6 elevation during SCI deactivated AMPK pathway, thereby exacerbating oxidative stress and inflammation in SCI mice. Conclusion. RGS6 is required for the initiation and progression of SCI, and knocking down RGS6 may provide promising therapeutic strategies for SCI patients.
Optimized Embedded Healthcare Industry Model with Lightweight Computing Using Wireless Body Area Network
Authors: Saba Tanzila , Rehman Amjad , Haseeb Khalid , Bahaj Saeed Ali , Lloret Jaime
Published: April 2022

Wireless technology is offering numerous growth to develop communication systems. The Internet of Things (IoT) is combined with the sensing ecosystem to transfer and process the physical environment. Recently, IoT devices have collaborated with wireless devices to improve embedded medical applications. Many solutions are proposed to decrease the power consumption of the sensing ecosystem and support the health industry. However, optimizing the transformation of collected data with lightweight power consumption is still a burning research issue. Moreover, uncontrolled network devices and healthcare professionals are remotely accessed by such embedded systems. Thus, securing sensitive information is also a significant factor for mobile communications. Therefore, this research presents an optimized embedded healthcare industry model with lightweight computing using a wireless body area network (WBAN), aiming to lessen the control overheads and improve the power consumption in mobile e-health services. To begin, it employs an optimal learning algorithm to lower the management costs of embedded systems in order to transform and administer the electronic health record (EHR) more efficiently. Second, with the help of trustworthy gateways, it delivers a safe EHR algorithm as well as lightweight computing resources for embedded systems. The proposed model is tested with a variety of experiments and demonstrates its significant improvement over state-of-the-art techniques.
An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques
Authors: Albishry Nabeel , AlGhamdi Rayed , Almalawi Abdulmohsen , Khan Asif Irshad , Kshirsagar Pravin R. , BaruDebtera
Published: April 2022

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.
RW-Fuzzer: A Fuzzing Method for Vulnerability Mining on Router Web Interface
Authors: Wang Longjuan , Cao Chunjie , Ye Jun , Zhong Wenjie
Published: April 2022

As the main routing device of the network, most routers can be set up and managed through their web enabled admin portal. This paper proposes a new method for router admin portal vulnerability mining fuzzing test (RW-fuzzer: Router Web fuzzer). The mutation samples that generated by Boofuzz are used to construct the test sample set for fuzzy testing. The constructed mutation test cases are more suitable for the attack load or critical value of the router’s Web interface. They can cause unexpected errors for the devices more easily, which achieves the goal of discover potential vulnerabilities, and the practicality is excellent. Based on the proposed RW-fuzzer method, this work conducted fuzzing tests on 4 widely sold router models from manufacturers. Four nday vulnerabilities and one 0day vulnerability have been found. Experiment results show that the proposed RW-fuzzer method is effective.
The Application of Literary Maps to the Teaching of English and American Literature in the Age of Big Data and the Internet of Things
Authors: Chen Binfeng
Published: April 2022

The concept of the Internet of Things has had a dramatic impact on the traditional way of life of human beings since its birth. This shock has almost spread to education, medical, and other fields. It is reflected in the teaching process of English and American literature in colleges and universities, which greatly enhances the vividness of teaching and makes rigid knowledge memory more vivid and specific. Literary maps, as an important entry point for regional research on the development of literature, are playing an increasingly important role in the teaching of English and American literature. This paper studies the role of literary maps in British and American literature in order to improve students’ ability to study British and American literature and promote the development of teaching. This paper mainly uses the experimental method and the investigation method to analyze this in detail and gets the concrete result. The data shows that literary maps play a certain role in structure, positioning, and empathy. All aspects are supported by nearly 25%. Therefore, the study of literary maps still has a significant impact on the teaching of English and American literature.
Estimating the Number of COVID-19 Cases and Impact of New COVID-19 Variants and Vaccination on the Population in Kerman, Iran: A Mathematical Modeling Study
Authors: Nakhaeizadeh Mehran , Chegeni Maryam , Adhami Masoumeh , Sharifi Hamid , Gohari Milad Ahmadi , Iranpour Abedin , Azizian Mahdieh , Mashinchi Mashaallah , Baneshi Mohammad Reza , Karamouzian Mohammad , Haghdoost Ali Akbar , Jahani Yunes
Published: April 2022

COVID-19 is spreading all over Iran, and Kerman is one of the most affected cities. We conducted this study to predict COVID-19-related deaths, hospitalization, and infected cases under different scenarios (scenarios A, B, and C) by 31 December 2021 in Kerman. We also aimed to assess the impact of new COVID-19 variants and vaccination on the total number of COVID-19 cases, deaths, and hospitalizations (scenarios D, E, and F) using the modified susceptible-exposed-infected-removed (SEIR) model. We calibrated the model using deaths reported from the start of the epidemic to August 30, 2021. A Monte Carlo Markov Chain (MCMC) uncertainty analysis was used to estimate 95% uncertainty intervals (UI). We also calculated the time-varying reproductive number (

R

t

) following time-dependent methods. Under the worst-case scenario (scenario A;

contact
 
rate
=
10

,

self
‐
isolation
 
rate
=
30
%

, and average vaccination shots per

day
=
5,000

), the total number of infections by December 31, 2021, would be 1,625,000 (95% UI: 1,112,000–1,898,000) with 6,700 deaths (95% UI: 5,200–8,700). With the presence of alpha and delta variants without vaccine (scenario D), the total number of infected cases and the death toll were estimated to be 957,000 (95% UI: 208,000–1,463,000) and 4,500 (95% UI: 1,500–7,000), respectively. If 70% of the population were vaccinated when the alpha variant was dominant (scenario E), the total number of infected cases and deaths would be 608,000 (95% UI: 122,000–743,000) and 2,700 (95% UI: 700–4,000), respectively. The

R

t

was ≥1 almost every day during the epidemic. Our results suggest that policymakers should concentrate on improving vaccination and interventions, such as reducing social contacts, stricter limitations for gathering, public education to promote social distancing, incensing case finding and contact tracing, effective isolation, and quarantine to prevent more COVID-19 cases, hospitalizations, and deaths in Kerman.