bluuebunny/crossref_metadata_2025 huggingface dataset:
LATENCY AND LIQUIDITY RISK
Authors: CARTEA ÁLVARO , JAIMUNGAL SEBASTIAN , SÁNCHEZ-BETANCOURT LEANDRO
Published: October 2021

Latency (i.e. time delay) in electronic markets affects the efficacy of liquidity taking strategies. During the time liquidity, takers process information and send marketable limit orders (MLOs) to the exchange, the limit order book (LOB) might undergo updates, so there is no guarantee that MLOs are filled. We develop a latency-optimal trading strategy that improves the marksmanship of liquidity takers. The interaction between the LOB and MLOs is modeled as a marked point process. Each MLO specifies a price limit so the order can receive worse prices and quantities than those the liquidity taker targets if the updates in the LOB are against the interest of the trader. In our model, the liquidity taker balances the tradeoff between the costs of missing trades and the costs of walking the book. In particular, we show how to build cost-neutral strategies, that on average, trade price improvements for fewer misses. We employ techniques of variational analysis to obtain the price limit of each MLO the agent sends. The price limit of an MLO is characterized as the solution to a class of forward–backward stochastic differential equations (FBSDEs) driven by random measures. We prove the existence and uniqueness of the solution to the FBSDE and numerically solve it to illustrate the performance of the latency-optimal strategies.
Equivalent Static Force in Heavy Mass Impacts on Structures
Authors: Sánchez-Haro Javier , Lombillo Ignacio , Capellán Guillermo
Published: October 2021

Structural engineers usually make simple hand calculations to pre-design structures before performing further complex analysis. However, in the case of structures subjected to impact loads, the problem is quite difficult to cope with, due to the lack of knowledge about simplified methods of analysis that can reproduce with sufficient accuracy the structural behavior, in order to establish an adequate pre-dimensioning, prior to a computer calculation. In this paper, a new formulation is presented to solve the problem of large mass impact on any kind of structure in a simplified way. The method enables a force equivalent to the impact load to be obtained that enables analysis as a static load case.
Amino acid environment affinity model based on graph attention network
Authors: Tong Xueheng , Liu Shuqi , Gu Jiawei , Wu Chunguo , Liang Yanchun , Shi Xiaohu
Published: October 2021

Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.
Dynamical symmetries, coherent states and nonlinear realizations: Towards noncommutative gravity
Authors: Cirilo-Lombardo Diego Julio
Published: October 2021

Based on the gravity model as a gauge theory previously by the authors, the introduction of noncommutative structures in the very geometry is discussed and developed in a new different way. Taking into account a linear Lagrangian in curvature and considering a pair of coherent vectors responsible, among other things, for the symmetry breaking, the introduction of noncommutativity in the very structure of the geometry is achieved. The fact that the vectors are coherent states ensure not only the natural and total quantization of the model, but also the formulation of the noncommutative structure, in particular by the introduction of a star product from the convolutory properties thereof. This new star product, in a sharp contrast with other proposals, is independent of the Bargmann index or other parameters depending on the dimension of the representation of the structure group of the noncommutativity. We also explain in the same way that a matrix model, through the properties of these coherent vectors, can be easily formulated. The pros and cons of the implementation of star products versus a theory of matrices based on coset coherent states are briefly discussed.
On horizontal immersions of discs in fat distributions of type (4,6)
Authors: Bhowmick Aritra
Published: October 2021

In this paper, we discuss horizontal immersions of discs in certain corank-[Formula: see text] fat distributions on [Formula: see text]-dimensional manifolds. The underlying real distribution of a holomorphic contact distribution on a complex [Formula: see text] manifold belongs to this class. The main result presented here says that the associated nonlinear PDE is locally invertible. Using this we prove the existence of germs of embedded horizontal discs.
Muscle damaging eccentric exercise attenuates disuse-induced declines in daily myofibrillar protein synthesis and transiently prevents muscle atrophy in healthy men
Authors: Jameson Tom S. O. , Kilroe Sean P. , Fulford Jonathan , Abdelrahman Doaa R. , Murton Andrew J. , Dirks Marlou L. , Stephens Francis B. , Wall Benjamin T.
Published: October 2021

We investigated the impact of prior muscle-damaging eccentric exercise on disuse-induced muscle deconditioning. Two and 7 days of muscle disuse per se lowered quadriceps muscle volume in association with lowered daily myofibrillar protein synthesis (MyoPS). Prior eccentric exercise prevented the decline in muscle volume after 2 days and attenuated the decline in MyoPS after 2 and 7 days. These data indicate eccentric exercise increases MyoPS and transiently prevents quadriceps muscle atrophy during muscle disuse.
Folding brains: from development to disease modeling
Authors: Del-Valle-Anton Lucia , Borrell Víctor
Published: October 2021

The human brain is characterized by the large size and intricate folding of its cerebral cortex, which are fundamental for our higher cognitive function and frequently altered in pathological dysfunction. Cortex folding is not unique to humans, nor even to primates, but is common across mammals. Cortical growth and folding are the result of complex developmental processes that involve neural stem and progenitor cells and their cellular lineages, the migration and differentiation of neurons, and the genetic programs that regulate and fine-tune these processes. All these factors combined generate mechanical stress and strain on the developing neural tissue, which ultimately drives orderly cortical deformation and folding. In this review we examine and summarize the current knowledge on the molecular, cellular, histogenic, and mechanical mechanisms that are involved in and influence folding of the cerebral cortex, and how they emerged and changed during mammalian evolution. We discuss the main types of pathological malformations of human cortex folding, their specific developmental origin, and how investigating their genetic causes has illuminated our understanding of key events involved. We close our review by presenting the animal and in vitro models of cortex folding that are currently used to study these devastating developmental brain disorders in children, and what are the main challenges that remain ahead of us to fully understand brain folding.
Nonparametric Pointwise Estimation for a Regression Model with Multiplicative Noise
Authors: Chen Jia , Kou Junke
Published: October 2021

In this paper, we consider a general nonparametric regression estimation model with the feature of having multiplicative noise. We propose a linear estimator and nonlinear estimator by wavelet method. The convergence rates of those regression estimators under pointwise error over Besov spaces are proved. It turns out that the obtained convergence rates are consistent with the optimal convergence rate of pointwise nonparametric functional estimation.
Pozzolanic Effect on the Hydration Heat of Cements Incorporating Fly Ash, Obsidian, and Slag Additives
Authors: Ustabas Ilker , Erdogdu Sakir , Omur Ihsan , Yilmaz Erol
Published: October 2021

Made up of an engineered mix of ordinary Portland cement (OPC) with artificial pozzolans such as trass, fly ash, and slag, the blended cements have been intensely employed within cementitious materials. The main reasons behind this intensive use can be clarified by enhanced workability/strength, the high resistance to chloride/sulfate, reduced permeability/alkali‐silica reaction, and a drop in the heat generated by cement’s hydration. The use of cementitious blends within concrete not only offers durable products but also cuts climate impact by energy saving and falling CO2 emissions. This study presents pozzolanic effect on the hydration heat of cements incorporating fly ash, obsidian, and slag additives. The blended cements were manufactured by three different replacement ratios of 20%, 30%, and 50%. The change in the hydration heat of obsidian‐, fly ash‐, and slag‐based cements was observed by several Turkish standards (TS EN 196‐8 and TS EN 196‐9). Mortars were used for determining the uniaxial strengths of obsidian‐, fly ash‐, and slag‐based cements. The results show that cement’s hydration heat decreases as the rate of additives (e.g., obsidian) increases from 20% to 50%. The cement’s fineness greatly affects its hydration heat. Increasing the refinement of pozzolanic material to a certain level (30%) leads to an increase in the hydration temperature. After reaching this level, there is no clear relation between the fineness and the replacement rate of pozzolans. As a result, the findings of this work will provide a good understanding of artificial pozzolans on performance and quality of obsidian‐, fly ash‐, and slag‐based cements.
Finite Element Analysis of a Novel Anterior Locking Plate for Thoracolumbar Burst Fracture
Authors: Ren Pengcheng , Cheng Xiaodong , Lu Chongyao , Wu Haotian , Yao Shuangquan , Yang Sidong , Song Zhaohui
Published: October 2021

Purpose. The finite element analysis method was used to explore the biomechanical stability of a novel locking plate for thoracolumbar burst fracture fusion fixation. Methods. The thoracolumbar CT imaging data from a normal volunteer was imported into finite software to build a normal model and three different simulated surgical models (the traditional double‐segment fixation model A, the novel double‐segment fixation model B, and the novel single‐segment fixation model C). An axial pressure (500 N) and a torque (10 Nm) were exerted on the end plate of T12 to simulate activity of the spine. We recorded the range of motion (ROM) and the maximum stress value of the simulated cages and internal fixations. Results. Model A has a larger ROM in all directions than model B (flexion 5.63%, extension 38.21%, left rotation 46.51%, right rotation 39.76%, left bending 9.45%, and right bending 11.45%). Model C also has a larger ROM in all directions than model B (flexion 555.63%, extension 51.42%, left rotation 56.98%, right rotation 55.42%, left bending 65.67%, and right bending 59.47%). The maximum stress of the cage in model A is smaller than that in model B except for the extension direction (flexion 96.81%, left rotation 175.96%, right rotation 265.73%, left bending 73.73%, and right bending 171.28%). The maximum stress value of the internal fixation in model A is greater than that in model B when models move in flexion (20.23%), extension (117.43%), and left rotation (21.34%). Conclusion. The novel locking plate has a smaller structure and better performance in biomechanical stability, which may be more compatible with minimally invasive spinal tubular technology.
Research on Basketball Teaching Network Course Resource Recommendation Method Based on Deep Learning Algorithm
Authors: Ma Kai
Published: October 2021

To solve the problem of invalid resource recommendation data and poor recommendation effect in basketball teaching network course resource recommendation, a basketball teaching network course resource recommendation method based on a deep learning algorithm is proposed. The objective function is applied to eliminate the noise in the basketball teaching network course resource data. The prominent characteristics of basketball teaching network curriculum resources are extracted using a kernel function and combined into a feature set. A convolution neural network (CNN) was employed to realize the basketball teaching network curriculum resources recommendation model. The model was assessed in terms of computation time and recognition error. To validate the performance, the proposed model was compared with two well-known recommendation models such as the learning resource recommendation method based on transfer learning and the personalized learning resource recommendation method based on three-dimensional feature collaborative domination. Experimental results show that the proposed model achieved the lowest computation time of 15 s and recommendation error less than 0.4% as compared with the existing model.
Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom
Authors: Miao Xia , Yu Ziyao , Liu Ming
Published: October 2021

The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the

k

-nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of

k

-nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.
Effect of Mild Hypothermia after Craniotomy on the Function of Related Organs in Patients with Traumatic Brain Injury
Authors: Cai Shu , Lu Zheng
Published: October 2021

Objective. To investigate the effect of mild hypothermia after craniotomy on the function of related organs in patients with traumatic brain injury. Method. A total of 240 patients with craniocerebral injury from January 2017 to December 2020 were retrospectively analyzed. Patients were randomly divided into a control group and an experimental group, with 120 cases in each group. The control group was treated with craniotomy decompression, and the experimental group was treated with early mild hypothermia based on craniotomy decompression. Patients’ venous blood was collected before operation (T0), at the end of operation (T1), 24 h after operation (T2), and 2 weeks after operation (T3) to detect the serum levels of the beta-subunit of S100 protein (S100-β); soluble growth stimulation expressed gene 2 (sST2), neutrophil gelatinase-associated lipocalin (NGAL), and interleukin 6 (IL-6). The prognostic effect was evaluated after 2 weeks of treatment. Results. After mild hypothermia treatment after craniotomy and decompression, the patients’ serum S100-β, sST2, NGAL, and IL-6 levels at different time points were significantly lower than the control group, and the total effective rate was higher than that of the control group. Conclusion. The treatment of mild hypothermia after craniotomy can reduce the related organs function damage indicators and inflammatory stress response, thus improving clinical efficacy and prognosis.
Domain Adaptation-Based Automatic Modulation Recognition
Authors: Li Tong , Xiao Yingzhe
Published: October 2021

Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validated through signal samples generated from the open-sourced Software Defined Radio (SDR) GNU Radio. The training dataset is composed of labeled samples in the source domain and unlabeled samples in the target domain. In the testing dataset, the samples are from the target domain to simulate the real scenario. Through the experiment, the proposed method has a recognition rate increase of about 88% under the CNN network structure and 91% under the ResNet network structure.
Melatonin Suppresses Apoptosis of Nucleus Pulposus Cells through Inhibiting Autophagy via the PI3K/Akt Pathway in a High‐Glucose Culture
Authors: Li Jian , Wang Chengqiang , Xue Lixin , Zhang Fan , Liu Jianqiang
Published: October 2021

Diabetes mellitus‐ (DM‐) associated hyperglycemia promotes apoptosis of disc nucleus pulposus (NP) cells, which is a contributor to intervertebral disc degeneration (IDD). Melatonin is able to protect against cell apoptosis. However, its effects on apoptosis of NP cell in a high‐glucose culture remain unclear. The purpose of the present study was to investigate the effects and molecular mechanism of melatonin on NP cell apoptosis in a high‐glucose culture. NP cells were cultured in the baseline medium supplemented with a high‐glucose concentration (0.2 M) for 3 days. The control cells were only cultured in the baseline medium. Additionally, the pharmaceutical inhibitor LY294002 was added along with the culture medium to investigate the possible role of the PI3K/Akt pathway. Apoptosis, autophagy, and activity of the PI3K/Akt pathway of NP cells among these groups were evaluated. Compared with the control NP cells, high glucose significantly increased cell apoptosis ratio and caspase‐3/caspase‐9 activity and decreased mRNA expression of Bcl‐2, whereas it increased mRNA or protein expression of Bax, caspase‐3, cleaved caspase‐3, cleaved PARP, and autophagy‐related molecules (Atg3, Atg5, Beclin‐1, and LC3‐II) and decreased protein expression of p‐Akt compared with the control cells. Additionally, melatonin partly inhibited the effects of high glucose on those parameters of cell apoptosis, autophagy, and activation of PI3K/Akt. In conclusion, melatonin attenuates apoptosis of NP cells through inhibiting the excessive autophagy via the PI3K/Akt pathway in a high‐glucose culture. This study provides new theoretical basis of the protective effects of melatonin against disc degeneration in a DM patient.
Risk of Posterior Capsular Rupture during Phacoemulsification in Patients with the History of Anti-VEGF Intravitreal Injections: Results from the Pan-American Collaborative Retina Study (PACORES) Group
Authors: Velez-Montoya Raul , Sanchez-Santos Idaira , Galvan-Chavez Mauricio , Wu Lihteh , Arevalo J. Fernando , Berrocal María H. , Alezzandrini Arturo A. , Figueroa Marta S. , Gallego-Pinazo Roberto , Dolz-Marco Rosa , Martinez-Rubio Clara , Gonzalez-Salinas Roberto
Published: October 2021

Purpose. To assess the risk for capsular rupture during routine phacoemulsification in patients with a history of anti-VEGF injections and other possible risk modifiers such as treatment patterns, type of anti-VEGF agent, and experience of the surgeon, among others. Methods. This study reviewed the medical records of 11,129 patients from 7 different hospitals in 5 countries. The study included 939 patients that underwent routine phacoemulsification and had a history of anti-VEGF therapy. We excluded patients with known risk factors for capsular rupture, as well as patients with a history of other retinal procedures. The study extracted data regarding general demographics, the number of previous injections, type of anti-VEGF agent, details of cataract surgery, and anti-VEGF treatment patterns. Results. Overall prevalence of posterior capsular rupture: 7.45% (95% CI: 5.9–9.32%). The mean number of injections per patient was 3.37 ± 2.8. More than 50% of the patients received their last anti-VEGF injection within three months before cataract surgery. The complication rate during intravitreal injections was 1.07%. In the univariate analysis, the experience of the cataract surgeon (inexperience surgeons; OR: 2.93) and the history of prior anti-VEGF therapy (OR: 1.77) were significant risk indicators for PCR (

p
<
0.05

). However, after controlling for age in the multivariate analysis, the trend did not reach a statistical significance. Conclusion. The risk for capsular rupture is higher in patients with a history of intravitreal anti-VEGF injections.
A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory
Authors: Liu Yifan , Bao Tiantian , Sang Huiyun , Wei Zhaokun
Published: October 2021

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.
Research on Intelligent Evaluation Model of Railway Internationalized Earthquake Emergency Rescue Talents Based on Analytic Hierarchy Process and Fuzzy Theory
Authors: Jing Jin , Yu Yang , Yuxin Jiang
Published: October 2021

Earthquakes occur frequently in the 21st century and cause a large number of casualties; induced secondary geological disasters will cause more serious casualties. How to reasonably deal with the earthquake disaster to carry out emergency rescue work is becoming increasingly urgent; the ability level of earthquake disaster emergency rescue personnel is directly related to the follow‐up relief effect. Based on this, aiming at the emergency rescue ability of nationalized railway management talents in high‐intensity earthquake areas around the world, this paper will use the methods of analytic hierarchy process and fuzzy theory to construct an intelligent evaluation model of railway international earthquake emergency rescue personnel ability. In addition, this paper carries out a questionnaire survey of experts in related fields and model empirical research and puts forward optimization measures and suggestions for the personnel training of railway international earthquake emergency rescue in high‐intensity seismic areas based on the results of model evaluation.
Profit Allocation of Agricultural Machinery Service-Oriented Manufacturing Alliance Based on Modified Shapley Value Method
Authors: Lyu Feng , Zhang Yanghang , Feng Zhuangzhuang , Su Jianxin
Published: October 2021

Shapely value is a method of determining the importance of individuals in the collective, avoiding the equal allocation of profit. The profit allocation on SOM (service-oriented manufacturing) alliance between agricultural machinery manufacturing enterprises and suppliers under the SOM mode is a complex problem restricted by many factors, but the Shapely value method does not consider the differences of member enterprises. Therefore, factors that affect the profit allocation are given in this paper, such as input level, effort level, innovation level, risk factor, and value-added factor. Based on these factors, the Euclidean distance is used to modify the traditional TOPSIS method to determine the profit allocation correction coefficient, and the GRA (grey relational analysis) is introduced into the TOPSIS method to calculate the closeness degree, which reflects the position relationship and consistency of data curve. Based on the modified Shapley value method, a profit allocation method of agricultural machinery service-oriented manufacturing alliance is constructed. Finally, an application example is given to verify the effectiveness of the proposed method.
Acupuncture for Hypertension in Animal Models: A Systematic Review and Meta-Analysis
Authors: Xiao Ling-Yong , Li Zheng , Du Yu-Zheng , Shi Hui-Yan , Yang Si-Qi , Zhang Yue-Xin , Li Rui-Yu , Lin Wan-Ling , Wang He-Yang , Dai Xiao-Yu
Published: October 2021

Objective. The aim of this study was to summarize and evaluate the efficacy of acupuncture in hypertension animal study. Methods. Studies were searched from six databases, including Medline, Embase, Chinese National Knowledge Infrastructure, Wanfang Data, VIP information database, and Chinese Biomedical Literature Database. Study quality of each included study was evaluated according to the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines, and the risk of bias was evaluated by the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were selected as outcomes. Meta-analyses were performed using Stata 12.0 software. The effect size was calculated by combining SBP/DBP/MAP data with the random effects model, respectively. Results. 67 studies containing 1522 animals were included. According to the ARRIVE guideline, 8 items were assessed as poor and 4 items were assessed as excellent. According to the SYRCLE tool, all studies were judged as having high risk of bias. Compared with the hypertension group, the pooled results showed significant antihypertension effects of acupuncture for SBP, DBP, and MAP. Similarly, compared with the sham-acupuncture group, the pooled results showed significant antihypertension effects of acupuncture for SBP, DBP, and MAP. Conclusion. Although pooled data suggested that the acupuncture group was superior to the hypertension group or sham-acupuncture group for SBP/DBP/MAP, the presentation of poor methodological quality, high risk of bias, and heterogeneity deserves cautious interpretation of the results.
Ginsenoside Re Improves Inflammation and Fibrosis in Hepatic Tissue by Upregulating PPARγ Expression and Inhibiting Oxidative Stress in db/db Mice
Authors: Jiang Yichuan , Sui Dayun , Li Min , Xu Huali , Yu Xiaofeng , Liu Jinsha , Yu Qian
Published: October 2021

Ginsenoside Re (Re) is the main component of “Zhenyuan Capsule” (ZYC), which was wildly used in clinic in China for adjunctive treatment of coronary heart disease (CHD) and type II diabetes (T2DM). Nonalcoholic fatty liver disease (NAFLD) is one of the most important complications of T2DM, as well as an important risk factor of CHD. The aim of the present study was to investigate the effects of Re on NAFLD in db/db mice, one of the most recognized gene deficient animal models on T2DM. Sixteen db/db mice and sixteen wild-type mice were divided into four groups and orally administered Re or placebo in equal volume. According to the results, Re showed no obvious effect on blood glucose, lipids, or body weight of db/db mice. Histology pictures of hepatic tissue showed that Re did not improve steatosis, too. However, some evidence suggested that hepatic injury in db/db mice was attenuated by Re administering. Collagen deposition and aminotransferase elevation were significantly downregulated in the DB + Re group compared to those in the DB Group. The mechanisms of the protect effects of Re represented in db/db mice with NAFLD might be inhibiting oxidative stress and the reupregulation of peroxisome proliferator-activated receptor γ (pparγ) expression. The results of this study indicated that ZYC might be able to help T2DM patients with NAFLD to control the progress of NAFLD as an alternation of thiazolidinediones, synthetic agonists of PPARγ, whose side effects and adverse events should not be ignored.
Ghrelin Alleviates Endoplasmic Reticulum Stress in MC3T3E1 Cells by Inhibiting AMPK Phosphorylation
Authors: Lv Xue , Zhang Qianping , Cheng Bingfei , Xin Ying , Wang Jun , Li Juanjuan , Li Chengqian , Yang Nailong
Published: October 2021

Ghrelin is a gastric endocrine peptide that has been found to be involved in the process of energy homeostasis and bone physiology in recent years. To explore the effects of ghrelin on endoplasmic reticulum stress (ERS) in MC3T3E1 cells and its possible mechanism, an ERS model was induced by tunicamycin (TM) in the osteoblast line MC3T3E1. TM at 1.5 μg/mL was selected as the experimental concentration found by CCK8 assay. Through the determination of apoptosis, reactive oxygen species production, and endoplasmic reticulum stress‐related gene expression, we found that ERS induced by TM can be relieved by ghrelin in a concentration‐dependent manner (P < 0.001). Compared with the TM group, ghrelin reduced the expression of ERS‐related marker genes induced by TM. Compared with the GSK621 + TM group without ghrelin pretreatment, the mRNA expression of genes in the ghrelin pretreatment group decreased significantly (P < 0.001). The results of protein analysis showed that the levels of BIP, p‐AMPK, and cleaved‐caspase3 in the TM group increased significantly, while the levels decreased after ghrelin pretreatment. In group GSK621 + TM compared with group GSK621 + ghrelin+TM, ghrelin pretreatment significantly reduced the level of p‐AMPK, which is consistent with the trend of the ERS‐related proteins BIP and cleaved‐caspase3. In conclusion, ghrelin alleviates the ERS induced by TM in a concentration‐dependent manner and may or at least partly alleviate the apoptosis induced by ERS in MC3T3E1 cells by inhibiting the phosphorylation of AMPK.
A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection
Authors: Deng Chuning , Liu Yongji
Published: October 2021

The rapid development of emerging technologies such as machine learning and data mining promotes a lot of smart applications, e.g., Internet of things (IoT). The supply chain management and communication are a key research direction in the IoT environment, while the inventory management (IM) has increasingly become a core part of the whole life cycle management process of the supply chain. However, the current situations of a long supply chain life cycle, complex supply chain management, and frequently changing user demands all lead to a sharp rise in logistics and communication cost. Hence, as the core part of the supply chain, effective and predictable IM becomes particularly important. In this way, this work intends to reduce the cost during the life cycle of the supply chain by optimizing the IM process. Specifically, the IM process is firstly formulated as a mathematical model, in which the objective is to jointly minimize the logistic cost and maximize the profit. On this basis, a deep inventory management (DIM) method is proposed to address this model by using the long short‐term memory (LSTM) theory of deep learning (DL). In particular, DIM transforms the time series problem into a supervised learning one and it is trained using the back propagation pattern, such that the training process can be finished efficiently. The experimental results show that the average inventory demand prediction accuracy of DIM exceeds about 80%, which can reduce the inventory cost by about 25% compared with the other state‐of‐the‐art methods and detect the anomaly inventory actions quickly.
A Novel Brain-Permeant Chemotherapeutic Agent for the Treatment of Brain Metastasis in Triple-Negative Breast Cancer
Authors: Deng Jiaojiao , Chernikova Sophia B. , Wang Yuelong , Rodriguez Mirna L. , Andersen Stephanie J. , Umeh-Garcia Maxine C. , Godfrey Bryanna O. , Ahmadian Saman S. , Fischer Wolf-Nicolas , Koller Kerry J. , Jandeleit Bernd , Ringold Gordon M. , Gephart Melanie Hayden
Published: October 2021

Development of metastases to central nervous system (CNS) is an increasing clinical issue following the diagnosis of advanced breast cancer. The propensity to metastasize to CNS varies by breast cancer subtype. Of the four breast cancer subtypes, triple-negative breast cancers (TNBC) have the highest rates of both parenchymal brain metastasis and leptomeningeal metastasis (LM). LM is rapidly fatal due to poor detection and limited therapeutic options. Therapy of TNBC brain metastasis and LM is challenged by multifocal brain metastasis and diffuse spread of LM, and must balance brain penetration, tumor cytotoxicity, and the avoidance of neurotoxicity. Thus, there is an urgent need for novel therapeutic options in TNBCs CNS metastasis. QBS10072S is a novel chemotherapeutic that leverages TNBC-specific defects in DNA repair and LAT1 (L-amino acid transporter type 1)-dependent transport into the brain. In our study, activity of QBS10072S was investigated in vitro with various cell lines including the human TNBC cell line MDA-MB-231 and its brain-tropic derivative MDA-MB-231-BR3. QBS10072S was preferentially toxic to TNBC cells. The efficacy of QBS10072S against brain metastasis and LM was tested using a model of brain metastasis based on the internal carotid injection of luciferase-expressing tumor cells into NuNu mice. The compound was well tolerated, delayed tumor growth and reduced leptomeningeal dissemination, resulting in significant extension of survival. Given that current treatments for LM are palliative with only few studies reporting a survival benefit, QBS10072S is planned to be investigated in clinical trials as a therapeutic for TNBC LM.

Significance:

TNBC brain metastasis often involves dissemination into leptomeninges. Treatment options for TNBC leptomeningeal metastasis are limited and are mostly palliative. Our study demonstrates significant efficacy of the brain-penetrating agent QBS10072S against TNBC brain metastasis and leptomeningeal spread.
Knowledge of Traction Alopecia and Hair Care Practices among Adolescents in Keffi, North-Central Nigeria
Authors: Okoro Obumneme Emeka , Imam Abubakar , Barminas Rachel
Published: October 2021

Background: Traction alopecia (TA) is the commonest cause of hair loss among women, with a prevalence of 46.2% in Nigeria. Adequate knowledge and good hair care practices can assist in its prevention. Objective: The aim of this study was to determine knowledge of both TA and hair care practices among adolescents in Keffi, Nigeria. Methods: This was a cross-sectional survey conducted among 333 female adolescents in 3 senior secondary schools between November 2019 and February 2020. The participants were randomly selected and interviewed using a questionnaire which sought information about the knowledge of TA and hair care practices. The data were analysed using SPSS 25 statistical software. Results: Overall, the knowledge score was poor in 61.4% of the adolescents. Hair plaiting 86.8% (289/333), use of chemical relaxers 68.8% (229/333), and use of hair covers 88.3% (294/333) were the predominant hair care practices. In total, 90.8% had concurrently relaxed and plaited their hair. Cornrows with own hair were the commonest hair grooming style. The use of hair attachments was believed to be the commonest cause of TA. The use of hijab was the commonest form of hair cover used. Conclusions: The knowledge of TA is poor among female adolescents. A large number engaged in risky hair care practices. Interventions to reduce the knowledge gap will reduce the prevalence of TA. Key Message: The knowledge of traction alopecia is poor among female adolescents in Keffi who engage in traumatic hair practices.
A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection
Authors: Saragih Glori Stephani , Rustam Zuherman , Aurelia Jane Eva
Published: October 2021

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10­­-8, 10­­-4, 10­­-3, 5x10­­-2, 10­­-1, 1, 10­­4} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.
Static hand gesture recognition of Arabic sign language by using deep CNNs
Authors: Ismail Mohammad H. , Dawwd Shefa A. , Ali Fakhradeen H.
Published: October 2021

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 & VGG16 for multi-model using.
MATLAB based design and performance analysis of electronically commutated BLDC motor
Authors: Etier Issa , A. Anci Manon Mary , Kannan Nithiyananthan
Published: October 2021

The main objective of this research work is to design the electronically communtated brushless direct current (BLDC) motor and analysis its performance in MATLAB environment. The use of BLDC engine is expanding daily, the performance analysis is progressively significant and the consumer loyalty is significant. In light of the ranking and requirements, the BLDC engine is planned. The BLDC motor is widely used in a variety of fields. Low ripple input supply and a suitable speed controller are needed to achieve desired motor output. The output of BLDC motors, such as torque, voltage, and speed response, is examined in this paper. The controller parameters have been fine-tuned to improve motor speed. It has been discovered that a three phase voltage source inverter (VSI) fed BLDC motor with a fractional-order proportional-integral-derivative (FOPID) controller provides superior BLDC motor response. The outcomes are broke down utilizing the MATLAB programming.
Bone cancer detection using electrical impedance tomography
Authors: Saha Nabanita , Rahaman Mohammad Anisur
Published: October 2021

Bone cancer is an uncommon sort of malignancy that alludes to irregular development of tissue inside the bone, with high opportunity to spread to different pieces of the body. It is extremely important to distinguish bone cancer at the beginning phase to cure it productively. Presently, in addition to a physical examination, magnetic resonance imaging, blood tests, positron emission tomography (PET), computed tomography (CT) or PET-CT scan, X-ray, Bone scan, Biopsy and computed tomography scan, are used to diagnose or determine the stage (or extent) of bone sarcoma. But these methods are costly and not free of radiation. Moreover, these machines are bulky. Electrical impedance tomography approach was proposed in this research for identifying bone cancer as this detection technique is able to distinguish between cancerous and non-cancerous cells by differentiating between their conductivity and it has the possibility to remove the limitations of conventional medical imaging techniques. Here, equivalent bone models were generated using (electrical impedance and diffused optical reconstruction software (EIDORS) which had been implemented in MATLAB, and three different image reconstruction algorithms-GREIT, Sheffield Backprojection, Gauss-Newton inverse algorithm had been used to detect the cancerous cells.
Density-based classification with the DENCLUE algorithm
Authors: El Hassani Mouhcine , Falih Noureddine , Bouikhalene Belaid
Published: October 2021

Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.
Compact dual-band RF rectifier for wireless energy harvesting using CRLH technique
Authors: Alhily Marwa Jasim , Al-Khafaji Nasr , Wadi Salim
Published: October 2021

In this paper, a new dual-band radio frequency (RF) rectifier was designed. The proposed design is a low-profile structure with dimensions of
 5×5.5 mm2 owing to the use of lumped elements rather than the conventional transmission lines which occupy large footprints. This property can be potentially exploited to use the proposed rectifier in high dense rectenna arrays to generate high output direct current (DC) voltages. Furthermore, the proposed design adopts the composite right/left-handed composite right left-handed (CRLH) technique to realize the dual-band structure at frequencies of 1.8 and 2.4 GHz. Afterward, the matching circuit was optimized to make sure that it offers good matching. The frequency response shows good matching at both bands which are about -22 and -25 dB respectively. Eventually, the simulated circuit has a conversion efficiency of 52% and output voltages of 0.5 V at -5 dBm for the two bands.
A small footprint printed cross-dipole antenna with wide impedance bandwidth and circular polarization
Authors: Hasan Mustafa , Alkhafaji Nasr , AlAnsary Hussam , Mohsin Azhar R.
Published: October 2021

Wideband circularly polarized (CP) cross-dipole antennas with flat, cavity and artificial magnetic conductor (AMC) reflectors are proposed. Each proposed antenna consists of a pair of driven dipoles, a pair of vacant-quarter printed rings, and a 50Ω coaxial probe. The boomerang shape has been adopted in the crossed-dipole. This shape makes the design more compact, so it can be a good candidate in the antenna array because of reducing the mutual coupling. All numerical simulation works have been done using the ANSYS electromagnetic (EM) software based on the finite element method (FEM) algorithm. The presented crossed-dipole with a cavity has the best performance compared to ones with conventional flat and AMC grounds. However, the crossed-dipole with the AMC ground is a low-profile structure. Thus, the paper investigates and discusses the results of the proposed strctures thoroughly. The obtained impedance bandwidth (IBW) is 42% (5.1-7.85 GHz) and the axial-ratio bandwidth (ARBW) is 7.72% (5.86-6.32 GHz) for the crossed-dipole with the conventional flat ground (i.e., reflector). Furthermore, the IBW and ARBW for the antenna with the cavity reflector are 50.37% (5.08-8.5 GHz) and 26.4% (5.72-7.46 GHz), respectively. The antenna with the AMC ground has the characterstics of the IBW and ARBW as 38.16% (5.36-7.89 GHz) and 15.16% (5.79-6.74 GHz), respectively. All structures are designed to operate for the C-band and wireless local area networks (WLAN) applications.