bluuebunny/crossref_metadata_2025 huggingface dataset:
Tight Error Analysis in Fixed-point Arithmetic
Authors: Simić Stella , Bemporad Alberto , Inverso Omar , Tribastone Mirco
Published: May 2022

We consider the problem of estimating the numerical accuracy of programs with operations in fixed-point arithmetic and variables of arbitrary, mixed precision, and possibly non-deterministic value. By applying a set of parameterised rewrite rules, we transform the relevant fragments of the program under consideration into sequences of operations in integer arithmetic over vectors of bits, thereby reducing the problem as to whether the error enclosures in the initial program can ever exceed a given order of magnitude to simple reachability queries on the transformed program. We describe a possible verification flow and a prototype analyser that implements our technique. We present an experimental evaluation on a particularly complex industrial case study, including a preliminary comparison between bit-level and word-level decision procedures.
Using Barrier Elision to Improve Transactional Code Generation
Authors: Honorio Bruno Chinelato , De Carvalho João P. L. , Morales Catalina Munoz , Baldassin Alexandro , Araujo Guido
Published: May 2022

With chip manufacturers such as Intel, IBM, and ARM offering native support for transactional memory in their instruction set architectures, memory transactions are on the verge of being considered a genuine application tool rather than just an interesting research topic. Despite this recent increase in popularity on the
hardware side of transactional memory (HTM)
,
software support for transactional memory (STM)
is still scarce and the only compiler with transactional support currently available, the
GNU Compiler Collection (GCC)
, does not generate code that achieves desirable performance. For
hybrid solutions of TM (HyTM)
, which are frameworks that leverage the best aspects of HTM and STM, the subpar performance of the software side, caused by inefficient compiler generated code, might forbid HyTM to offer optimal results. This article extends previous work focused exclusively on STM implementations by presenting a detailed analysis of transactional code generated by GCC in the context of HybridTM implementations. In particular, it builds on previous research of transactional memory support in the Clang/LLVM compiler framework, which is decoupled from any TM runtime, and presents the following novel contributions: (a) it shows that STM’s performance overhead, due to an excessive amount of read and write barriers added by the compiler, also impacts the performance of HyTM systems; and (b) it reveals the importance of the previously proposed annotation mechanism to reduce the performance gap between HTM and STM in phased runtime systems. Furthermore, it shows that, by correctly using the annotations on just a few lines of code, it is possible to reduce the total number of instrumented barriers by 95% and to achieve speed-ups of up to 7× when compared to the original code generated by GCC and the Clang compiler.

1
Controlling Galvanic Corrosion with Oxalic Acid and Imidazole for Chemical Mechanical Planarization of Cobalt-Copper Interface
Authors: Hazarika Jenasree , Gupta Apeksha , Rajaraman Prasanna Venkatesh
Published: May 2022

The work focuses on the investigation of Co/Cu removal rate (RR) selectivity and reduction of galvanic corrosion associated with Co and Cu by using oxalic acid (weak acid) as the complexing agent and imidazole as an inhibitor in hydrogen peroxide (H2O2) and fumed silica-based slurry. The results obtained from dissolution study, polishing experiments and potentiodynamic polarization measurements revealed that the proposed chemistry can achieve a desirable Co/Cu RR selectivity and a significant decrease in corrosion potential of Co and Cu (pH 9) to be used in the semiconductor industry. The corrosion potential difference (CuEcorr - CoEcorr) was reduced to 12 mV by using 0.1 wt% H2O2 + 0.02 M oxalic acid + 5 ppm imidazole solution at pH 9. Meanwhile, a removal rate of ∼147 nm min−1 for Co and ∼140 nm min−1 for Cu was achieved using the same composition in a fumed silica slurry which resulted in Co/Cu selectivity ratio of 1.05:1, which is acceptable for cobalt barrier and copper interconnect CMP. Based on FTIR and UV spectra, the dissolution mechanism in the proposed chemistry is also discussed.
Device and Circuit Level Assessment of Negative Capacitance TFETs for Low-Power High-Performance Digital Circuits
Authors: Shoaib Mohammad , Intekhab Amin S. , Kumar Naveen , Anand Sunny , Chunn Ankush , Alam M. Shah
Published: May 2022

In this paper, we propose and analyse the performance of negative capacitance tunnel field-effect-transistors (NC-TFETs) at device and circuit level. We design and simulate four NC-TFETs, two are Silicon based “Si NC N-TFET,” “Si NC P-TFET” and two are Heterojunction Silicon-Germanium source based “SiGe NC N-TFET,” “SiGe NC P-TFET.” The effect of NC is incorporated with 5 nm thick layer of ferroelectric Hafnium Zirconium Oxide HfZrO2 (FE-HZO) in gate stack of TFETs by using of Landau-Khalatnikov (L-K) equations in MATLAB. The results show that at reduced NC gate voltage, the ON current (ION), On to OFF current (ION/IOFF) in NC devices is enhanced by

∼
100
%
,

while

∼
50
%

reduction in the threshold voltage (VTH) and Average subthreshold slope (SSAV) is observed. The SiGe NC-TFETs showed the best response with maximum ION

∼

10

−
5

A

μ
m

range, ION/IOFF ratio of

∼

10

11

,

lowest SS

∼
25
mv

decade

−
1

,

as compare to Si NC-TFETs. The circuit transient analysis is done using the Verilog-A model based on look-up table (LUT) approach. The propagation delay of NC NAND circuits is reduced

∼

90%, the SiGe NC-TFETs based circuits showing the smallest propagation delay and fast transient characteristics. This paper also demonstrates that the impact of FE-HZO thickness (TFE) on the device-circuit characteristics.
Review—Design of a Novel Full Adder Circuit based on CNTFET Technology
Authors: Marani R. , Perri A. G.
Published: May 2022

In this paper we review a procedure to design a full adder circuit based on CNTFET technology. In particular the proposed circuit is based on NAND and NOT logic gates. Using ADS software, we describe the procedure to determine the velocity, delay and power delay product (PDP), showing moreover the improvements obtained with CNTFET technology compared to CMOS one.
Comparison of Stromal Vascular Fraction and Adipose-Derived Stem Cells for Protection of Renal Function in a Rodent Model of Ischemic Acute Kidney Injury
Authors: Aum Joomin , Jang Myoung Jin , Kim Yu Seon , Kim Bo Hyun , An Dong Hyeon , Han Jae Hyeon , Suh Nayoung , Kim Choung-Soo , You Dalsan
Published: May 2022

Aims. Few studies have compared the use of different cell types derived from adipose tissue or the optimal route for efficient and safe cell delivery in ischemic acute kidney injury (AKI). We compared the abilities of stromal vascular fraction (SVF) and adipose-derived stem cells (ADSC), injected via three different routes, to protect renal function in a rodent model of ischemic AKI. Methods. Ninety male Sprague-Dawley rats were randomly divided into 9 groups: sham, nephrectomy control, AKI control, transaortic renal arterial SVF injection, renal parenchymal SVF injection, tail venous SVF injection, transaortic renal arterial ADSC injection, renal parenchymal ADSC injection, and tail venous ADSC injection groups. Their renal function was assessed 4 days before and 1, 2, 3, 4, 7, and 14 days after surgical procedures to induce ischemic AKI. The histomorphometric studies were performed 14 days after surgical procedures. Results. Renal parenchymal injection of SVF notably reduced the level of serum blood urea nitrogen and creatinine elevation compared to the AKI control group. Renal parenchymal injection of SVF notably reduced the level of creatinine clearance decrease. In addition, collagen content was lower in the renal parenchymal SVF injection group, and fibrosis was reduced. Apoptosis was reduced in the renal parenchymal SVF injection group, and proliferation was increased. The expression levels of antioxidative markers such as glutathione reductase and peroxidase were higher in the renal parenchymal SVF injection group. Conclusions. Our findings suggest that renal function is protected from ischemic AKI through renal parenchymal injection of SVF, which has enhanced antifibrotic, antiapoptotic, and antioxidative effects.
Long-Term Environmental Enrichment Relieves Dysfunctional Cognition and Synaptic Protein Levels Induced by Prenatal Inflammation in Older CD-1 Mice
Authors: Zhang Zhe-Zhe , Zeng Li-Ping , Chen Jing , Wu Yong-Fang , Wang Ya-Tao , Xia Lan , Yang Qi-Gang , Wang Fang , Chen Gui-Hai
Published: May 2022

A mounting body of evidence suggests that prenatal inflammation may enhance the rate of age-associated cognitive decline and may involve aberrant amounts of synaptic proteins in the hippocampus, including synaptotagmin-1 (Syt1) and activity-regulated cytoskeleton-associated protein (Arc). However, little is known about the specific impact of adolescent environmental enrichment (EE) on age-associated cognitive decline and the changes in synaptic proteins caused by prenatal inflammation. In this study, CD-1 mice in late pregnancy were given intraperitoneal doses of lipopolysaccharide (LPS, 50 μg/kg) or normal saline. Offspring arising from LPS dams were divided into a LPS group and a LPS plus EE (LPS-E) group. The LPS-E mice were exposed to EE from 2 months of age until the end of the experiment (3 or 15 months old). The Morris water maze (MWM) was used to assess the spatial learning and memory capacities of experimental mice, while western blotting and RNA-scope were used to determine the expression levels of Arc and Syt1 in the hippocampus at the protein and mRNA levels, respectively. Analysis revealed that at 15 months of age, the control mice experienced a reduction in cognitive ability and elevated expression levels of Arc and Syt1 genes when compared to control mice at 3 months of age. The LPS-E group exhibited better cognition and lower protein and mRNA levels of Arc and Syt1 than mice in the LPS group of the same age. However, the enriched environment mitigated but did not counteract, the effects of prenatal inflammation on cognitive and synaptic proteins when tested at either 3 or 15 months of age. Our findings revealed that long-term environmental enrichment improved the expression levels of synaptic proteins in CD-1 mice and that this effect was linked to the dysfunctional cognition caused by prenatal inflammation; this process may also be involved in the reduction of hippocampal Arc and Syt1 gene expression.
Conditions to Flatten the Curvature Tensor Associated with a Symmetric Affine Connection
Authors: Cuellar Esperanza , Silva Ana C. , Lizárraga David A.
Published: May 2022

In this study, we present a problem that consists in flattening the curvature associated with an affine connection on ℝ2. This problem involves distributions and symmetric affine connections defined on a differential manifold M. For rank 1 constant distributions, we characterize the existence of solutions and get sufficient conditions to solve the problem. The general statement of the problem and some illustrative examples are given.
Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning
Authors: Xia Lei
Published: May 2022

This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In this study, on the basis of the 3D U-Net segmentation method, the multilevel boundary sensing RUNet was worked out after optimization. 92 patients with lung cancer were selected, and their clinical data were counted; meanwhile, the lung nodule detection was performed to obtain the segmentation effect under 3D U-Net. The accuracy of 3D U-Net and multilevel boundary sensing RUNet was compared on lung magnetic resonance imaging (MRI) after lung nodule segmentation. Patients with benign lung tumors were taken as controls; the blood immune biochemical indicators progastrin-releasing peptide (pro-CRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) in patients with malignant lung tumors were analyzed. It was found that the accuracy, sensitivity, and specificity were all greater than 90% under the algorithm-based MRI of benign and malignant tumor patients. Based on the imaging signs for the MRI image of lung nodules, the segmentation effect of the RUNet was clearer than that of the 3D U-Net. In addition, serum levels of pro-CRP, NSE, and CAE in patients with benign lung tumors were 28.9 pg/mL, 12.5 ng/mL, and 10.8 ng/mL, respectively, which were lower than 175.6 pg/mL, 33.6 ng/mL, and 31.9 ng/mL in patients with malignant lung tumors significantly (

P
<
0.05

). Thus, the RUNet image segmentation method was better than the 3D U-Net. The pro-CRP, CEA, and NSE could be used as diagnostic indicators for malignant lung tumors.
An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning
Authors: Li Wenle , Liu Wencai , Hussain Memon Fida , Wang Bing , Xu Chan , Dong Shengtao , Wang Haosheng , Hu Zhaohui , Quan Xubin , Deng Yizhuo , Liu Qiang , Su Shibin , Yin Chengliang
Published: May 2022

Background. Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods. We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient’s demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results. Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions. The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies.
The Impact of Integrated Management System on the Organizational Excellence and Organizational Innovation
Authors: Daharat Anas Nasir Mahmoud , Sued Mohammad Kamil , Gheisari Asma
Published: May 2022

Innovation in organizations, particularly in the public sector, aims at reducing costs and increasing the quality of goods and services. Thereby, a key aspect of ensuring organizational excellence is adopting innovative practices that allow the development and incorporation of novel concepts appropriate for optimizing IMS. The study is aimed at investigating the relationship between IMS, organizational excellence, and innovation. The techniques used in this study are mostly quantitative and primarily based on qualitative strategies, including phenomenology, narrative studies, grounded principal studies, interviews, and case studies. The final results of the inspection show that integrated management tools have a great impact on organizational excellence and innovation. Each result (indirect/overall) is statistically huge. IMS provides a management philosophy for the team, allowing the correct management of the technology and achieving the desired results. Therefore, IMS may also benefit enterprises by cultivating an internal innovation system within the organization.
Role of Antimicrobial Drug in the Development of Potential Therapeutics
Authors: Mayegowda Shilpa Borehalli , NG Manjula , Alghamdi Saad , Atwah Banan , Alhindi Zain , Islam Fahadul
Published: May 2022

Population of the world run into several health-related emergencies among mankind and humans as it creates a challenge for the evolution of novel drug discoveries. One such can be the emergence of multidrug-resistant (MDR) strains in both hospital and community settings, which have been due to an inappropriate use and inadequate control of antibiotics that has led to the foremost human health concerns with a high impact on the global economy. So far, there has been application of two strategies for the development of anti-infective agents either by classical antibiotics that have been derived for their synthetic analogs with increased efficacy or screening natural compounds along with the synthetic compound libraries for the antimicrobial activities. However, need for newer treatment options for infectious diseases has led research to develop new generation of antimicrobial activity to further lessen the spread of antibiotic resistance. Currently, the principles aim to find novel mode of actions or products to target the specific sites and virulence factors in pathogens by a series of better understanding of physiology and molecular aspects of the microbial resistance, mechanism of infection process, and gene-pathogenicity relationship. The design various novel strategies tends to provide us a path for the development of various antimicrobial therapies that intends to have a broader and wider antimicrobial spectrum that helps to combat MDR strains worldwide. The development of antimicrobial peptides, metabolites derived from plants, microbes, phage-based antimicrobial agents, use of metal nanoparticles, and role of CRISPR have led to an exceptional strategies in designing and developing the next-generation antimicrobials. These novel strategies might help to combat the seriousness of the infection rates and control the health crisis system.
A Novel Necroptosis-Associated lncRNA Signature Can Impact the Immune Status and Predict the Outcome of Breast Cancer
Authors: Zhang Xin , Zhang Xingda , Li Guozheng , Hao Yi , Liu Lei , Zhang Lei , Chen Yihai , Wu Jiale , Wang Xinheng , Yang Shuai , Xu Shouping
Published: May 2022

Breast cancer (BRCA) is one of the leading causes of death among women worldwide, and drug resistance often leads to a poor prognosis. Necroptosis is a type of programmed cell death (PCD) and exhibits regulatory effects on tumor progression, but few studies have focused on the relationships between necroptosis-associated lncRNAs and BRCA. In this study, we established a signature basis of 7 necroptosis-related lncRNAs associated with prognosis and divided BRCA patients into high- and low-risk groups. Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. Cox assays confirmed that risk score was an independent prognostic factor for BRCA patients. The receiver operating characteristic (ROC) curve proved the predictive accuracy of the signature and the area under the curve (AUC) values of the risk score reached 0.722. The nomogram relatively accurately predicted the prognosis of the patients. GSEA analysis suggested that the related signaling pathways and biological processes enriched in the high- and low-risk groups may influence the tumor microenvironment (TME) of BRCA. ssGSEA showed the difference in immune cell infiltration, immune pathway activation, and immune checkpoint expression between the two risk groups, with the low-risk group more suitable for immunotherapy. According to the significant difference in IC50 between risk groups, patients can be guided for an individualized treatment plan. Overall, the authors established a prognostic signature consisting of 7 necroptosis-associated lncRNAs that can independently predict the clinical outcome of BRCA patients. The difference in the tumor immune microenvironment between the low- and high-risk populations may be the reason for the resistance to immunotherapy in some patients.
In Silico Analysis Revealed Five Novel High-Risk Single-Nucleotide Polymorphisms (rs200384291, rs201163886, rs193141883, rs201139487, and rs201723157) in ELANE Gene Causing Autosomal Dominant Severe Congenital Neutropenia 1 and Cyclic Hematopoiesis
Authors: Shinwari Khyber , Bolkov Mikhail A. , Yasir Akbar Muhammad , Guojun Liu , Deryabina Svetlana S. , Tuzankina Irina A. , Chereshnev Valery. A.
Published: May 2022

Single-nucleotide polymorphisms in the ELANE (Elastase, Neutrophil Expressed) gene are associated with severe congenital neutropenia, while the ELANE gene provides instructions for making a protein called neutrophil elastase. We identified disease susceptibility single-nucleotide polymorphisms (SNPs) in the ELANE gene using several computational tools. We used cutting-edge computational techniques to investigate the effects of ELANE mutations on the sequence and structure of the protein. Our study suggested that eight nsSNPs (rs28931611, rs57246956, rs137854448, rs193141883, rs201723157, rs201139487, rs137854451, and rs200384291) are the most deleterious in ELANE gene and disturb protein structure and function. The mutants F218L, R34W, G203S, R193W, and T175M have not yet been identified in patients suffering from SCN and cyclic hematopoiesis, while C71Y, P139R, C151Y, G214R, and G203C reported in our study are already associated with both of the disorders. These mutations are shown to destabilize structure and disrupt ELANE protein activation, splicing, and folding and might diminish trypsin-like serine protease efficiency. Prediction of posttranslation modifications highlighted the significance of deleterious nsSNPs because some of nsSNPs affect potential phosphorylation sites. Gene-gene interactions showed the relation of ELANE with other genes depicting its importance in numerous pathways and coexpressions. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking. This research sheds light on how ELANE failure upon mutation results in disease progression, including congenital neutropenia, and validation of these novel predicted nsSNPs is required through the wet lab.
RTBAC: A Risk-Aware Topic-Based Access Control Model for Text Data with Paragraph-Level Authorization
Authors: Ma Ke , Yang Geng
Published: May 2022

Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control (RTBAC) model, which uses topics to represent the content relationships between users and data. The RTBAC model also uses risk technologies to grant users corresponding access rights based on their historical behaviours and their access requests. The RTBAC model is a fine-grained access control model, and the authorization of RTBAC can reach the paragraph level. Experimental results show that RTBAC is an efficient access control model and the access control granularity of the RTBAC model is more than 3 times that of the existing content-based access control models.
Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis
Authors: Zhang Jingwei , Huang Junchao , Liu Wenjun , Ding Liang , Cheng Dongdong , Xiao Haijun
Published: May 2022

This study was aimed at exploring common oncogenic genes and pathways both in osteosarcoma and Ewing’s sarcoma. Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing’s sarcoma tissues by RT-qPCR and western blot compared to normal tissues. 201 genes were differentially expressed. There were 121 nodes and 232 edges of the PPI network. Among 12 hub genes, hub genes FN1, COL1A1, and COL1A2 may involve in the development of osteosarcoma and Ewing’s sarcoma. And they were reduced to expression both in osteosarcoma and Ewing’s sarcoma tissues at mRNA and protein levels compared to normal tissues. Knockdown of FN1, COL1A1, and COL1A2 enhanced the cell proliferation and migration of U2OS under the restriction of cisplatin. Our findings revealed the common oncogenic genes such as FN1, COL1A1, and COL1A2, which may act as antioncogene by enhancing cisplatin sensitivity in osteosarcoma cells, and pathways were both in osteosarcoma and Ewing’s sarcoma.
Sustainable Development Garden Landscape Design Based on Data Mining and Virtual Reality
Authors: Bai Yuchen
Published: May 2022

At present, there is no obvious development of virtual reality technology in the garden industry. In order to improve the application of virtual reality technology in the sustainable development garden landscape design, this paper combines data mining technology and virtual reality technology to construct the sustainable development garden landscape design system. Moreover, this paper proposes a method of using the maximum likelihood method to solve the optimal hyperparameters of the covariance function and analyzes the elite-preserving genetic algorithm and the rough steps of its optimization. In addition, this paper analyzes the digital design process of garden landscape and improves the algorithm. Finally, on the basis of virtual reality technology and data mining technology, an intelligent and sustainable development garden landscape design system is constructed. The experimental analysis results show that the sustainable development garden landscape design system based on data mining and virtual reality proposed in this paper has a good effect.
Low Voltage Ride through Enhancement Using Grey Wolf Optimizer to Reduce Overshoot Current in the Grid-Connected PV System
Authors: Jaalam N. , Ahmad A.Z. , Khalid A. M. A. , Abdullah R. , Saad N. M. , Ghani S. A. , Muhammad L. N.
Published: May 2022

In today’s world, the DG should not be disconnected in the event of a power outage but should instead remain linked to the grid and supported by reactive power. This can be accomplished by implementing the low voltage ride through (LVRT) with a proportional integral (PI) controller. As a result, the voltage profile can be enhanced. The PI controller, on the other hand, has drawbacks in that setting the gain takes a long time and results in an overshoot current on the grid, which could trigger the protection relay. To address this issue, this paper proposes employing a grey-wolf optimizer (GWO) to enhance the LVRT in a 5 MW three-phase grid-connected PV system. A MATLAB simulation was carried out then under a three-phase fault and load disturbance to verify the efficiency. It is found that, even with a 70% voltage sag, the PV system can remain connected to the electrical grid while minimising overshoot current on the grid side.
Adipose-Derived Stem Cells Exosomes Improve Fat Graft Survival by Promoting Prolipogenetic Abilities through Wnt/β-Catenin Pathway
Authors: Chen Kexin , Xiong Jiachao , Xu Sha , Wu Minliang , Xue Chunyu , Wu Minjuan , Lv Chuan , Wang Yuchong
Published: May 2022

Autologous fat grafting has been widely used in plastic surgery in recent years, but the unstable retention of fat graft has always been a key clinical problem. Adipose tissue has poor tolerant to ischemia, so the transplanted adipose tissue needs to rebuild blood supply at an early stage in order to survive stably. Our previous study has found that comparing to human foreskin fibroblast exosome (HFF-Exo), human adipose-derived stem cells exosome (hADSC-Exo) can significantly improve the proliferation of vascular endothelial cells and the angiogenic effect of artificial dermal preconstructed flaps. Therefore, the ability of hADSC-Exo to improve the retention of adipose grafts and its potential regenerative mechanism aroused our strong interest. In this study, we applied hADSC-Exo and HFF-Exo to adipose grafts and explored the potential regeneration mechanism through various means such as bioinformatics, immunofluorescence, immunohistochemistry, and adipogenic differentiation. The results showed that hADSC-Exo can significantly promote grafts angiogenesis and adipogenic differentiation of ADSC to improve the retention of fat grafts and may downregulate the Wnt/β-catenin signaling pathway to promote the adipogenic differentiation. In summary, our results provide a theoretical basis for the clinical translation of hADSC-Exo in fat grafting.
Hemoglobin I from Lucina pectinata on Collagen Scaffold: A Prospective Hydrogen Sulfide Scavenger
Authors: Santiago Jennifer Vargas , Cheeseborough Anibal Quintana , López-Garriga Juan
Published: May 2022

Hydrogen sulfide (H2S), independently of being a toxic gas with a characteristic smell of rotten eggs, is a crucial signaling molecule with significant physiological functions. Given the rapid diffusivity of the gas, it is a challenge to develop robust sensors and biomarkers to quantify free or bound H2S. In addition, there is the need to further develop a robust biosystem to efficiently trap or scavenge H2S from different producing environments. The work presented here uses recombinant met-aquo rHbI (rHbI-H2O) immobilization techniques on collagen to determine its ability to bind H2S due to its high affinity (

1.24
×

10

8

 M-1). The hemeprotein will function as a scavenger on this scaffold system. UV-Vis absorption and UV-Vis diffuse reflectance (%R) spectroscopy of rHbI-H2O and rHbI-sulfide (rHbI-H2S) complex in solution and collagen scaffold demonstrated that the heme chromophore retains its reactivity and properties. UV-Vis diffuse reflectance measurements, transformed using the Kubelka-Munk function (K-M function), show a linear correlation (

R

2

=
0.9987

and 0.9916) of rHbI-H2O and rHbI-H2S within concentrations from 1 μM to 35 μM for derivatives. The extraordinary affinity of rHbI-H2O for H2S suggests recombinant met-aquo HbI in a collagen scaffold is an excellent scavenger moiety for hydrogen sulfide. These findings give insight into H2S trapping using the rHbI-H2O-collagen scaffold, where the rHbI-H2S concentration can be determined. Future pathways are to work toward the development of a met-aquo rHbI collagen solution capable of being printed as single drops on polymer, cotton or chromatographic paper. Upon exposure of these matrixes to H2S, the rHbI-H2S complex is formed and its concentration determined using UV-Vis diffuse reflectance technique.
Automatic Rule Generation for Decision-Making in Context-Aware Systems Using Machine Learning
Authors: Jabla Roua , Khemaja Maha , Buendia Félix , Faiz Sami
Published: May 2022

With the increasing interest devoted to dynamic environments, a crucial aspect is revealed in context-aware systems to deal with the rapid changes occurring in users’ surrounding environments at runtime. However, most context-aware systems with predefined context-aware rules may not support effective decision-making in dynamic environments. These context-aware rules, which take into account different context information to reach an appropriate decision, could lose their efficiency at runtime. Therefore, a growing need is emerging to address the decision-making issue leveraged by dynamic environments. To tackle this issue, we present an approach that relies on improving decision-making in the wake of dynamic environments through automatically enriching a rule knowledge base with new context-aware rules discovered at runtime. The major features of the presented approach are as follows: (i) a hybridization of two machine learning algorithms for rule generation, (ii) an extended genetic algorithm (GA) for rule optimization, and (iii) a rule transformation for the knowledge base enrichment in an automated manner. Furthermore, extensive experiments on different datasets are performed to assess the effectiveness of the presented approach. The obtained experimental results depict that this approach exhibits better effectiveness compared to some algorithms and state-of-the-art works.
Reduced Gain and Shortened Time Constant of Vestibular Velocity Storage as a Source of Balance and Movement Sensitivities in Gravitational Insecurity
Authors: Potegal Michael , May-Benson Teresa A. , Oxborough Sara , Hall Amy , McKnight Stefanie
Published: May 2022

Gravitational insecurity (GrI) involves lifetime movement and balance concerns whose pathophysiological origins are unclear. We tested whether balance symptoms in mild GrI might involve anomalies in vestibular velocity storage (VVS), a brainstem/cerebellar circuit that amplifies gain and prolongs the persistence of weak vestibular signals from small/slow head movements. A Provisional Gravitational Insecurity Index (PGrI) was developed, evaluated for psychometrics/demographics, and used to identify otherwise healthy adults with life-long balance challenges as well as sex, age, and ethnicity-matched comparison adults without such challenges. Balance confidence, sensory hypersensitivities, spatial orientation, anxiety, and hearing loss were self-reported. Standing balance under visual/proprioceptive restrictions and perrotary vestibulo-ocular nystagmus were evaluated. The PGrI showed approximated test-retest reliability and convergent and discriminant validity. When only vestibular input was available, mild GrI participants on a tilting platform used effortful hip strategies for balance significantly more than did comparison participants. Rotation testing revealed that mild GrI participants had significantly less low frequency gain and shortened VVS persistence. Combined, these two parameters correlated significantly with PGrI. The PGrI also correlated with problematic spatial orientation, but surprisingly, not to anxiety. Balance/movement issues in GrI are likely due to VVS deficiencies. Additional mechanisms may account for other GrI symptoms. Better understanding of GrI’s pathophysiological basis will be useful in informing the larger health-provider community about this condition.
An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN
Authors: Aboamer Mohamed Abdelkader , Sikkandar Mohamed Yacin , Gupta Sachin , Vives Luis , Joshi Kapil , Omarov Batyrkhan , Singh Sitesh Kumar
Published: May 2022

Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.
Value Analysis of Neoadjuvant Radiotherapy for Breast Cancer after Modified Radical Mastectomy Based on Data Mining
Authors: Liu Bo , Huang Haiyun , Pan Lijuan , Ma Yufeng
Published: May 2022

Over the last two generations, there has been a surge of interest in nonmutilating treatment for women with early breast cancer. Neoadjuvant radiation therapy, which is progressively being provided to breast cancer patients, could be used to decrease tumor burden while also providing an ability to examine treatment response. This paper aims to explore the effects of the initiation time of radiotherapy after modified adjuvant radical mastectomy on the prognosis of breast cancer. The EMR data can be used to mine hidden rules, which are of great significance for treatment and prognosis analysis. In collaboration with breast cancer, the appropriate prediction model and visualization method are selected and a visual analysis system for breast cancer group and treatment plan based on electronic medical record is constructed. Patients with multiple dimensions are reduced and clustered to form patient groups. The differences of characteristics among patient groups are intuitively displayed by using Nightingale diagram, word cloud, and time axis visualization methods. The support vector machine (SVM) model is used to predict the treatment scheme. The radiotherapy time after modified radical surgery in the two groups was within 15 weeks (observation group) and 15 weeks (routine group), respectively. The incidence of complications, local recurrence rate, progression-free survival, and quality of life scores of patients in the routine group and observation group were compared. The total incidence of complications differed significantly between the observation and routine groups. The physical function, material function, psychological function, and social function of the observation group were significantly higher than the routine group

P
<
0.05

. Radiotherapy within 15 weeks after modified radical mastectomy for breast cancer can not only reduce the local recurrence rate but also prolong the progression-free survival of patients, and the incidence of complications will not increase, which will greatly help improve the quality of life of patients.
Efficacy Evaluation of Zoledronic Acid Combined with Chemotherapy in the Treatment of Lung Cancer Spinal Metastases on Computed Tomography Images on Intelligent Algorithms
Authors: Cao Wei , Zhang Peng , Dong Nana , Hu Anwen , Xiao Jiwen , Zou Dexin , Xiang Shulin , Qi Yanxia
Published: May 2022

To analyze the effectiveness and safety of zoledronic acid combined with chemotherapy for lung cancer spinal metastases, 96 patients with lung cancer spinal metastases were averagely classified into the experimental group (gemcitabine, cisplatin, and zoledronic acid) and the control group (gemcitabine and cisplatin). An optimized noise variance estimation algorithm (OMAPB) was proposed based on the maximum a posteriori Bayesian method (MAPB), and the algorithm was applied to the patient’s computed tomography (CT) scan. The results indicated that in terms of curative effect, the number of complete remission (CR), partial remission (PR) cases, effective rate, and clinical benefit rate of the test group was significantly higher than those of the control group. The number of progress disease (PD) cases was significantly lower than that of the control group (

P
<
0.05

). The disease progression time of the test group patients was 6.2 months, and the disease progression time of the control group patients was 3.7 months (

P
<
0.05

). The test group patients had 8 cases of bone marrow suppression and gastrointestinal reactions after treatment. In the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. The patients in the control group were complicated with bone marrow suppression in 14 cases, gastrointestinal reaction in 17 cases, fever in 5 cases, pain in 4 cases, and hair loss in 6 cases. The difference was statistically significant (

P
<
0.05

). It showed that zoledronic acid combined with chemotherapy could effectively improve the treatment efficiency and clinical benefit rate of patients with lung cancer spinal metastases, prolong the progression of the disease, reduce the degree of bone tissue damage, and would not increase chemotherapy adverse events.
Application of Internet of Things Energy System in Dynamic Risk Assessment of Composite Fault of Transmission Line
Authors: Liu Jie , Jia Boyan , Ding Likun , Zhang Zhimeng , Sun Cuiying
Published: May 2022

In order to study the application of Internet of things energy system in complex fault risk dynamic assessment of transmission line. Firstly, the concept of power grid dynamic risk assessment is introduced, and the process of power grid dynamic risk assessment system based on Internet of things is designed. Then, it puts forward how to use the ubiquitous Internet of things multisource data to solve the key problems such as dynamic perception of fault probability, dynamic selection of fault set, dynamic generation of post fault state, and dynamic risk assessment of operation process. Finally, taking the maximum operation mode of a provincial power grid in summer 2013 as an example, this paper selects key 500 kV transmission lines for risk assessment, and the actual power grid example shows that. The power grid comprehensive risk assessment system considering the fault characteristics of transmission lines can effectively predict the fault probability of transmission lines; distinguish the two risks of high loss, low probability, and low loss and high probability; and provide guidance for operators. It is practical and effective.
Identification of End-User Economical Relationship Graph Using Lightweight Blockchain-Based BERT Model
Authors: Jagdish Mukta , Shah Devangkumar Umakant , Agarwal Varsha , Loganathan Ganesh Babu , Alqahtani Abdullah , Rahin Saima Ahmed
Published: May 2022

Current methods for extracting information from user resumes do not work well with unstructured user resumes in economic announcements, and they do not work well with documents that have the same users in them. Unstructured user information is turned into structured user information templates in this study. It also proposes a way to build person relationship graphs in the field of economics. First, the lightweight blockchain-based BERT model (B-BERT) is trained. The learned B-BERT pretraining model is then utilized to get the event instance vector, categorize it appropriately, and populate the hierarchical user information templates with accurate user characteristics. The aim of this research is that it has investigated the approach of creating character connection graphs in the Chinese financial system and suggests a framework for doing so in the economic sector. Furthermore, the relationship between users is found through the filled-in user information template, and a graph of user relationships is made. This is how it works: finally, the experiment is checked by filling in a manually annotated dataset. In tests, the method can be used to get text information from unstructured economic user resumes and build a relationship map of people in the financial field. The experimental results show that the proposed approach is capable of efficiently retrieving information from unstructured financial personnel resume text and generating a character relationship graph in the economic sphere.
Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme
Authors: Pandimurugan V. , Rajasoundaran S. , Routray Sidheswar , Prabu A. V. , Alyami Hashem , Alharbi Abdullah , Ahmad Sultan
Published: May 2022

Purpose. The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance. Methods. As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages. Results. This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques. Conclusion. The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.