bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Algorithms for Right-sizing Data Centers
Authors: Albers Susanne , Quedenfeld Jens
Published: October 2022

Electricity cost is a dominant and rapidly growing expense in data centers. Unfortunately, much of the consumed energy is wasted, because servers are idle for extended periods of time. We study a capacity management problem that dynamically right-sizes a data center, matching the number of active servers with the varying demand for computing capacity. We resort to a data-center optimization problem introduced by Lin, Wierman, Andrew, and Thereska [
25
,
27
] that, over a time horizon, minimizes a combined objective function consisting of operating cost, modeled by a sequence of convex functions, and server switching cost. All prior work addresses a continuous setting in which the number of active servers, at any time, may take a fractional value.

In this article, we investigate for the first time the discrete data-center optimization problem where the number of active servers, at any time, must be integer valued. Thereby, we seek truly feasible solutions. First, we show that the offline problem can be solved in polynomial time. Our algorithm relies on a new, yet intuitive graph theoretic model of the optimization problem and performs binary search in a layered graph. Second, we study the online problem and extend the algorithm
Lazy Capacity Provisioning
(LCP) by Lin et al. [
25
,
27
] to the discrete setting. We prove that LCP is 3-competitive. Moreover, we show that no deterministic online algorithm can achieve a competitive ratio smaller than 3. Hence, while LCP does not attain an optimal competitiveness in the continuous setting, it does so in the discrete problem examined here. We prove that the lower bound of 3 also holds in a problem variant with more restricted operating cost functions, introduced by Lin et al. [
25
].

In addition, we develop a randomized online algorithm that is 2-competitive against an oblivious adversary. It is based on the algorithm of Bansal et al. [
7
] (a deterministic, 2-competitive algorithm for the continuous setting) and uses randomized rounding to obtain an integral solution. Moreover, we prove that 2 is a lower bound for the competitive ratio of randomized online algorithms, so our algorithm is optimal. We prove that the lower bound still holds for the more restricted model.

Finally, we address the continuous setting and give a lower bound of 2 on the best competitiveness of online algorithms. This matches an upper bound by Bansal et al. [
7
]. A lower bound of 2 was also shown by Antoniadis and Schewior [
4
]. We develop an independent proof that extends to the scenario with more restricted operating cost.
Electrochemical Sensing of Nitrofurazone and Semicarbazide on an Au-Ag Film Fabricating from a Deep Eutectic Solvent
Authors: Yeh Min-Yu , Liu Yan-Ru , Huang Tsu-Hsuan , Chen Yun-Hsuan , Hsieh Yi-Ting
Published: October 2022

A facile electrochemical sensor is reported for the highly selective and sensitive detection of nitrofurazone (NFZ) and its metabolites, semicarbazide (SC), by using an Au-Ag modified electrode. The Au-Ag film was produced on a Cu foil by electrodeposition in a choline chloride-urea-glycerol ternary deep eutectic solvent containing gold (III) ions and silver (I) ions. Cyclic voltammetry (CV) indicates that the co-deposition of Au-Ag would be feasible. The morphology, crystallinity, and composition of Au-Ag deposits were characterized via scanning electron microscopy (SEM), powder X-ray diffractometer, and X-ray photoelectron spectroscopy (XPS). Under the optimal condition, the Au-Ag modified electrode shows a wider linear concentration range of 1.99–643.49 μM with a low detection limit of 0.2 μM for NFZ. The corresponding linear range of SC is 7.94 to 482 μM, and the detection limit is 1.01 μM. It also demonstrated good anti-interference ability against various common interfering substances. Finally, the modified electrode was successfully applied in the real samples, and the average recoveries for NFZ and SC were 100.59% and 101.01%, respectively. The present electrochemical strategy is promising to broaden the practical electrochemical application in the environment.
The Evolution Tracking of Tribasic Lead Sulfates Features in Lead-Acid Battery Positive Electrode using Design of Experiments
Authors: Jhabli Oussama , El Mouchtari El Mountassir , Boutamart Mustapha , Rafqah Salah , Redouany Yassine , Bouhmmad Adil , Nouneh Khalid , Briche Samir
Published: October 2022

The positive electrode of lead-acid battery (LAB) still limits battery performance. Several approaches have been attempted to remedy this problem either with the incorporation of additives or by electrode modification. However initial performance and cycling of the LAB is determined by the kind and content of basic lead sulfate in the paste. As a result, it is critical to comprehend the mechanisms that take place during paste manufacture and how they affect the properties of the active materials. In this work, the experimental design methodology was applied to track the crystallite size and content of tribasic lead sulfates (3BS), and specific surface area of cured paste, using the AZURAD® software. The effect of two quantitative factors (water/LO ratio and acid/LO ratio) and one qualitative factor (curing program) were studied. SEM, XRD and BET were used to characterize the cured pastes. The acid/LO ratio plays an important role in determining the 3BS phase content reaching 78% for crystalline phase. At high temperatures, the acid/LO ratio shows, more influence on the 3BS crystallites size attaining a maximum value of 58 nm. The temperature in the curing step mitigates the effect of both ratios on the crystallites size of 3BS and SSA of cured paste. Results reveals two zones of nanostructured paste material evolution delimited at 10% of acid/LO ratio.
Structural Evolution of Au Electrodes during Cathodic Corrosion: Initial Stages of Octahedral-Nanocrystal Growth
Authors: Elnagar Mohamed M. , Kibler Ludwig A. , Jacob Timo
Published: October 2022

Cathodic corrosion of Au wires in highly concentrated NaOH (5–20 M) is systemically investigated to monitor the structural transformation as a function of applied electrode potential, polarization time, and electrolyte concentration. Current–potential curves for the restructured Au electrodes in 0.1 M H2SO4 demonstrate preferential (111)-orientation of nanofacets and an increase in electrochemically active surface area as a function of the applied parameters. SEM shows that polarizing the Au surfaces at sufficiently negative potentials leads to the growth of nanosized octahedral and triangular crystallites that can be tailored by varying NaOH concentrations. The nanocrystal growth occurs through dissolution and metal redeposition. The formation of etching pits indicates that Au dissolution is dominating during the initial stages of cathodic corrosion. The number of nanocrystals increases at more negative potentials due to the greater nucleation density induced by the higher concentration of dissolved Au species. Polarization times above 5.0 min result in fractal growth indicated by the appearance of multi-armed nanostructures. This work not only advances our understanding of the initial stages of nanocrystal formation but also offers a powerful means for controlling the shape and size of metal nanocrystals simply by the rational design of the cathodic corrosion.
Effects of Quadruple Therapy Combined with Probiotics on Helicobacter Pylori-Related Peptic Ulcer
Authors: Zhou Ye , Li Tingzan
Published: October 2022

The present study was designed to observe the effect of quadruple therapy combined with probiotics on Helicobacter pylori-related peptic ulcer. The patients in the control group (

n
=
90

) were given regular quadruple therapy including proton pump inhibitor ilaprazole enteric-coated tablet + two antibiotics amoxicillin dispersible tablet and metronidazole tablet + colloidal bismuth pectin capsule for 2 weeks. Patients in the study group (

n
=
90

) were given abovementioned quadruple therapy combined with probiotics live combined Bifidobacterium, Lactobacillus, and Enterococcus Capsules, oral for 2 weeks. Then Hp clearance rate, recurrence rate, levels of gastrointestinal hormone makers, and advance reactions between two groups were compared. At the 2nd week after the treatment, the Helicobacter pylori clearance rate in the study group (87.79%) was significantly higher than the control group (78.89%), and the total recurrence rate in the study group (6.67%) was significantly lower than the control group (13.33%) (

P
<
0.05

). Serum gastrin and motilin expression were lower, and somatostatin expressions was significantly higher than those in the control group (

P
<
0.05

). There was no significant difference in the total incidence of adverse reactions between the two groups (

P
>
0.05

). In summary, quadruple therapy combined with probiotics in the treatment of Helicobacter pylori-related peptic ulcer can improve the Helicobacter pylori clearance rate, reduce the Helicobacter pylori recurrence rate, and is beneficial to improving the level of gastrointestinal hormones, with certain safety.
Ginsenoside Rg1 Reduces Oxidative Stress Via Nrf2 Activation to Regulate Age-Related Mesenchymal Stem Cells Fate Switch Between Osteoblasts and Adipocytes
Authors: Hou Jiying , Wang Lu , Wang Chen , Ma Ruoxiang , Wang Ziling , Xiao Hanxianzhi , Zeng Di , Ling Li , Wang Yaping
Published: October 2022

Background. An important feature of aging cells is the gradual loss of physiological integrity. As aging progresses, MSCs change preferring to differentiate toward adipocytes rather than osteoblasts. Oxidative stress accumulation is an important factor in age-related bone loss. Many experiments have demonstrated the good therapeutic effect of Ginsenoside (Rg1) on oxidative stress injury. In this study, we investigated the effect of Rg1 on the osteogenic-adipogenic differentiation balance of bone marrow mesenchymal stem cells (BMMSC). Objective. To analyze the potential application value of Rg1 in the treatment of senile osteoporosis. Methods. BMMSCs were isolated from healthy donors of different ages and identified based on isotype and by multi-differentiation induction. Rg1 was used to treat BMMSCs, The differentiation propensity was analyzed by induction of differentiation assay. Antioxidant capacity of BMMSCs as measured by oxidative stress product assay Related mechanism studies were confirmed by quantitative real-time reverse transcription-polymerase chain reaction (qRT‐PCR), immunofluorescence, western blotting, and inhibitor treatment. Moreover, Observation of the effects of Rg1 on aging BMMSCs under in vivo conditions by treatment of aged mice with Rg1 injections. Results. Rg1 treatment rescued age‐induced switch of BMMSCs differentiation fate in vitro. In elderly people, Rg1 markedly increased osteogenic differentiation of BMMSCs by decreasing oxidative stress, while inhibiting adipogenic differentiation. However, this effect was abolished in BMMSCs by an Nrf2-inhibitor. Notably, aging mice showed a reduction in adipocyte distribution in the bone marrow and a decrease in oxidative stress products after a 3-month period of Rg1 treatment. Conclusion. We have uncovered a novel function for Rg1 that involves attenuating bone loss via Nrf2 antioxidant signaling, which in turn may potentially be utilized as a therapeutic agent for improving osteogenic differentiation in aging BMMSCs.
Automatic Recognition and Repair System of Mural Image Cracks Based on Cloud Edge Computing and Digitization
Authors: Gao Yongli , Zhou Zijie
Published: October 2022

Mural painting is the art on the wall, it is the painting that people draw on the wall, it is one of the earliest forms of painting in human history, and it is also an accessory part of the building. The decorative and beautifying functions of murals make them an important aspect of environmental art. Cloud edge computing is a combination of cloud computing and edge computing, that fully absorbs the advantages of both cloud computing and edge computing and maximizes their advantages. In this study, based on cloud edge computing and digital technology, the automatic identification and repair system of fresco image cracks is studied. Image segmentation techniques have been proposed in this study, using 60 murals in three regions as experimental objects. Through experimental analysis, it is found that the traditional pine poise treatment method takes the shortest repair time. However, for a specific image, it is difficult to guarantee the quality of its restoration. The mural image in area A was repaired with the conventional pine pitch repair method, which took 113.01 seconds, and the subjective evaluation was 69 points. Using the repair method described in this study to repair, it takes 127.38 seconds, and its subjective evaluation score is the highest, which is 87 points. The experimental results have shown that the cloud edge computing method and digital technology have had a certain positive effect on the identification and repair system of fresco image cracks.
Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence
Authors: Rodríguez-Santos Francisco , Quintanar-Reséndiz Ana L. , Delgado-Gutiérrez Guillermo , Palacios-Luengas Leonardo , Jiménez-Ramírez Omar , Vázquez-Medina Rubén
Published: October 2022

Regarding the problem of digital camera identification, many methods have been proposed, and for several of them, their effectiveness has been verified on the basis of disputed flat images. However, in real cases the disputed images are natural images, rather than flat images. In that case, several of the already proposed methods are not effective. Hence, in this paper, a method is proposed for the digital camera identification from natural images based on the statistical comparison between the residual noise in the natural disputed images and the fingerprint defined for the eligible digital cameras. In the reported case studies, the HDR database provided by the Communications and Signal Processing Laboratory of University of Florence is used to select a set of eligible digital cameras, and from this image database, for each digital camera, a set of disputed flat images, a set of disputed natural images, and a set of flat reference images were selected. Thus, the fingerprint of each digital camera was calculated from the probability density function (PDF) of the photo-response nonuniformity (PRNU) extracted from its reference images. Therefore, in order to identify the source digital camera of a natural disputed image, the Jensen–Shannon divergence (JSD) was implemented to statistically compare the PRNU-based fingerprint of each eligible source camera against the noise residual of that disputed image. The proposed method has a similar effectiveness to methods based on the peak-to-correlation energy or the Kullback–Leibler divergence when the disputed images are flat images and the PRNU is considered, but it is significantly more effective than those methods when the disputed images are natural images.
Study on Optimization of Marketing Communication Strategies in the Era of Artificial Intelligence
Authors: Wen Lingying , Lin Wen , Guo Mingde
Published: October 2022

This paper starts from the research of computational advertising and scene theory, based on the background of artificial intelligence technology application, and studies the communication strategy of the computational advertising scene. It studies the three major strategies of computing advertising scene communication and deeply analyzes the principles and applications of scene insight strategy, content selection strategy, and community operation strategy. Results show the following: (1) under the background that the era of artificial intelligence has arrived, computing advertising uses artificial intelligence algorithms to complete breakthrough upgrades. Through the combination of algorithms and data, its intelligent upgrades are mainly manifested in three aspects: First, to achieve higher matching accuracy communication, the second is efficient customized communication, and the third is to realize the contextual interaction between advertisements and users; (2) the “intelligent” performance characteristics of intelligent scene dissemination of computing advertising are mainly in the application of scene intelligence technology, the intelligence of the data platform, and the intelligent construction of user portraits; (3) in the era of artificial intelligence, the communication strategies for computing advertising scenarios mainly include intelligent scenario insight strategies, content selection strategies, and community operation strategies. First, the intelligent scene insight strategy is mainly analyzed from two levels. On the one hand, it is a scene mining based on intelligent data and an in-depth analysis of the user tag system centered on intelligent algorithms. On the other hand, through the research on keyword search, the impact of intelligent upgrade on scenario insight is analyzed. Secondly, in terms of content selection strategy, the application of artificial intelligence technology has brought a new upgrade to the creation and recommendation mechanism of computational advertisements.
Guideline and Implementation of Osteosarcoma Nursing Care for Children and Adolescents
Authors: Gao Qian , Yao Yuhong , Xu Qi
Published: October 2022

Clinical trials on pediatric oncology use therapeutic techniques with the overwhelming majority of children’s cancer patients obtaining therapy via clinical investigation procedures. Medical treatment is scheduled according to a specific protocol for enrolled patients. These protocols often do not refer to nursing care. Nursing care, on the other hand, must complement the medical care specified in the medical research protocol. Safe treatment administration, assessment of treatment responses, patients’ and families’ education, and communication with the whole medical team are just a few of the critical nursing tasks that should be properly managed. Nursing care standards have been developed in this study to strike a good balance between the procedure for clinical research and the nursing care connected with it. These recommendations outline the nursing activities and considerations that must be made while caring for pediatric cancer patients who are engaged in a specific clinical investigation procedure. The objective of this study is to outline the procedure through which nursing care guidelines could be developed and evaluated. The goal of this study was to find out the involvement of nurses in the process of health education for osteosarcoma and family patients.
Exercise Preconditioning Ameliorates Cognitive Impairment in Mice with Ischemic Stroke by Alleviating Inflammation and Modulating Gut Microbiota
Authors: Lv Heng , Wang Shasha , Tian Meihui , Wang Liya , Gao Jie , Zhao Qitao , Li Zhaoyu , Jia Xianjie , Yu Ying
Published: October 2022

Several studies have demonstrated that exercise preconditioning is an effective means of alleviating poststroke cognitive impairment (PSCI). Mechanisms of regulating cognitive function have not been fully elucidated. Herein, the present study is aimed at exploring the effect of the microbiota-gut-inflammasome-brain axis in the process of exercise preconditioning moderating cognitive impairment after ischemic stroke. We observed that exercise preconditioning decreased infarct size, reduced the degree of neuronal damage, and alleviated cognitive impairment in mice with ischemic stroke. In addition, exercise preconditioning also reduced the expression of inflammatory cytokines, as well as NLRP3, Caspase-1, IL-18, and IL-1β protein expressions. Ischemic stroke could downregulate the abundance of Roseburia while increasing the abundance of the Helicobacter at the level of genus. As a comparison, exercise preconditioning increased the abundance of the Lactobacillus, which was beneficial for mice at the genus level. In conclusion, exercise preconditioning can improve cognitive dysfunction after ischemic stroke through alleviating inflammation and regulating the composition and diversity of the gut microbiota, which might provide a new strategy for the prevention of PSCI.
Association of Serum Homocysteine with Cardiovascular and All-Cause Mortality in Adults with Diabetes: A Prospective Cohort Study
Authors: Lu Jingtong , Chen Kegong , Chen Wei , Liu Chang , Jiang XingPei , Ma Zili , Li Dong , Shen Yanjiao , Tian Hai
Published: October 2022

Background. Homocysteine (Hcy) was implicated in oxidative stress and diabetes biologically. However, the clinical evidence on the link between Hcy level and diabetes is limited and controversial. This study is aimed at investigating the association of serum Hcy with all-cause and cardiovascular mortality in diabetic patients. Methods. Serum Hcy was measured among 2,286 adults with type 2 diabetes in NHANES 1999-2006. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% CIs for the association of Hcy with all-cause and cause-specific mortality. Results. Over a median follow-up of 11.0 (interquartile range, 8.9-13.4) years, 952 of the 2286 patients with diabetes died, covering 269 (28.3%) cardiovascular deaths and 144 (15.2%) cancer deaths. Restricted cubic spline showed the linear relationship between Hcy and all-cause mortality risk. After multivariate adjustment, higher serum Hcy levels were independently associated with increased risk of all-cause and cardiovascular mortality. Compared with participants in the bottom tertile of Hcy, the multivariate-adjusted HRs and 95% CI for participants in the top quartile were 2.33 (1.64-3.30) for all-cause mortality (

p

trend

<
0.001

), 2.24 (1.22-4.10) for CVD mortality (

p

trend

=
0.017

), and 2.05 (0.90-4.69) for cancer mortality (

p

trend

=
0.096

). The association with total mortality was especially stronger among patients with albuminuria. Serum Hcy significantly improved reclassification for 10-year mortality in diabetic patients (net reclassification

index
=
0.253

and integrated discrimination

improvement
=
0.011

). Conclusions. Serum Hcy was associated with risks of all-cause and cardiovascular mortality in diabetic adults. Our results suggested that Hcy was a promising biomarker in risk stratification among diabetic patients.
Constant-Size Credential-Based Packet Forwarding Verification in SDN
Authors: Wu Ping , Chang Chao-Wen , Ma Ying-Ying , Zuo Zhi-Bin
Published: October 2022

The emerging software-defined networking (SDN) technology lacks tools to proactively ensure that policies will be followed or to inspect the behavior of the network. The network is vulnerable to sophisticated attacks against packets, such as alteration, injection, dropping, and hijacking attacks. Accurate and efficient network packet forwarding verification is a critical issue to ensure the correctness of packet forwarding when confronting with malicious attacks, while most of the existing packet forwarding verification solutions were implemented by inserting linear-scale cryptographic tags that increased with path length, which introduced significant communication overhead. In this paper, we propose a constant-size credential based packet forwarding verification mechanism in SDN. In the scheme, the ingress switch of a flow embeds a tag credential of constant-size which is independent of the packet forwarding path, each downstream switch verifies packets basing on the constant-size credential, and the controller periodically acquires node forwarding statistics along the path and localizes anomaly. The header space communication overhead of the proposed scheme is less than existing linear-scale mechanisms. We further prototype and evaluate the proposed scheme. Experiments demonstrate that the scheme achieves efficient forwarding and effective anomaly localization with less than 11% of additional forwarding delays and no more than 10% of throughput degradation.
circMTND5 Participates in Renal Mitochondrial Injury and Fibrosis by Sponging MIR6812 in Lupus Nephritis
Authors: Luan Junjun , Jiao Congcong , Ma Cong , Zhang Yixiao , Hao Xiangnan , Zhou Guangyu , Fu Jingqi , Qiu Xingyu , Li Hongyu , Yang Wei , Illei Gabor G. , Kopp Jeffrey B. , Pi Jingbo , Zhou Hua
Published: October 2022

Recent studies have focused on nuclear-encoded circular RNAs (circRNAs) in kidney diseases, but little is known about mitochondrial circRNAs. Differentially expressed circRNAs were analyzed by RNA deep sequencing from lupus nephritis (LN) biopsies and normal human kidneys. In LN renal biopsies, the most downregulated circRNA was circMTND5, which is encoded in the mitochondrial genome. We quantitated circMTND5 by qPCR and localized by fluorescence in situ hybridization (FISH). Mitochondrial abnormalities were identified by electron microscopy. The expression of mitochondrial genes was decreased, and the expression of profibrotic genes was increased on qPCR and immunostaining. RNA binding sites for MIR6812 and circMTND5 were predicted. MIR6812 expression was increased by FISH and qPCR. In HK-2 cells and its mitochondrial fraction, the role of circMTND5 sponging MIR6812 was assessed by their colocalization in mitochondria on FISH, RNA immunoprecipitation, and RNA pulldown coupled with luciferase reporter assay. circMTND5 knockdown upregulated MIR6812, decreased mitochondrial functional gene expression, and increased profibrotic gene expression. Overexpression of circMTND5 reversed these effects in hTGF-β stimulated HK-2 cells. Similar effects were observed in HK-2 cells with overexpression and with knockdown of MIR6812. We conclude that circMTND5 alleviates renal mitochondrial injury and kidney fibrosis by sponging MIR6812 in LN.
Predictive Analysis of Diabetes-Risk with Class Imbalance
Authors: ElSeddawy Ahmed I. , Karim Faten Khalid , Hussein Aisha Mohamed , Khafaga Doaa Sami
Published: October 2022

Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced class has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy.
Visfatin Amplifies Cardiac Inflammation and Aggravates Cardiac Injury via the NF-κB p65 Signaling Pathway in LPS-Treated Mice
Authors: Hu Yewen , Wu Nan , Du Weiping , Wang Shuangshuang , Wang Jian , Zhang Chaoxia , Chen Xiaomin , Shen Caijie
Published: October 2022

Background. Visfatin is an adipocytokine that has been demonstrated to be involved in cardiovascular diseases. This study aims at determining the role of visfatin in sepsis-induced cardiac injury and identify its possible mechanisms. Methods. Dynamic changes in visfatin expression in mice with lipopolysaccharide- (LPS-) induced septicemia were measured. Additionally, mice were pretreated with visfatin and further administered LPS to observe the effects of visfatin on cardiac injury. Finally, septic mice were also pretreated with JSH-23 to investigate whether visfatin regulates cardiac injury via the NF-κB p65 pathway. Results. Visfatin expression levels in both the heart and serum were increased in LPS-treated mice and peaked at 6 hours, and visfatin was derived from cardiac macrophages. In septic mice, pretreatment with visfatin reduced the survival rate, worsened cardiac dysfunction, and increased the expression of cardiac injury markers, including creatine kinase myocardial bound (CK-MB) and lactate dehydrogenase (LDH). Treatment with visfatin also increased the infiltration of CD3+ cells and F4/80+ cells, amplified the cardiac inflammatory response, and elevated myocardial cell apoptosis. Treatment with JSH-23 reversed the effects of visfatin in septic mice. Conclusions. This study showed that visfatin amplifies the cardiac inflammatory response and aggravates cardiac injury through the p65 signaling pathway. Visfatin may be a clinical target for preventing cardiac injury in sepsis.
Optimal Injection Parameters for Enhancing Coalbed Methane Recovery: A Simulation Study from the Shizhuang Block, Qinshui Basin, China
Authors: Liu Du , Shu Longyong , Wang Yanbin , Huo Zhonggang , Zhao Shihu , Xiong Xing
Published: October 2022

The injection of N2 into coal reservoir has great potential in improving recovery of coalbed methane (CBM). In this study, a numerical model was established based on the GEM component model to evaluate the influences of different N2 injection parameters (production injection well spacing, gas injection timing, gas injection duration, gas injection rate, and the bottom-hole injection pressure) on the production of CBM in the Shizhuang Block of Qinshui Basin, China. Based on the economic benefit of CBM production, the production increasing rate and nitrogen replacement ratio were established to optimize the N2 injection parameters. The results show that (1) the production injection well spacing had the greatest influence on CBM production, followed by injection duration and the bottom-hole injection pressure, and injection timing and injection rate had a relatively small influence. (2) With increasing gas injection duration, injection rate, and the bottom-hole injection pressure, the rate of production increased and the nitrogen replacement ratio decreased. (3) The optimal N2 injection scheme was revealed with the production injection well spacing of 180 m, the injection timing of a second year after gas production, an injection duration of 7 years, an injection rate of 5000 m3/d, and a bottom-hole injection pressure of 10 MPa. Under these conditions, the rate of production increasing rate, the nitrogen displacement ratio, and the regional recovery of the four production wells were 18.14%, 0.5, and 48.96%, respectively, some 8.88% higher than that without nitrogen displacement, showing good effect in terms of CBM production.
Early Diagnosis of Abnormal Left Ventricular Systolic Functions of Rare Pathogenic Titin Mutation Gene Carriers in FHCM by Three-Dimensional Speckle Tracking Echocardiography Combined with Gene Detection
Authors: Luo Xiang-hong , Zhu Rui , Chen Qian , Shi Pei-hong , Na Li-sha
Published: October 2022

Objective. This study aimed to explore the early diagnosis of abnormal left ventricular systolic function of rare pathogenic titin (TTN) mutation gene carriers in familial hypertrophic cardiomyopathy (FHCM) by three-dimensional speckle tracking echocardiography (3D-STE) combined with gene detection. Methods. Eighteen members of a Hui nationality family in Ningxia province of China were enrolled in this study in July 2019. The proband was tested with high-throughput sequencing of gene detection technology to detect the whole exome, and the mutation locus of pathogenic TTN gene was analyzed. According to the result, 16 subjects were divided into two groups: carrier group (n = 4) and noncarrier group (n = 12). Related indicators from 2DE were obtained, and myocardial strain indicators from 3D-STE were analyzed by postprocessing software of Tomtec. Strain indicators included global longitudinal strain (GLS), global circumference strain (GCS), global radial strain (GRS), regional longitudinal strain (RLS), regional circumference strain (RCS), and regional radial strain (RRS). All those indicators were compared between the two groups, and a receiver operating characteristic (ROC) curve was used for further analysis. Results. There were 4 subjects diagnosed as asymptomatic TTN gene carriers with the mutation locus of Val135643Ile. Compared with the noncarrier group, GLS and partial RLS were significantly reduced in the carrier group. The ROC curve shows that GLS has the largest AUC, and its sensitivity was better than LVPWD and specificity was better than IVSD and LVMI obtained from 2DE in the carrier group. Conclusions. There were 4 subjects diagnosed as asymptomatic TTN gene carriers with the mutation locus of Val135643Ile, and their GLS and partial RLS were significantly reduced; GLS had the better sensitivity and specificity than LVPWD, IVSD, and LVMI.
Comprehensive Approach to Mitigating Solar Photovoltaic Power Penetration Effects in a Microgrid
Authors: Kim Youngil , Bourogaoui Manef , Houari Azeddine , Kim Hyeok
Published: October 2022

High solar photovoltaic (PV) penetration in the electrical grid can result in undesired effects on the voltage quality, leading to line loss and voltage magnitude increases. One of the main criteria to ensure the safe penetration of high-power solar systems in the main grid is maintaining an acceptable voltage magnitude when a disturbance occurs (e.g., 0.95 and 1.05 per unit) with respect to total installed power generation capacity of PV power plants. This manuscript analyzes the effects of high solar PV penetration per unit of voltage stability using the Fast Voltage Stability Index and total power loss. Moreover, we investigate the flexibility benefits of coordinated voltage control based on a smart inverter of solar PV capacitor banks (SI-CBs) under five cases in a typical microgrid (MG) test model. For the test of the SI-CBs, MG modeling is developed on a modified IEEE 123 test feeder, which includes 11 building load solar PVs with smart inverters and capacitor banks with real-time data from an area in Los Angeles, California, USA. The simulation results are presented to validate the effectiveness of the proposed approach using a real-time MATLAB interface to the Open Distribution System Simulator (OpenDSS).
Evolutionary Data Preprocessing to Alleviate Class Imbalance
Authors: Seo Jae-Hyun
Published: October 2022

Intrusion detection technology for network attacks is developing rapidly with the development of artificial intelligence technology. Recently, machine learning-based methods that can detect new types of attacks have been developed. To improve the classification performance of the rare classes in the intrusion detection dataset, we study the efficient data preprocessing method based on machine learning. The UNSW-NB15, a well-known network intrusion detection dataset, is used in the experiments. The dataset includes 9 attack types and has severe class imbalance and overlap, so it is difficult to improve the classification performance above a certain level. To improve the classification performance by adjusting the number of instances of rare classes is needed. SMOTE techniques and genetic algorithms are used to optimize the ratio between classes in the training dataset. The computation time is reduced by creating a training dataset that samples only a few percent of the UNSW-NB15 dataset. Many new training datasets are generated based on the small training dataset according to the randomly generated SMOTE ratios. The classification experiments are conducted with these new training datasets. A new dataset is generated by combining the results of the experiments, and a regression model is generated by training the dataset. The best tuple of SMOTE ratios is searched by applying the model as a fitness function of the genetic algorithm. The D-S-1G combination exhibited the best performance among the test results. It consists of a decision tree classifier and the support vector regressor (SVR). As a result, the computation time was significantly reduced, and the optimal SMOTE ratios showed better results than the experimental results of the original UNSW-NB15 dataset. It was found that the classification result of each rare class relies heavily on the type of classifier.
Optimization on Tribological Behaviour of AA7075/Zirconium Boride Composites Using Taguchi Technique
Authors: Mohan S. Krishna , Reddy R. Meenakshi , Kamalakar V. , S Pranavan , Amareswar M. , Seikh A. H. , Siddique M. H. , Kishore T. L. , Singh Bhupender
Published: October 2022

The impact of wear factors on composite materials is examined in this article. Stir casting was used to produce AA7075 alloys with varied nano-zirconium boride weight ratios. The dry sliding wear test was done utilizing pin-on-disc test equipment. The L27 orthogonal array was built by Taguchi’s experiments for optimization. We calculated the proportion of responses that could be attributed to the input parameters using SNR and ANOVA. The rate of wear and the frictional coefficient raised with the highest loading level. Load concentration had an extreme impact on wear rate, coefficient of friction, sliding distance, and speed. Nano-zirconium boride particles placed in the matrix alloy prohibited any material loss. Zirconium boride nanoparticles were applied to AA7075 to increase its wear resistance.
Learning Identity-Consistent Feature for Cross-Modality Person Re-Identification via Pixel and Feature Alignment
Authors: Chan Sixian , Du Feng , Lei Yanjing , Lai Zhounian , Mao Jiafa , Li Chao
Published: October 2022

RGB-IR cross-modality person re-identification (ReID) can be seen as a multicamera retrieval problem that aims to match pedestrian images captured by visible and infrared cameras. Most of the existing methods focus on reducing modality differences through feature representation learning. However, they ignore the huge difference in pixel space between the two modalities. Unlike these methods, we utilize the pixel and feature alignment network (PFANet) to reduce modal differences in pixel space while aligning features in feature space in this paper. Our model contains three components, including a feature extractor, a generator, and a joint discriminator. Like previous methods, the generator and the joint discriminator are used to generate high-quality cross-modality images; however, we make substantial improvements to the feature extraction module. Firstly, we fuse batch normalization and global attention (BNG) which can pay attention to channel information while conducting information interaction between channels and spaces. Secondly, to alleviate the modal difference in feature space, we propose the modal mitigation module (MMM). Then, by jointly training the entire model, our model is able to not only mitigate the cross-modality and intramodality variations but also learn identity-consistent features. Finally, extensive experimental results show that our model outperforms other methods. On the SYSU-MM01 dataset, our model achieves a rank-1 accuracy of

40.83
%

and an mAP of

39.84
%

.
Software Development Data Analysis and Processing under the Internet of Things Monitoring System
Authors: Li Junru , Zhang Lijuan , Tang Xiaoqing
Published: October 2022

In order to solve the problem of highly extensible vibration test data acquisition and analysis, the author proposes a method for software development data analysis and processing under the Internet of Things monitoring system. The software platform is mainly designed through the design of software architecture based on multitask operation, active window design, reserved API interface and hardware universal design; it ensures the strong expansibility of the software platform, so as to realize the universality of the software platform. High-level vibration data analysis software designed based on this platform, such as modal parameter identification and dynamic load identification software, can be easily redeveloped by using the existing functions and software architecture of the platform, expand software functions, realize more complex vibration data analysis and processing, reduce repetitive labor, and speed up the software development process. The results showed that: the amplitude error is less than 4%. Conclusions. The feasibility and availability of software development data under the IoT monitoring system are verified.
An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring
Authors: He Liheng , Zhu Tingru , Lv Meng
Published: October 2022

The development of remote sensing technology has passed an effective means for forest resource management and monitoring, but remote sensing technology is limited by sensor hardware equipment, and the quality of remote sensing image data is low, which is difficult to meet the needs of forest resource change monitoring. This paper presents a remote sensing image classification method based on the combination of the SSIF algorithm and wavelet denoising. Forest information is extracted from PALSAR/PALSAR-2 radar remote sensing data. The forest distribution map is generated by pixel level fusion algorithm, and the accuracy of the forest distribution map is evaluated by a confusion matrix. The remote sensing image is spatio-temporal fused by the SSIF algorithm to capture more details of forest distribution. The simulation analysis shows that the overall accuracy of the forest classification results obtained by the fusion algorithm is 96% ± 1, and the kappa coefficient is 0.66. The accuracy of forest recognition meets the requirements.
Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings
Authors: Wang Zhihong , Xiong Hao
Published: October 2022

With the rapid development of interactive 3D graphics technology, as well as the growing demand for virtual reality, digital urbanization and digital cultural heritage protection and time-consuming and inefficient traditional artificial building modeling methods have been far from meeting the rapid and intelligent needs of the application market and automatic. Architectural modeling methods have been paid more and more attention. Architectural modeling is an application-oriented comprehensive research field. According to different application scenarios, its research methods cover many technical fields and disciplines. This paper introduces a method of modeling ancient buildings using depth image estimation, spherical projection mapping, 3D adversarial generation network, and other techniques. The characteristics of architectural modeling methods are discussed from different disciplinary and technical perspectives. Second, the three major schools of architectural modeling technology, mainly the process modeling method, image modeling method, and point cloud modeling method, as well as the inverse process modeling method, which has attracted much attention and challenges in recent years, are summarized in detail. Then, the problem of building modeling is discussed. The problems and challenges of building modeling technology are analyzed, and the future development trend is predicted.
Model Design of Big Data Information Security Management Based on the Internet of Things
Authors: Yao Jian
Published: October 2022

At present, the level of modernization and informatization is constantly improving, especially in rapidly developing China. A large amount of information is collected every second, forming a huge database and making people live in the “big data era.” Following cloud computing and the Internet of things, big data technology has become another revolutionary change in the global society, changing global development and becoming a new development point for technological innovation, industrial policy, and national information security. Big data in the new age poses new challenges and perspectives for the nation’s infosec development. Big data is a renewed tool for state security. Nations use big data to create state infosec, offering great facilitation, potential for adoption, and business value. This is a “new blue ocean” for competition among countries. Although big data brings convenience to public life, it also poses a serious threat to national information security. After the research and experiment of the model design of big data information security management of the Internet of things, the experimental data have shown that 86.67% set passwords in communication devices and storage devices. 66.67% installed firewalls, and 76.67% ran antivirus software. Compared with before, the total ratio of setting a password increased by 53.34% and the total ratio of installing a firewall and running antivirus software both increased by 26.67%. It can be seen from the above data that the protection of big data information under the Internet of things has been significantly improved. From the above data, through the big data information security management of the Internet of things, a new development direction is proposed for the development of information security.
[Retracted] PRP from Personal Blood Relieves Joint Fluid‐Inducing Synovial Injury through NF‐κB Pathway and Mitochondrial Apoptosis in Human Synovial Fibroblast Cells
Authors: Xie Li , Lin Wanchang , Zhu Lingqi , Zhou Liang , Lin Dasheng , Huang Lianshui , Xie Wei , Zhai Wenliang
Published: October 2022

Background. Platelet‐rich plasma (PRP) therapy is a new kind of biological therapy to retune the plasma concentrator into the patient’s body for the treatment of osteoarthritis diseases. The present research aimsed to confirm the treatment effects of PRP against osteoarthritis injury and elucidate its potential mechanism via constructing a kind of cellular injury model of human synovial fibroblast cells (HSF cells) induced by synovial fluid from osteoarthritis patients. Materials and Methods. HSF cells wereas firstly treated with the different doses of synovial fluid from osteoarthritis patients, and evaluated for the cellular injury via cell morphology and MTT assay. And then, the protective effect of PRP against cellular injury were examined by cell morphology and MTT assay. Following, flow cytometry and western blot assay were employed to evaluate the effect of PRP on mitochondrial apoptosis. Finally, the effect of PRP on NF‐κB pathway‐associated inflammation wasere examined by Elisa ELISA assay and western blot. Results. The dilution ratio 1 : 5 of synovial fluid displayed an excellent injury effect against HSF cells and selected as the model condition. The data from cellular image and MTT assay showed that PRP with the doses 1 : 5 and 1 : 10 could alleviate the cellular mounts decrease in the damaged HSF cells. Flow cytometry, western blot, and Elisa ELISA assay displayed that PRP could relieve the cellular mitochondrial apoptosis and NF‐κB pathway‐associated inflammation in the damaged HSF cells. Conclusion. PRP might relieve HSF cells injury induced by synovial fluid from osteoarthritis patients through alleviating the mitochondrial apoptosis and NF‐κB pathway‐associated inflammation.