bluuebunny/crossref_metadata_2025 huggingface dataset:
Variable generalized Chaplygin gas in f(Q) gravity and the inflationary cosmology
Authors: Aziza Altaibayeva , Chakraborty Gargee , Chattopadhyay Surajit
Published: September 2021

The work presented in this paper aims to study the inflationary cosmology of [Formula: see text] gravity where [Formula: see text] by two models of Dark Energy (DE), namely Variable Generalized Chaplygin Gas (VGCG) and the Nojiri–Odintsov Holographic Dark Energy. The reconstruction has been carried out in the absence as well as presence of viscosity. The viscous pressure is considered to be [Formula: see text], where [Formula: see text] and [Formula: see text] is the Hubble parameter. In viscous and nonviscous scenarios, the reconstruction has been carried out for power law scale factor and bounce scale factor. For the specific range of cosmic time [Formula: see text], the quintessence and quintom behavior are observed for the equation of state parameters. The slow roll parameters have been studied for both the cases in nonviscous scenario and for the case, namely bounce scale factor for VGCG of nonviscous scenario, we have found that there is a scope of exit from inflation. Furthermore, constraints have been deduced to study the presence of singularity. Finally, the [Formula: see text] gravity is reconstructed as a function of cosmic time [Formula: see text] and is found to stay at positive level for a range of cosmic time [Formula: see text].
Numerical Method to Solve a Hybrid Fuzzy Conformable Fractional Differential Equations
Authors: Shahryari N. , Allahviranloo T. , Abbasbandy S.
Published: September 2021

This research introduces a new definition of fuzzy fractional derivative, fuzzy conformable fractional derivative, which is defined based on generalized Hukuhara differentiability. Namely, we investigate the Hybrid fuzzy fractional differential equation with the fuzzy conformable fractional generalized Hukuhara derivative. We establish that the Hybrid fuzzy fractional differential equation admits two fuzzy triangular solutions and prove that these fuzzy solutions are obtained together with a characterization of these solutions by two systems of fractional differential equations. We propose an adaptable numerical scheme for the approximation of the fuzzy triangular solutions. Numerical results reveal that the numerical method is convenient for solving the Hybrid fuzzy conformable fractional differential equation.
Competitive compatibility
Authors: Doctorow Cory
Published: September 2021

Seeking to make Big Tech less central to the Internet.
Learning-‘N-Flying: A Learning-Based, Decentralized Mission-Aware UAS Collision Avoidance Scheme
Authors: Rodionova Alëna , Pant Yash Vardhan , Kurtz Connor , Jang Kuk , Abbas Houssam , Mangharam Rahul
Published: September 2021

Urban Air Mobility, the scenario where hundreds of manned and
Unmanned Aircraft Systems
(UASs) carry out a wide variety of missions (e.g., moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-‘N-Flying (LNF), a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on the fly, and allows autonomous
Unmanned Aircraft System
(UAS)s managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UASs as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining (1) learning-based decision-making and (2) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UASs on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds) and under certain assumptions has failure rates of less than 1% in the worst case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.
Testing Deep Learning-based Visual Perception for Automated Driving
Authors: Abrecht Stephanie , Gauerhof Lydia , Gladisch Christoph , Groh Konrad , Heinzemann Christian , Woehrle Matthias
Published: September 2021

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.
A Hierarchical Hybrid Locking Protocol for Parallel Real-Time Tasks
Authors: Chen Zewei , Lei Hang , Yang Maolin , Liao Yong , Qiao Lei
Published: September 2021

Parallel tasks have been paid growing attention in recent years, and the scheduling with shared resources is of significant importance to real-time systems. As an efficient mechanism to provide mutual exclusion for parallel processing, spin-locks are ubiquitous in multi-processor real-time systems. However, the spin-locks suffer the scalability problem, and the intra-task parallelism further exacerbates the analytical pessimism. To overcome such deficiencies, we propose a Hierarchical Hybrid Locking Protocol (H2LP) under federated scheduling. The proposed H2LP integrates the classical Multiprocessor Stack Resource Policy (MSRP) and uses a token mechanism to reduce global contentions. We provide a complete analysis framework supporting both heavy and light tasks under federated scheduling and develop a blocking analysis with the state-of-the-art linear optimization technique. Empirical evaluations showed that the H2LP outperformed the other state-of-the-art locking protocols in at least

configurations when considering exclusive clustering. Furthermore, our partitioned approach for light tasks can substantially improve schedulability by mitigating the over-provisioning problem.
Federated Scheduling of Sporadic DAGs on Unrelated Multiprocessors
Authors: Voudouris Petros , Stenström Per , Pathan Risat
Published: September 2021

This paper presents a federated scheduling algorithm for implicit-deadline sporadic DAGs that execute on an unrelated heterogeneous multiprocessor platform. We consider a global work-conserving scheduler to execute a single DAG exclusively on a subset of the unrelated processors. Formal schedulability analysis to find the makespan of a DAG on its dedicated subset of the processors is proposed. The problem of determining each subset of dedicated unrelated processors for each DAG such that the DAG meets its deadline (i.e., designing the federated scheduling algorithm) is tackled by proposing a novel processors-to-task assignment heuristic using a new concept called
processor value
. Empirical evaluation is presented to show the effectiveness of our approach.
Compositional Learning and Verification of Neural Network Controllers
Authors: Ivanov Radoslav , Jothimurugan Kishor , Hsu Steve , Vaidya Shaan , Alur Rajeev , Bastani Osbert
Published: September 2021

Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.
Thermal-aware Adaptive Platform Management for Heterogeneous Embedded Systems
Authors: Maity Srijeeta , Ghose Anirban , Dey Soumyajit , Biswas Swarnendu
Published: September 2021

Recent trends in real-time applications have raised the demand for high-throughput embedded platforms with integrated CPU-GPU based Systems-On-Chip (SoCs). The enhanced performance of such SoCs, however, comes at the cost of increased power consumption, resulting in significant heat dissipation and high on-chip temperatures. The prolonged occurrences of high on-chip temperature can cause accelerated in-circuit ageing, which severely degrades the long-term performance and reliability of the chip. Violation of thermal constraints leads to on-board dynamic thermal management kicking-in, which may result in timing unpredictability for real-time tasks due to transient performance degradation. Recent work in adaptive software design have explored this issue from a control theoretic stand-point, striving for smooth thermal envelopes by tuning the core frequency.

Existing techniques do not handle thermal violations for periodic real-time task sets in the presence of dynamic events like change of task periodicity, more so in the context of heterogeneous SoCs with integrated CPU-GPUs. This work presents an OpenCL runtime extension for thermal-aware scheduling of periodic, real-time tasks on heterogeneous multi-core platforms. Our framework mitigates dynamic thermal violations by adaptively tuning task mapping parameters, with the eventual control objective of satisfying
both
platform-level thermal constraints and task-level deadline constraints. We consider multiple platform-level control actions like task migration, frequency tuning and idle slot insertion as the task mapping parameters. To the best of our knowledge, this is the first work that considers such a variety of task mapping control actions in the context of heterogeneous embedded platforms. We evaluate the proposed framework on an Odroid-XU4 board using OpenCL benchmarks and demonstrate its effectiveness in reducing thermal violations.
Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections
Authors: Yang Liren , Ozay Necmiye
Published: September 2021

In this paper, we study feedback dynamical systems with memoryless controllers under imperfect information. We develop an algorithm that searches for “adversarial scenarios”, which can be thought of as the strategy for the adversary representing the noise and disturbances, that lead to safety violations. The main challenge is to analyze the closed-loop system's vulnerabilities with a potentially complex or even unknown controller in the loop. As opposed to commonly adopted approaches that treat the system under test as a black-box, we propose a synthesis-guided approach, which leverages the knowledge of a plant model at hand. This hence leads to a way to deal with gray-box systems (i.e., with known plant and unknown controller). Our approach reveals the role of the imperfect information in the violation. Examples show that our approach can find non-trivial scenarios that are difficult to expose by random simulations. This approach is further extended to incorporate model mismatch and to falsify vision-in-the-loop systems against finite-time reach-avoid specifications.
Algorithmic poverty
Authors: Kirkpatrick Keith
Published: September 2021

Algorithms can have a devastating impact on people's lives, especially if they already are struggling economically.
Mitochondrial ion channels in cardiac function
Authors: Singh Harpreet
Published: September 2021

Mitochondria have been recognized as key organelles in cardiac physiology and are potential targets for clinical interventions to improve cardiac function. Mitochondrial dysfunction has been accepted as a major contributor to the development of heart failure. The main function of mitochondria is to meet the high energy demands of the heart by oxidative metabolism. Ionic homeostasis in mitochondria directly regulates oxidative metabolism, and any disruption in ionic homeostasis causes mitochondrial dysfunction and eventually contractile failure. The mitochondrial ionic homeostasis is closely coupled with inner mitochondrial membrane potential. To regulate and maintain ionic homeostasis, mitochondrial membranes are equipped with ion transporting proteins. Ion transport mechanisms involving several different ion channels and transporters are highly efficient and dynamic, thus helping to maintain the ionic homeostasis of ions as well as their salts present in the mitochondrial matrix. In recent years, several novel proteins have been identified on the mitochondrial membranes and these proteins are actively being pursued in research for roles in the organ as well as organelle physiology. In this article, the role of mitochondrial ion channels in cardiac function is reviewed. In recent times, the major focus of the mitochondrial ion channel field is to establish molecular identities as well as assigning specific functions to them. Given the diversity of mitochondrial ion channels and their unique roles in cardiac function, they present novel and viable therapeutic targets for cardiac diseases.
Role of glypican-1 in regulating multiple cellular signaling pathways
Authors: Pan Jiajia , Ho Mitchell
Published: September 2021

Glypican-1 (GPC1) is one of the six glypican family members in humans. It is composed of a core protein with three heparan sulfate chains and attached to the cell membrane by a glycosyl-phosphatidylinositol anchor. GPC1 modulates various signaling pathways including fibroblast growth factors (FGF), vascular endothelial growth factor-A (VEGF-A), transforming growth factor-β (TGF-β), Wnt, Hedgehog (Hh), and bone morphogenic protein (BMP) through specific interactions with pathway ligands and receptors. The impact of these interactions on signaling pathways, activating or inhibitory, is dependent upon specific GPC1 domain interaction with pathway components, as well as cell surface context. In this review, we summarize the current understanding of the structure of GPC1, as well as its role in regulating multiple signaling pathways. We focus on the functions of GPC1 in cancer cells and how new insights into these signaling processes can inform its translational potential as a therapeutic target in cancer.
Ginger metabolites and metabolite-inspired synthetic products modulate intracellular calcium and relax airway smooth muscle
Authors: Luković Elvedin , Perez-Zoghbi Jose F. , Zhang Yi , Zhu Yingdong , Sang Shengmin , Emala Charles W.
Published: September 2021

Asthma affects millions of people worldwide and its prevalence is increasing. It is characterized by chronic airway inflammation, airway remodeling, and pathologic bronchoconstriction, and it poses a continuous treatment challenge with very few new therapeutics available. Thus, many asthmatics turn to plant-based complementary products, including ginger, for better symptom control, indicating an unmet need for novel therapies. Previously, we demonstrated that 6-shogaol (6S), the primary bioactive component of ginger, relaxes human airway smooth muscle (hASM) likely by inhibition of phosphodiesterases (PDEs) in the β-adrenergic (cyclic nucleotide PDEs), and muscarinic (phospholipase C, PLC) receptor pathways. However, oral 6S is extensively metabolized and it is unknown if the resulting metabolites remain bioactive. Here, we screened all the known human metabolites of 6S and several metabolite-based synthetic derivatives to better understand their mechanism of action and structure-function relationships. We demonstrate that several metabolites and metabolite-based synthetic derivatives are able to prevent Gq-coupled stimulation of intracellular calcium [Ca2+]iand inositol trisphosphate (IP3) synthesis by inhibiting PLC, similar to the parent compound 6S. We also show that these compounds prevent recontraction of ASM after β-agonist relaxation likely by inhibiting PDEs. Furthermore, they potentiate isoproterenol-induced relaxation. Importantly, moving beyond cell-based assays, metabolites also retain the functional ability to relax Gq-coupled-contractions in upper (human) and lower (murine) airways. The current study indicates that, although oral ginger may be metabolized rapidly, it retains physiological activity through its metabolites. Moreover, we are able to use naturally occurring metabolites as inspiration to develop novel therapeutics for brochoconstrictive diseases.
A direct neural measure of variable precision representations in visual working memory
Authors: Merkel C. , Bartsch M. V. , Schoenfeld M. A. , Vellage A.-K. , Müller N. G. , Hopf J.-M.
Published: September 2021

The present work demonstrates for the first time that the contralateral delay activity (CDA), an online electrophysiological measure of the number of representations maintained in memory, is also a reliable measure of the precision of memory representations. Furthermore, we show that the CDA fluctuates independently for individual items held in memory, thereby providing unambiguous direct neurophysiological support for independently fluctuating memory representations.
Correlation between LRP1B Mutations and Tumor Mutation Burden in Gastric Cancer
Authors: Hu Sizhe , Zhao Xiaokang , Qian Feng , Jin Cancan , Hou Kaishun
Published: September 2021

Background. It has been shown that low-density lipoprotein receptor-related protein 1B (LRP1B) mutations correlate with tumor mutation burden (TMB) and prognosis in patients with melanoma and non-small-cell lung cancer, while the relationship between LRP1B mutations and TMB in gastric cancer needs further study. This study is aimed at exploring the relationship between LRP1B mutations and TMB in gastric cancer. Methods. Mutation frequency profiles of the genes in patients with gastric cancer in TCGA-STAD dataset were analyzed by bioinformatics analysis. The relationship among LRP1B mutations, TMB, and patient clinical features in gastric cancer was investigated by the chi-square test. The TMB prediction capacity based on LRP1B mutation status was evaluated by ROC curves. Results. LRP1B is one of the top 10 genes with high gene mutation frequency in gastric cancer. The mutation status of LRP1B in gastric cancer patients was significantly correlated with age and TP53 and MUC16 mutation status. The result of ROC curve analysis revealed that the mutation status of LRP1B could be considered as an indicator of the degree of TMB in patients with gastric cancer. Conclusion. This study presented the relationship between TMB and LRP1B mutations in gastric cancer, providing a novel perspective for gastric cancer prognosis and therapy.
F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network
Authors: Guo Chuanpeng , Yang Wei , Shuai Mengxia , Huang Liusheng
Published: September 2021

Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rate samples. To deal with the challenge, we use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet—a four-step strategy: embedding, encoding, attention, and classification for quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. Among them, embedding converts codewords into high-density numerical vectors, encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors, and attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make a comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.
Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model
Authors: Yasin Seyab , Salem Sultan , Ayed Hamdi , Kamal Shahid , Suhail Muhammad , Khan Yousaf Ali
Published: September 2021

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.
Application and Effectiveness of Big Data and Artificial Intelligence in the Construction of Nursing Sensitivity Quality Indicators
Authors: Chen Aie , Jiang Xiaozhen , Lian Fen , Wu Jing , Weng Xiaohua , Li Wen
Published: September 2021

In order to explore the quality management efficiency of applying big data and artificial intelligence in nursing quality index, a method of building a nursing management platform integrating nursing indicators and nursing events is proposed. Based on the investigation of the application demand of nursing information system, the method achieves timely data sharing and transmission through WLAN technology and realizes nursing management monitoring, nursing quality index enquiry, and automatic statistical analysis under the vertical management mode of nursing. The results showed that 77 people (73%) thought the time decreased, 19 people (18%) thought the time was the same, and 9 people (7%) thought the time increased. In terms of intelligent application and big data of nursing information management system, there is a significant difference in nursing management efficiency before and after using nursing management information system (P < 0.001). The nursing management control platform is designed and applied, and the nursing quality control method and actual management process are improved, which is very good for strengthening nursing quality management. The overall optimization of the quality control process is realized, which helps to mobilize the initiative and enthusiasm of nursing staff and continuously improve the effectiveness of nursing management and nursing efficiency.
A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma
Authors: Chen Xi , Yan Lijun , Lu Yu , Jiang Feng , Zeng Ni , Yang Shufang , Ma Xianghua
Published: September 2021

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.
Analysis of the Significance of Immune Cell Infiltration and Prognosis of Non-Small-Cell Lung Cancer by Bioinformatics
Authors: Sun Heng , Sui Bowen , Li Yu , Yan Jun , Cao Mingming , Zhang Lijia , Liu Songjiang
Published: September 2021

Objective. To perform gene set enrichment analysis (GSEA) and analysis of immune cell infiltration on non-small-cell lung cancer (NSCLC) expression profiling microarray data based on bioinformatics, construct TICS scoring model to distinguish prognosis time, screen key genes and cancer-related pathways for NSCLC treatment, explore differential genes in NSCLC patients, predict potential therapeutic targets for NSCLC, and provide new directions for the treatment of NSCLC. Methods. Transcriptome data of 81 NSCLC patients and the GEO database were used to download matching clinical data (access number: GSE120622). Form the expression of non-small cell lung cancer (NSCLC). TICS values were calculated and grouped according to TICS values, and we used mRNA expression profile data to perform GSEA in non-small-cell lung cancer patients. Biological process (GO) analysis and DAVID and KOBAS were used to undertake pathway enrichment (KEGG) analysis of differential genes. Use protein interaction (PPI) to analyze the database STRING, and construct a PPI network model of target interaction. Results. We obtained 6 significantly related immune cells including activated B cells through the above analysis (Figure 1(b),

p
<
0.001

). Based on the TICS values of significantly correlated immune cells, 41 high-risk and 40 low-risk samples were obtained. TICS values and immune score values were subjected to Pearson correlation coefficient calculation, and TICS and IMS values were found to be significantly correlated (Cor = 0.7952). Based on non-small-cell lung cancer mRNA expression profile data, a substantial change in mRNA was found between both the high TICS group as well as the low TICS group (FDR 0.01, FC > 2). The researchers discovered 730 mRNAs that were considerably upregulated in the high TICS group and 121 mRNAs that were considerably downregulated in the low TICS group. High confidence edges (combined score >0.7) were selected using STRING data; then, 191 mRNAs were matched to the reciprocal edges; finally, an undirected network including 164 points and 777 edges was constructed. Important members of cellular chemokine-mediated signaling pathways, such as CCL19, affect patient survival time. Conclusion. (1) The longevity of patients with non-small-cell lung cancer was substantially connected with the presence of immature B cells, activated B cells, MDSC, effector memory CD4 T cells, eosinophils, and regulatory T cells. (2) Immune-related genes such as CX3CR1, CXCR4, CXCR5, and CCR7, which are associated with the survival of NSCLC, affect the prognosis of NSCLC patients by regulating the immune process.
Nomogram Prediction Model for Diabetic Retinopathy Development in Type 2 Diabetes Mellitus Patients: A Retrospective Cohort Study
Authors: Chen Xiaomei , Xie Qiying , Zhang Xiaoxue , Lv Qi , Liu Xin , Rao Huiying
Published: September 2021

Background. This study is aimed at investigating the systemic risk factors of diabetic retinopathy and further establishing a risk prediction model for DR development in T2DM patients. Methods. This is a retrospective cohort study including 330 type 2 diabetes mellitus (T2DM) patients who were followed up from December 2012 to November 2020. Multivariable cox regression analysis identifying factors associated with the hazard of developing diabetic retinopathy (DR) was used to construct the DR risk prediction model in the form of nomogram. Results. 50.6% of participants (mean age:

58.60
±
10.55

) were female, and mean duration of diabetes was

7.09
±
5.36
 
years

. After multivariate cox regression, the risk factors for developing DR were age (HR 1.068, 95%Cl 1.021-1.118,

P
=
0.005

), diabetes duration (HR 1.094, 95%Cl 1.018-1.177,

P
=
0.015

), HbA1c (HR 1.411, 95%Cl 1.113-1.788,

P
=
0.004

), albuminuria (HR 6.908, 95%Cl 1.794-26.599,

P
=
0.005

), and triglyceride (HR 1.554, 95%Cl 1.037-2.330,

P
=
0.033

). The AUC values of the nomogram for predicting developing DR at 3-, 4-, and 5-year were 0.854, 0.845, and 0.798. Conclusion. Combining age, diabetes duration, HbA1c, albuminuria, and triglyceride, the nomogram model is effective for early recognition and intervention of individuals at high risk of DR development.
Stress‐Strength Reliability for Exponentiated Inverted Weibull Distribution with Application on Breaking of Jute Fiber and Carbon Fibers
Authors: Abu El Azm Wael S. , Almetwally Ehab M. , Alghamdi Abdulaziz S. , Aljohani Hassan M. , Muse Abdisalam Hassan , Abo-Kasem O. E.
Published: September 2021

For the first time and by using an entire sample, we discussed the estimation of the unknown parameters θ1, θ2, and β and the system of stress‐strength reliability R = P(Y < X) for exponentiated inverted Weibull (EIW) distributions with an equivalent scale parameter supported eight methods. We will use maximum likelihood method, maximum product of spacing estimation (MPSE), minimum spacing absolute‐log distance estimation (MSALDE), least square estimation (LSE), weighted least square estimation (WLSE), method of Cramér‐von Mises estimation (CME), and Anderson‐Darling estimation (ADE) when X and Y are two independent a scaled exponentiated inverted Weibull (EIW) distribution. Percentile bootstrap and bias‐corrected percentile bootstrap confidence intervals are introduced. To pick the better method of estimation, we used the Monte Carlo simulation study for comparing the efficiency of the various estimators suggested using mean square error and interval length criterion. From cases of samples, we discovered that the results of the maximum product of spacing method are more competitive than those of the other methods. A two real‐life data sets are represented demonstrating how the applicability of the methodologies proposed in real phenomena.
Management Problems of Modern Logistics Information System Based on Data Mining
Authors: Zu Enhou , Shu Ming-Hung , Huang Jui-Chan , Hsu Bi-Min , Hu Chien-Ming
Published: September 2021

With the development of technology, the data stored by humans is growing geometrically. Especially in the logistics industry, the rise of online e-commerce has created a huge data flow in the informatized logistics network. How to collect, analyze, and organize this information in time and analyze the meaning of this information from it is a difficult problem. The paper aims to learn the management of logistics systems from the perspective of statistics. This article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. The information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. The experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits. The experimental data show that mining effective and accurate information from massive information can help companies to quickly make decisions, formulate scientific logistics management programs, improve operating efficiency, reduce operating costs, and obtain good benefits.
Experimental Study on Mechanical Properties of Gas Storage Sandstone under Water Content
Authors: Zhu Sinan , Sun Junchang , Wu Yingjie , Zhao Baoyun , Zhang Li , Zhang Xin , Wang Bingyuan
Published: September 2021

In order to investigate the influence of water content on the damage and degradation characteristics of Hutubi sandstone, different saturation experiments and laboratory mechanical experiments were carried out on Hutubi sandstone. The experimental results show that under uniaxial and triaxial conditions, with the increase in water content, the axial, lateral, and volume deformations gradually increase, and the deformation shows obvious dilatancy characteristics. The deviator stress and elasticity modulus of the sandstone decreased exponentially with the increasing water content. Confining pressure has a significant effect on the strength improvement of sandstone under the constant water content. Based on lognormal distribution, the damage constitutive model of sandstone which can reflect both uniaxial and triaxial condition with different water contents was proposed. The theoretical curves were compared with the experimental curves, and it was found that the theoretical curves and the experimental curves have similar changing trends. It shows the rationality of the statistical damage constitutive model.
Comprehensive Analysis of RNA Expression Profile Identifies Hub miRNA-circRNA Interaction Networks in the Hypoxic Ischemic Encephalopathy
Authors: Wei Lin , Li Xia , Wang Lijuan , Song Yanyan , Dong Hongmei
Published: September 2021

Hypoxic ischemic encephalopathy (HIE) is classified as a sort of serious nervous system syndrome that occurs in the early life period. Noncoding RNAs had been confirmed to have crucial roles in human diseases. So far, there were few systematical and comprehensive studies towards the expression profile of RNAs in the brain after hypoxia ischemia. In this study, 31 differentially expressed microRNAs (miRNAs) with upregulation were identified. In addition, 5512 differentially expressed mRNAs, long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) were identified in HIE groups. Bioinformatics analysis showed these circRNAs and mRNAs were significantly enriched in regulation of leukocyte activation, response to virus, and neutrophil degranulation. Pathway and its related gene network analysis indicated that HLA − DPA1, HLA − DQA2, HLA − DQB1, and HLA − DRB4 have a more crucial role in HIE. Finally, miRNA-circRNA-mRNA interaction network analysis was also performed to identify hub miRNAs and circRNAs. We found that miR-592 potentially targeting 5 circRNAs, thus affecting 15 mRNA expressions in HIR. hsa_circ_0068397 and hsa_circ_0045698 were identified as hub circRNAs in HIE. Collectively, using RNA-seq, bioinformatics analysis, and circRNA/miRNA interaction prediction, we systematically investigated the differentially expressed RNAs in HIE, which could give a new hint of understanding the pathogenesis of HIE.
Research on the Cultivation and Promotion of College Students’ Legal Quality and Legal Knowledge Based on Deep Learning
Authors: Cheng Cheng , Chen Wenlin , Li Ao
Published: September 2021

Under the new situation, it is urgent to strengthen the cultivation of the legal consciousness of young college students, which makes the research on the cultivation and promotion of college students’ legal quality and legal knowledge become very important. This article is aimed at studying the cultivation and promotion of college students’ legal quality and legal knowledge based on deep learning. This article first analyzes the legal quality and legal knowledge level of college students by proposing a questionnaire survey method, interview method, and interdisciplinary research method and establishes an explanatory structure model of the factors affecting the legal quality of college students; secondly, it introduces the basic theories of deep learning and explains its practical application; finally, the training rules of neural network are constructed through experiments, and the stage method of legal quality training based on BP neural network is introduced in detail. Considering that virtual reality technology has recently penetrated into the game industry and other fields, we have reason to believe that it will find ways to enter the legal knowledge training industry. The experimental results of this article show that the cultivation and improvement of college students’ legal quality and legal knowledge based on deep learning can reduce the momentary confusion and impulsiveness of college students. Among them, more than 80% of college students who know a little about the Constitution account for more than 80%, which also shows the importance of cultivating and improving the laws and regulations of college students. The results show that the method is accurate and fast.
Long Noncoding RNAs Regulate the Radioresistance of Breast Cancer
Authors: Li Zhifeng , Wang Fujin , Zhu Yinxing , Guo Ting , Lin Mei
Published: September 2021

Breast cancer (BRCA) has severely threatened women’s health worldwide. Radiotherapy is a treatment for BRCA, which applies high doses of ionizing radiation to induce cancer cell death and reduce disease recurrence. Radioresistance is one of the most important elements that affect the therapeutic efficacy of radiotherapy. Long noncoding RNAs (lncRNAs) are suggested to dominate crucial roles in regulating the biological behavior of BRCA. Currently, some studies indicate that overexpression or inhibition of lncRNAs can greatly alter the radioresistance of BRCA. In this review, we summarized the knowledge on the classification and function of lncRNAs and the molecular mechanism of BRCA radioresistance, listed lncRNAs related to the BRCA radioresistance, highlighted their underlying mechanisms, and discussed the potential application of these lncRNAs in regulating BRCA radioresistance.
Heightened Local Th17 Cell Inflammation Is Associated with Severe Community-Acquired Pneumonia in Children under the Age of 1 Year
Authors: Liu Ming , Lu Bingtai , Fan Huifeng , Guo Xuanjie , Du Shuling , Yang Diyuan , Xu Yiping , Li Yue , Che Di , Liu Yunfeng , Gu Xiaoqiong , Ding Tao , Wang Ping , Luo Hai-bin , Lu Gen
Published: September 2021

Severe community-acquired pneumonia (sCAP) early in life is a leading cause of morbidity, mortality, and irreversible sequelae. Herein, we report the clinical, etiological, and immunological characteristics of 62 children

age
<
1

year. We measured 27 cytokines in plasma and bronchoalveolar lavage (BAL) from 62 children

age
<
1

year who were diagnosed with CAP, and then, we analyzed correlations among disease severity, clinical parameters, and etiology. Of the entire cohort, three cytokines associated with interleukin-17- (IL-17-) producing helper T cells (Th17 cells), IL-1β, IL-6, and IL-17, were significantly elevated in sCAP patients with high fold changes (FCs); in BAL, these cytokines were intercorrelated and associated with blood neutrophil counts, Hb levels, and mixed bacterial-viral infections. BAL IL-1β (area under the curve (AUC) 0.820), BAL IL-17 (AUC 0.779), and plasma IL-6 (AUC 0.778) had remarkable predictive power for sCAP. Our findings revealed that increased local Th17 cell immunity played a critical role in the development of sCAP in children

age
<
1

year. Th17 cell-related cytokines could serve as local and systemic inflammatory indicators of sCAP in this age group.
Futibatinib, an Irreversible FGFR1–4 Inhibitor, in Patients with Advanced Solid Tumors HarboringFGF/FGFRAberrations: A Phase I Dose-Expansion Study
Authors: Meric-Bernstam Funda , Bahleda Rastislav , Hierro Cinta , Sanson Marc , Bridgewater John , Arkenau Hendrik-Tobias , Tran Ben , Kelley Robin Kate , Park Joon Oh , Javle Milind , He Yaohua , Benhadji Karim A. , Goyal Lipika
Published: September 2021

Futibatinib, a highly selective, irreversible FGFR1–4 inhibitor, was evaluated in a large multihistology phase I dose-expansion trial that enrolled 197 patients with advanced solid tumors. Futibatinib demonstrated an objective response rate (ORR) of 13.7%, with responses in a broad spectrum of tumors (cholangiocarcinoma and gastric, urothelial, central nervous system, head and neck, and breast cancer) bearing both known and previously uncharacterized FGFR1–3 aberrations. The greatest activity was observed in FGFR2 fusion/rearrangement–positive intrahepatic cholangiocarcinoma (ORR, 25.4%). Some patients with acquired resistance to a prior FGFR inhibitor also experienced responses with futibatinib. Futibatinib demonstrated a manageable safety profile. The most common treatment-emergent adverse events were hyperphosphatemia (81.2%), diarrhea (33.5%), and nausea (30.4%). These results formed the basis for ongoing futibatinib phase II/III trials and demonstrate the potential of genomically selected early-phase trials to help identify molecular subsets likely to benefit from targeted therapy.

Significance:

This phase I dose-expansion trial demonstrated clinical activity and tolerability of the irreversible FGFR1–4 inhibitor futibatinib across a broad spectrum of FGFR-aberrant tumors. These results formed the rationale for ongoing phase II/III futibatinib trials in cholangiocarcinoma, breast cancer, gastroesophageal cancer, and a genomically selected disease-agnostic population.

This article is highlighted in the In This Issue feature, p. 275
Persistent Hyperplastic Primary Vitreous with Microphthalmia and Coloboma in a Patient with Okur-Chung Neurodevelopmental Syndrome
Authors: Murakami Hiroaki , Uehara Tomoko , Enomoto Yumi , Nishimura Naoto , Kumaki Tatsuro , Kuroda Yukiko , Asano Mizuki , Aida Noriko , Kosaki Kenjiro , Kurosawa Kenji
Published: September 2021

Okur-Chung neurodevelopmental syndrome is a rare autosomal dominant disorder caused by pathogenic variants in CSNK2A1, which encodes the alpha 1 catalytic subunit of ­casein kinase II. This syndrome is characterized by intellectual disability, developmental delay, and multisystemic ­abnormalities including those of the brain, extremities, and skin as well as cardiovascular, gastrointestinal, and immune systems. In this study, we describe a 5-year-old boy with a de novo novel nonsense variant in CSNK2A1, NM_001895.3:c.319C>T (p.Arg107*). He showed bilateral persistent hyperplastic primary vitreous with microphthalmia, lens dysplasia, and coloboma. Ocular manifestations are very rare in this syndrome, and this study expands the spectrum of the clinical presentations of this syndrome.
Signal Quality of Electrical Cardiometry and Perfusion Index in Very Preterm Infants
Authors: Schwarz Christoph E. , O’Toole John M. , Livingstone Vicki , Pavel Andreea M. , Dempsey Eugene M.
Published: September 2021

Objective: The use of noninvasive monitoring of neonatal hemodynamics is increasing in neonatal care. Methods include noninvasive cardiac output estimated by electrical cardiometry (EC) and peripheral perfusion as perfusion index (PI) using pulse oximetry. Our aim was to evaluate the feasibility to continuously monitor preterm infants with EC and PI during the first 2 postnatal days and the effects of averaging EC data in signal quality (SigQ) analysis. Design: Prospective observational study. Setting: Tertiary neonatal academic hospital. Patients: Preterm infants <32 weeks gestation from birth until 48 h. Main Outcome Measures: Continuous EC and PI measurements. Feasibility was quantified as the time with high SigQ, classified using SigQ index in EC and exception codes in PI. Our predefined threshold for good feasibility was minimum of 24 h with high SigQ for both. Results: Twenty-two preterm infants (median [IQR] gestational age 28 + 6 (26 + 0, 30 + 4) weeks + days, birth weight 960 [773, 1,500] g) were included. We recorded a minimum of 24 h with high SigQ in 14 infants for EC (unaveraged data) and 22 infants for PI measurements. The median (range) % of recording time with high SigQ was 74% (50%, 88%) for EC and 94% (82%, 96%) for PI. Using 1 minute averaging for EC data resulted in an increase of infants with minimum 24 h of high SigQ to 21 infants. Conclusions: EC and PI monitoring are feasible in preterm infants within the first 48 h, but SigQ remains problematic for EC. Signal dropout is masked in averaged EC values.
Enhanced Recovery after Surgery for Radical Cystectomy Decreases Postoperative Complications at Different Times
Authors: Lannes François , Walz Jochen , Maubon Thomas , Rybikowski Stanislas , Fakhfakh Sami , Picini Marion , Tourret Maxime , Brun Clément , Gravis Gwenaelle , Pignot Géraldine
Published: September 2021

Introduction: This study aimed to assess whether enhanced recovery after surgery (ERAS) improves, at different time points, postoperative complications in patients undergoing radical cystectomy. Methods: We performed a retrospective monocentric study using prospectively maintained databases including all patients treated by radical cystectomy between January 2015 and July 2019. An ERAS protocol was applied in all patients from February 2018. We analyzed and compared between non-ERAS and ERAS groups early and 90-day postoperative complications and 90-day readmission. ERAS was analyzed to know its implication in fast recovery improvement over time. Results: A total of 150 patients underwent radical cystectomy, 74 without ERAS and 76 with ERAS protocol. ERAS decreased significantly early (p = 0.039) and 90-day (0.012) postoperative complications. In multivariate analysis, ERAS was an independent factor associated with less early (OR: 0.48, 95% CI: 0.25–0.96; p = 0.37) and 90-day (OR: 0.31, 95% CI: 0.14–0.68; p = 0.004) postoperative complications. There was no significant difference between groups for 90-day readmission (p = 0.349). Mean length of stay did not differ significantly between ERAS and non-ERAS groups (12.7 ± 6.2 and 13.1 ± 5.7 days, respectively; p = 0.743). Discussion/Conclusion: Our study shows that ERAS has an early positive impact that lasts over time on postoperative complications. ERAS implementation has decreased early and 90-day postoperative complications without increasing 90-day readmission. In our cohort, length of stay was not improved with ERAS protocol.