bluuebunny/crossref_metadata_2025 huggingface dataset:
Determinants of the Continuum of Maternal Healthcare Services in Northwest Ethiopia: Findings from the Primary Health Care Project
Authors: Atnafu Asmamaw , Kebede Adane , Misganaw Bisrat , Teshome Destaw Fetene , Biks Gashaw Andargie , Demissie Getu Debalkie , Wolde Haileab Fekadu , Gelaye Kassahun Alemu , Yitayal Mezgebu , Ayele Tadesse Awoke , Azale Telake , Derso Terefe , Gebremedhin Tsegaye , Dellie Endalkachew
Published: August 2020

Background. The maternity continuum of care is the continuity of maternal healthcare services that a woman uses, which includes antenatal care (ANC 4+), skill birth attendant (SBA), and postnatal care (PNC) within 48 hours of delivery. It is one of the essential strategies for reducing maternal and newborn mortality. This study aimed to assess the factors associated with the completion of a continuum of maternal healthcare services among mothers who gave birth in the past five years. Methods. A community-based cross-sectional study was conducted from May 01 to June 29, 2019, among 565 randomly selected mothers who gave birth in five years before the study in primary healthcare project implementation districts of north Gondar zone, Amhara National Regional State, Ethiopia. Bivariable and multivariable logistic regression analysis were computed, and in the multivariable logistic regression analysis, adjusted odds ratio (AOR) with 95% confidence interval (CI) and a p value of less than 0.05 were used to identify the associated factors with completion of the continuum of maternal healthcare services. Results. The study revealed that the overall completion of the continuum of maternal healthcare services was 21.60% (95% CI: 18.20, 24.90). Women who were able to read and write (AOR: 2.70, 95% CI: 1.22, 6.04), using car/motorcycle as a means of transportation to get the health facility (AOR: 5.59, 95% CI: 2.29, 9.50), travel time less than an hour to get the health facility (AOR: 4.98, 95% CI: 2.97, 8.38), being satisfied with the service delivery (AOR: 1.89, 95% CI: 1.15, 3.11), and getting health education on maternal healthcare services in the last 6 months (AOR: 2.77, 95% CI: 1.52, 5.05) were factors associated with the completion of the continuum of maternal healthcare services. Conclusions. The completion of the continuum of maternal healthcare services was relatively low, indicating that women were not getting the likely health benefit from the present health services. Therefore, interventions should focus on increasing women’s awareness, improving the availability of services at nearby health facilities, and improving service delivery by considering women’s preferences and needs to increase their satisfaction are essential to increase the completion of maternal healthcare services.
Alternating Direction Multiplier Method for Matrix l2,1-Norm Optimization in Multitask Feature Learning Problems
Authors: Hu Yaping , Liu Liying , Wang Yujie
Published: August 2020

The joint feature selection problem can be resolved by solving a matrix l2,1-norm minimization problem. For l2,1-norm regularization, one of the most fascinating features is that some similar sparsity structures can be employed by multiple predictors. However, the nonsmooth nature of the problem brings great challenges to the problem. In this paper, an alternating direction multiplier method combined with the spectral gradient method is proposed for solving the matrix l2,1-norm optimization problem involved with multitask feature learning. Numerical experiments show the effectiveness of the proposed algorithm.
Query-Specific Deep Embedding of Content-Rich Network
Authors: Li Yue , Wang Hongqi , Yu Liqun , Cooper Sarah Yvonne , Wang Jing-Yan
Published: August 2020

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations of its neighboring nodes. The GCN output is further fed to a deep encoder-decoder model to convert each node to a Gaussian distribution and then convert back to its node identity. The dissimilarity between the two nodes is measured by the Wasserstein distance between their Gaussian distributions. We define the nodes of the network to be positives if they are relevant to the query node and negative if they are irrelevant. The labeling of the positives/negatives is based on an upper bound and a lower bound of the Wasserstein distances between the candidate nodes and the query nodes. We learn the parameters of CNN, GCN, encoder-decoder model, Gaussian distributions, and the upper bound and lower bounds jointly. The learning problem is modeled as a minimization problem to minimize the losses of node identification, network structure preservation, positive/negative query-specific relevance-guild distance, and model complexity. An iterative algorithm is developed to solve the minimization problem. We conducted experiments over benchmark networks, especially innovation networks, to verify the effectiveness of the proposed method and showed its advantage over the state-of-the-art methods.
A New Upscaling Method for Fluid Flow Simulation in Highly Heterogeneous Unconventional Reservoirs
Authors: Zhang Qi , Yu Huibin , Li Xiaofeng , Liu Tiesheng , Hu Junfeng
Published: August 2020

High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study proposed a new upscaling method integrated gray lattice Boltzmann method (GLBM) and pore network model (PNM), accounting for the fluid flow in heterogeneous porous media. This method can reasonably reduce the computational loads while preserving certain micropore characteristics. Verifications are conducted by comparing the simulation and experimental results on tight sandstones, and good agreements are achieved. The proposed method is proven to be capable of estimating bulk properties in highly heterogenous unconventional reservoirs. This method could contribute to the development of multiscale pore structure characterizations and enhance the understandings of fluid flow mechanisms in unconventional reservoirs.
Study on Solubilization and Stabilization of Eight Flavonoids by 17 Chinese Herbal Polysaccharides
Authors: Cao Xuexiao , Wang Lei , Liu Meiqi , Ren Xiaoliang , Han Tingting , Deng Yanru
Published: August 2020

Flavonoids are important active components of traditional Chinese medicines (TCMs) because of their many biological activities. We studied the interaction between 17 polysaccharides and eight flavonoids via high-performance liquid chromatography (HPLC) and the effect of the interactions on the solubility and stability of the flavonoids. The effect of the polysaccharides on the solubility of flavonoids was analyzed by statistical methods and showed significant solubility improvements. The constant temperature acceleration method (90°C/pH 9 buffer solution) was used to measure the degradation kinetics and half-life of flavonoids with and without polysaccharides. All the polysaccharides displayed a stabilizing effect on all eight flavonoids. The stabilizing effects varied in the order: quercetin, baicalein > baicalin > galuteolin > daidzin > rutin > luteolin > daidzien. The phase-solubility method was applied to quercetin and baicalein to study the mechanism of action of the polysaccharides. It appeared that the two flavonoids could form 1 : 1 inclusion complexes with polysaccharides, which may be one of the factors increasing solubility and stability. These findings increase our understanding of the role of endogenous polysaccharides in TCM in improving the stability and bioavailability of bioactive flavonoids.
An Integrated Decoupling Device for Azimuth Control of a Balloon-Borne Gondola
Authors: Wang Honghui , Yu Xiaojun , Wu Juan , Liang Shicheng , Fan Zeming , Yuan Zhaohui
Published: August 2020

Controlling and maintaining the orientation of the balloon-borne gondola for high-altitude flight is a prerequisite for ensuring the pointing control of observation instruments. When the balloon-borne gondola is flying in the stratosphere of the atmosphere, the existing external interferences will be converted into the coupling moment to the azimuth control system. Meanwhile, those uncertain factors and the frictional nonlinearity of the control system will also cause a certain magnitude of coupling moment. The existence of such coupling moment largely impacts on the accuracy and stability of the orientation control for the angular momentum exchange devices of the balloon-borne gondola. To address such an issue, this paper proposes and implements a novel type of integrated decoupler device. With this decoupler adopted, the aziDmuth control system could sense the existence of coupling torque and azimuth fluctuations quickly and suppress the influences of external interference, uncertain factors, and system structure nonlinearity on the azimuth control effectively, thereby improving the control accuracy of the azimuth control system. Both simulations and experiments are conducted to verify the effectiveness of the proposed device. The results show that the integration of the decoupler and the controller of the azimuth control system provide the azimuth control of the balloon-borne gondola with high accuracy and stability. Such a decoupler device design has a broad potential and could not only be used for balloon-borne gondola control but also could be applied onto other control systems using angular momentum exchange devices as actuators.
Strict Deformation Quantization via Geometric Quantization in the Bieliavsky Plane
Authors: Hurtado P. , Leones A. , Moreno J. B.
Published: August 2020

Using standard techniques from geometric quantization, we rederive the integral product of functions on ℝ2 (non-Euclidian) which was introduced by Pierre Bieliavsky as a contribution to the area of strict quantization. More specifically, by pairing the nontransverse real polarization on the pair groupoid ℝ2×ℝ¯2, we obtain the well-defined integral transform. Together with a convolution of functions, which is a natural deformation of the usual convolution of functions on the pair groupoid, this readily defines the Bieliavsky product on a subset of L2ℝ2.
Multiview Translation Learning for Knowledge Graph Embedding
Authors: Bin Chenzhong , Qin Saige , Rao Guanjun , Gu Tianlong , Chang Liang
Published: August 2020

Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.
Deciphering the Molecular Targets and Mechanisms of HGWD in the Treatment of Rheumatoid Arthritis via Network Pharmacology and Molecular Docking
Authors: Liu Wei , Fan Yihua , Tian Chunying , Jin Yue , Du Shaopeng , Zeng Ping , Wang Aihua
Published: August 2020

Background. Huangqi Guizhi Wuwu Decoction (HGWD) has been applied in the treatment of joint pain for more than 1000 years in China. Currently, most physicians use HGWD to treat rheumatoid arthritis (RA), and it has proved to have high efficacy. Therefore, it is necessary to explore the potential mechanism of action of HGWD in RA treatment based on network pharmacology and molecular docking methods. Methods. The active compounds of HGWD were collected, and their targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and DrugBank database, respectively. The RA‐related targets were retrieved by analyzing the differentially expressed genes between RA patients and healthy individuals. Subsequently, the compound‐target network of HGWD was constructed and visualized through Cytoscape 3.8.0 software. Protein‐protein interaction (PPI) network was constructed to explore the potential mechanisms of HGWD on RA using the plugin BisoGenet of Cytoscape 3.8.0 software. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed in R software (Bioconductor, clusterProfiler). Afterward, molecular docking was used to analyze the binding force of the top 10 active compounds with target proteins of VCAM1, CTNNB1, and JUN. Results. Cumulatively, 790 active compounds and 1006 targets of HGWD were identified. A total of 4570 differentially expressed genes of RA with a p value <0.05 and |log 2(fold change)| > 0.5 were collected. Moreover, 739 GO entries of HGWD on RA were identified, and 79 pathways were screened based on GO and KEGG analysis. The core target gene of HGWD in RA treatment was JUN. Other key target genes included FOS, CCND1, IL6, E2F2, and ICAM1. It was confirmed that the TNF signaling pathway and IL‐17 signaling pathway are important pathways of HGWD in the treatment of RA. The molecular docking results revealed that the top 10 active compounds of HGWD had a strong binding to the target proteins of VCAM1, CTNNB1, and JUN. Conclusion. HGWD has important active compounds such as quercetin, kaempferol, and beta‐sitosterol, which exert its therapeutic effect on multiple targets and multiple pathways.
The Influence of Blade Angle on the Performance of Plastic Centrifugal Pump
Authors: Li Jun , Tang Lingfeng , Zhang Yaoyao
Published: August 2020

In order to obtain the influence of blade placement angle on the performance of plastic centrifugal pumps, this article used the velocity modulus method. Based on the method, the hydraulic design of the flow passage components of the plastic centrifugal pump was carried out, and the two‐dimensional model and three‐dimensional model diagram of the flow components were established. The flow field of the impeller model under different working conditions was simulated and the results were analyzed by ANSYS CFX. The influence of different fluid loads on the solid structure under design conditions on the structure characteristics of the impeller was studied by ANSYS Workbench. Impeller models with different outlet angles were established to study the influence of the outlet angle on the performance of pump, and fluid‐structure interaction for different impeller models was utilized to study the influence of the outlet angle on the structural characteristics of the impeller. According to the Stepanoff velocity modulus method, considered the import prerotation, the wrap angle design method and the blade inlet angle design method were proposed. In order to study the influence of different inlet angles on the performance of pump, the inlet angle was changed to establish multiple sets of impeller models, 3D printing technology was used to print out each impeller, and performance experiments were performed on the pump equipped with the impeller. The result of the experiments showed that working pressure of plastic centrifugal pump exceeding 5 atm would cause impeller structure damage. When the outlet angle was 35°, the plastic centrifugal pump reached the highest efficiency of 81.0161% and the highest H of 35.8029. The maximum deformation caused by the flow field load on the impeller increased with the increase of the outlet angle. With the increase of the inlet angle, the efficiency and H of the plastic centrifugal pump were reduced. Under normal pressure load, the deformation of the impeller first decreased and then increased, and when β1 was 13°, the total deformation of the impeller was the smallest.
Conceptual Design of a 10 MW Multipurpose Research Reactor Using VVR-KN Fuel
Authors: Nguyen Nhi-Dien , Nguyen Kien-Cuong , Huynh Ton-Nghiem , Vo Doan-Hai-Dang , Tran Hoai-Nam
Published: August 2020

The paper presents a conceptual design of a 10 MW multipurpose nuclear research reactor (MPRR) loaded with the low-enriched uranium (LEU) VVR-KN fuel type. Neutronics and burnup calculations have been performed using the REBUS-MCNP6 linkage system code and the ENDF/B-VII.0 data library. The core consists of 36 fuel assemblies: 27 standard fuel assemblies and 9 control fuel assemblies with the uranium density of 2.8 gU/cm3 and the 235U enrichment of 19.75 wt.%. The cycle length of the core is 86 effective full-power days with the excess reactivity of 9600 and 1039 pcm at the beginning of cycle and the end of cycle, respectively. The highest power rate and the highest discharged burnup of fuel assembly are 393.49 kW and 56.74% loss of 235U, respectively. Thermal hydraulics analysis has also been conducted using the PLTEMP4.2 code for evaluating the safety parameters at a steady state of the hottest channel. The maximum temperatures of coolant and fuel cladding are 66.0°C and 83.0°C, respectively. This value is lower than the design limit of 98°C for cladding temperature. Thermal fluxes at the vertical irradiation channels and the horizontal beam ports have been evaluated. The maximum thermal fluxes of 2.5 × 1014 and 8.9 ×1013 n·cm−2·s−1 are found at the neutron trap and the beryllium reflector, respectively.
Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization
Authors: Tian Dan , Zhang Guoshan , Zang Shouyu
Published: August 2020

Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.
Challenges in Removing the Essure® Device
Authors: van Gastel Daniëlle M. , Maassen Liselotte W. , Koks Carolien A. M. , Veersema Sebastiaan , Bongers Marlies Y.
Published: August 2020

We present a case about Essure® removal surgery in which the third markers of the device have torn off. The woman needed a second surgery for complete removal of the devices. Fluoroscopy during surgery is a good method to visualize the lost fragments. With fluoroscopy, a hysterectomy is not needed for complete removal. It is important to understand the structure of the device and to be aware of the four radiopaque markers during surgery and their removal.
Multiple Approaches to Quantifying the Effective Porosity of Lacustrine Shale Oil Reservoirs in Bohai Bay Basin, East China
Authors: Ma Binyu , Hu Qinhong , Yang Shengyu , Yin Na , Qiao Hongguo , Zhang Tao , Meng Mianmo
Published: August 2020

An effective porosity is defined as the ratio of volume of interconnected pore space to total volume of a porous sample. It controls the magnitude of fluid flow and is a key parameter in the assessment of recoverable resources. However, its accurate measurement in tight formations is challenging, due to their complex pore structure and lithofacies heterogeneity. In this study, porosities of sixteen lacustrine shale samples from the second Member of the Kongdian Formation (Ek2) in the Cangdong Sag, Bohai Bay Basin were measured and compared using multiple methods and sample sizes to compare and contrast the effective porosity results. The methods included helium pycnometry (HP; cubes of 1 cm3 and grains at 500-841 μm), water immersion porosimetry (WIP; cubes), mercury intrusion porosimetry (MIP; cubes), and nuclear magnetic resonance (NMR; cubes). Finally, samples were completely sealed using paraffin for bulk density measurements to evaluate the extent of potential clay swelling in shale samples involving probing fluids. Results from the HP, WIP, and MIP methods for skeletal density, bulk density, and effective porosity with cubic samples were compared. While very similar skeletal densities were found for all three methods, a lower bulk density, and therefore lower porosity, from the MIP approach can be attributed to the experimental conditions (e.g., vacuum efficiency, applied pressure, wettability of water/helium vs. mercury) and the probable presence of pores with diameters larger than 50 μm not measurable by MIP. Furthermore, the HP porosity of granular samples with 500-841 μm grain sizes can be regarded as approaching the total porosity. The complicated relationship between WIP and NMR porosities may result from the heat-induced volatilization of moisture in pores during NMR tests, and countercurrent imbibition of water replacing the residual hydrocarbons during the saturation process for sample preparation in both tests. The swelling behavior of the lacustrine Ek2 shale with water is not significant because of the low content of expansive clay minerals. In summary, the WIP and HP methods are recommended for effective porosity measurement, whereas the NMR and MIP methods are invaluable for the measurement of pore-size distribution, with additional information on the effective porosity.
An Innovative Drug Delivery System Loaded with a Modified Combination of Triple Antibiotics for Use in Endodontic Applications
Authors: Parhizkar Ardavan , Nojehdehian Hanieh , Tabatabaei Fahimeh , Asgary Saeed
Published: August 2020

The objective of the current study was to introduce “Polylactic co-Glycolic Acid- (PLGA-) Coated Ceramic Microparticles” as an innovative drug delivery system, loaded with a new combination of triple antibiotics (penicillin G, metronidazole, and ciprofloxacin (PMC)) for use in endodontic treatments. Ceramic microparticles were made from β-tricalcium phosphate and hydroxyapatite and examined by “Scanning Electronic Microscope (SEM).” Then, fixed amounts of the selected antibiotics were added to a prepared PLGA solution and stirred thoroughly. Next, the prepared ceramic microparticles were dispersed completely in the drugs solution. The deposited “PMC-loaded PLGA-coated ceramic microspheres (PPCMs)” were dried and incubated in phosphate buffer saline (PBS) for 21 days. The drug release from PPCMs was quantified by a UV spectrophotometer. The antimicrobial activity of PPCMs was investigated using the “Agar Plate Diffusion Test (ADT),” “Minimum Inhibitory Concentration (MIC),” and “Minimum Bactericidal Concentration (MBC)” against Enterococcus faecalis (E. faecalis) and Aggregatibacter actinomycetemcomitans (A.a). The cell viability test (MTT) was conducted for cytotoxicity against human gingival fibroblasts. SEM micrographs of PPCMs showed spherical-like ceramic microparticles with smooth surfaces. Crystal-like antibiotic particles (chunks) were also found on PPCMs. Initial burst of antibiotics (31 µg/mL, 160 µg/mL, and 18 µg/mL for ciprofloxacin, metronidazole, and penicillin G, respectively, in the first 4 days) followed by gradual and sustained release was observed within a period of 21 days. PPCMs demonstrated pH close to normal physiological environment and antibacterial activity against E. faecalis and A.a in the first 2 days. MTT showed cell viability of more than 70% for PPCMs after 24 h and 72 h of exposure. In conclusion, PPCMs demonstrated satisfactory release of antibiotics, antibacterial activity against the selected microorganisms, and biocompatibility. Thus, PPCMs may be used to deliver modified triple antibiotics to the root canal system for use in endodontic applications.
Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing
Authors: Cao Xuefei , Fu Yulong , Chen Bo
Published: August 2020

In this paper, a network intrusion detection system is proposed using Bayesian topic model latent Dirichlet allocation (LDA) for mobile edge computing (MEC). The method employs tcpdump packets and extracts multiple features from the packet headers. The tcpdump packets are transferred into documents based on the features. A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic. Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic. A threshold is defined in the training phase as the minimum likelihood of a host. In the test phase, when a host’s test traffic has a likelihood lower than the host’s threshold, the traffic is labeled as an intrusion. The intrusion detection system is validated using DARPA 1999 dataset. Experiment shows that our method is suitable to protect the security of MEC.
Representative Sample Sampling Method for Size Effect Experiment of Jointed Rock Mass
Authors: Huang Man , Liu Dan , Hong Chenjie , Du Shigui , Luo Zhanyou , Li Changhong , Huang Yongliang
Published: August 2020

The influence of size effect on the hydromechanical behavior of rock mass has long been recognized. As a result, analysis of the rock mass size effect has been developed. However, when conducting size effect studies, the representativeness of the sample is less considered. Therefore, combining the existing statistical methods and sampling methods, a comprehensive representative sampling method for rock mass is present. In this method, a large number of sample statistics of different sizes are provided in the progressive coverage method; then, the plane density of the track length is defined as the value of stratification to perform stratified sampling for representative samples. Furthermore, it is applied to a joint network model generated in accordance with the actual situation and compared with those of other methods. The sampling results show that the proposed method can provide a certain reference value for studying the scale-dependent behavior of rock mass mechanical parameters.
Outcome and Complications of Frontal Sinus Stenting: A Case Presentation and Literature Review
Authors: Khan Mohammed A. , Alshareef Waleed A. , Marglani Osama A. , Herzallah Islam R.
Published: August 2020

Introduction. Frontal sinus surgery remains challenging to manage because of its complex anatomy and narrow outflow tract. A number of studies suggest the success of frontal sinus stenting to reduce postoperative complications in endoscopic frontal sinus surgery. However, failure and complications of frontal sinus stenting may occur. Method. We present a case of frontal sinus stenting with migration of the stent and erosion of the lamina papyracea together with a granulomatous reaction around the stent. PubMed and Medline search was also conducted to study the current evidence on frontal sinus stenting benefits and complications. Results. Still there are no guidelines or universally accepted indications for the use of frontal sinus stenting in the literature. A limited number of studies suggest the success of frontal sinus stenting to reduce postoperative stenosis in endoscopic frontal sinus surgery. However, failure and complications of frontal sinus stenting may occur. Infection, pain, edema, and stent obstruction may also occur. Our case report also highlights the potential of orbital complications as well as the consequences of inducing a granulomatous reaction. Conclusion. The value of frontal sinus stenting is still a subject of debate. Complications of frontal sinus stenting are not uncommon and thus necessitate regular follow-up.
Antibody to CD137 Activated by Extracellular Adenosine Triphosphate Is Tumor Selective and Broadly EffectiveIn Vivowithout Systemic Immune Activation
Authors: Kamata-Sakurai Mika , Narita Yoshinori , Hori Yuji , Nemoto Takayuki , Uchikawa Ryo , Honda Masaki , Hironiwa Naoka , Taniguchi Kenji , Shida-Kawazoe Meiri , Metsugi Shoichi , Miyazaki Taro , Wada Naoko A. , Ohte Yuki , Shimizu Shun , Mikami Hirofumi , Tachibana Tatsuhiko , Ono Natsuki , Adachi Kenji , Sakiyama Tetsushi , Matsushita Tomochika , Kadono Shojiro , Komatsu Shun-ichiro , Sakamoto Akihisa , Horikawa Sayuri , Hirako Ayano , Hamada Koki , Naoi Sotaro , Savory Nasa , Satoh Yasuko , Sato Motohiko , Noguchi Yuki , Shinozuka Junko , Kuroi Haruka , Ito Ami , Wakabayashi Tetsuya , Kamimura Masaki , Isomura Fumihisa , Tomii Yasushi , Sawada Noriaki , Kato Atsuhiko , Ueda Otoya , Nakanishi Yoshito , Endo Mika , Jishage Kou-ichi , Kawabe Yoshiki , Kitazawa Takehisa , Igawa Tomoyuki
Published: August 2020

Agonistic antibodies targeting CD137 have been clinically unsuccessful due to systemic toxicity. Because conferring tumor selectivity through tumor-associated antigen limits its clinical use to cancers that highly express such antigens, we exploited extracellular adenosine triphosphate (exATP), which is a hallmark of the tumor microenvironment and highly elevated in solid tumors, as a broadly tumor-selective switch. We generated a novel anti-CD137 switch antibody, STA551, which exerts agonistic activity only in the presence of exATP. STA551 demonstrated potent and broad antitumor efficacy against all mouse and human tumors tested and a wide therapeutic window without systemic immune activation in mice. STA551 was well tolerated even at 150 mg/kg/week in cynomolgus monkeys. These results provide a strong rationale for the clinical testing of STA551 against a broad variety of cancers regardless of antigen expression, and for the further application of this novel platform to other targets in cancer therapy.

Significance:

Reported CD137 agonists suffer from either systemic toxicity or limited efficacy against antigen-specific cancers. STA551, an antibody designed to agonize CD137 only in the presence of extracellular ATP, inhibited tumor growth in a broad variety of cancer models without any systemic toxicity or dependence on antigen expression.

See related commentary by Keenan and Fong, p. 20.

This article is highlighted in the In This Issue feature, p. 1
Intratumoral Heterogeneity in Uveal Melanoma
Authors: Fonseca Cristina , Pinto-Proença Rita , Bergeron Sabrina , Pires Luís Miguel , Fernandes Júlia , Carreira Isabel Marques , Burnier Miguel N. , Proença Rui
Published: August 2020

Tumor biopsies in uveal melanoma (UM) serve mainly the purpose of prognostication and assessment of individual metastatic risk, but can be used for diagnosis in selected cases. The importance of precise information is paramount for selecting adequate surveillance protocols, patient counseling, and optimization of treatment strategies. However, intratumoral heterogeneity and sample representativity are major concerns and can interfere with the correct prediction of the patient’s prognosis. We report a series of cases of UM with distinct morphologically identifiable areas, highlighting the differences in clinical behavior, as well as histopathological and genetic features.
Red Blood Cell Distribution Is a Significant Predictor of Severe Illness in Coronavirus Disease 2019
Authors: Lippi Giuseppe , Henry Brandon M. , Sanchis-Gomar Fabian
Published: August 2020

Introduction: As red blood cell distribution width (RDW) significantly predicts clinical outcomes in patients with respiratory tract infections and in those with critical illnesses, we performed a critical analysis of the literature to explore the potential prognostic role of this laboratory parameter in coronavirus disease 2019 (COVID-19). Methods: An electronic search was conducted in Medline, Scopus and Web of Science, using the keywords “coronavirus disease 2019” OR “COVID-19” AND “red blood cell distribution width” OR “RDW” in all fields, up to the present time, with no language restriction. Studies reporting the value of RDW-CV in CO­VID-19 patients with or without severe illness were included in a pooled analysis. Results: The pooled analysis included 3 studies, totaling 11,445 COVID-19 patients’ samples (2,654 with severe disease; 23.2%). In all investigations RDW-CV was higher in COVID-19 patients with severe illness than in those with mild disease, with differences between 0.30 and 0.70%. The pooled analysis, despite consistent heterogeneity (I2: 88%), revealed that the absolute RDW-CV value was 0.69% higher (95% CI 0.40–0.98%; p < 0.001) in COVID-19 patients with severe illness compared to those with mild disease. Conclusion: These results, along with data published in other studies, support the use of RDW for assessing the risk of unfavorable COVID-19 progression.
Improved ant colony optimization for quantum cost reduction
Authors: Thakral Shaveta , Bansal Dipali
Published: August 2020

Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
Cerebral infarction classification using multiple support vector machine with information gain feature selection
Authors: Rustam Zuherman , Arfiani Arfiani , Pandelaki Jacub
Published: August 2020

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
The effect of a SECoS in crude palm oil forecasting to improve business intelligence
Authors: Al-Khowarizmi Al-Khowarizmi , Nasution Ilham Ramadhan , Lubis Muharman , Lubis Arif Ridho
Published: August 2020

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.
Design and implementation of grid based clustering in WSN using dynamic sink node
Authors: Kamil Aya Ahkam , Naji Maham Kamil , Turki Hasan Abdulhadi
Published: August 2020

A wireless sensor networks (WSNs) play a significant application, especially in the monitored remoting environmental, which enables by the availability of sensors which are cheaper, smaller, and intelligent. The equipment of such sensors be with wireless interfaces, which a communication with other sensors occurs for creating a network, that contains many distributed nodes. The closest nodes to the sink are exploited at an enormous traffic load while the data from the whole regions are forwarded between them to reach the sink. This result in exhausting their energy quickly and partitioning the network. This is solved by changing the sink node position in Grid based clustering technique, which considers the optimal method for this purpose. A simulation with MATLAB can be applied for grid based clustering technique to evaluate the performance of WSN. The expected results deal with outperforms in throughput, reducing energy consumption and increasing residual energy, in addition to prolong the network lifetime of the sensor network
Lightweight hamming product code based multiple bit error correction coding scheme using shared resources for on chip interconnects
Authors: Chlaab Asaad Kadhum , Flayyih Wameedh Nazar , Rokhani Fakhrul Zaman
Published: August 2020

In this paper, we present multiple bit error correction coding scheme based on extended Hamming product code combined with type II HARQ using shared resources for on chip interconnect. The shared resources reduce the hardware complexity of the encoder and decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 20% and 28% reduction in area and power consumption respectively, with only small increase in decoder delay compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 58% of total power consumption compared to the other error control schemes. The low complexity and excellent residual flit error rate make the proposed code suitable for on chip interconnection links.
Artificial intelligence in education and assessment methods
Authors: Braiki Balqis Al , Harous Saad , Zaki Nazar , Alnajjar Fady
Published: August 2020

Today, artificial intelligence has proliferated to reach almost every wing of daily life, perhaps one of the most sensitive of these being education. While teaching, insofar as it involves training human minds, is still mostly a form of art rather than a regular science, the taking up of this elitist job by computers has triggered much debate and controversy, involving the teaching community as much as the select corporate AI giants who strive to create computers capable of teaching better than humans. This paper surveys the most relevant studies carried out in this field to date. First, it introduces AI and describes the different AI applications in field of education and course assessment. It then goes on to list the most common topics in the educational context that have been resolved through AI and machine learning techniques, and finally, some of the most promising future lines of research are discussed.
Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator
Authors: Chamidah Nur , Yonani Yolanda Swastika , Ana Elly , Lestari Budi
Published: August 2020

Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.
Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm
Authors: Basri M. A. , Noordin A.
Published: August 2020

Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.
Design selective band antenna using coupling sidewall and multi resonator for wireless communications
Authors: Jassim Ali Khalid , Farhan Malik Jasim , Fahad Alaa Fadhil
Published: August 2020

Design and simulation of antenna with performance operating at bandwidth (4.5-6.5) GHz, with center frequency 5.5 GHz. This antenna constriction from two cylindrical antenna shapes and four cylindrical resonators with include iris to matching impedance, to enhancement sharp edge band and four tuned between coupling sidewall to removed distortions. Improving the frequency selection of the bandwidth requirement antenna is by adjusting the cylinder length of the antenna The proposed antenna is operating ideal filter because the edge of the band rejection is matched the edge of the bandwidth requirement which makes it perform well including broad edges for the band rejection and sharp edges of the bandwidth requirement together with a little insertion between two types and good return loss of the bandwidth requirement. CST software used to investigate and simulated results.
QoS of Wi-Fi performance based on signal strength and channel for indoor campus network
Authors: Ghafar Adiba Abd , Kassim Murizah , Ya’acob Norsuzila , Mohamad Roslina , Rahman Ruhani Ab
Published: August 2020

The implementation of Wi-Fi on campuses brings huge benefits for campus users in communications and education development. Some campuses face existing poor Quality of Service (QoS) on the Wi-Fi performance which is also unjustified and issues on unsatisfied connections. This research aims to analyses the Wi-Fi performance of the campus network for three indoor access points. Signal strength, usage percentage, channel utilization, and the number of clients count per access points are measured. Results show that 56% is the highest signal percentage and the lowest signal percentage is at 1%. Access point of Ustudent is the highest at 53% and lowest at 38%. The highest signal strength of -45dBm is found and the other two are below by -2 dBm. The lowest signal strength is at -95 dBm. SSID CCNA_1 is identified has the best signal strength compared to other SSIDs because it runs on 5 GHz frequency. The most channels used are channels 1, 4, and 6. Respectively 151, 67, and 57 users are connected to Ustudent, UHotspot, and CCNA_1. This study is significant for the QoS in a campus network in providing good network services. Thus, the QoS on Wi-Fi performance is improvised, monitored and analyzed for continues supports users in the campus network successfully
Prediction of land-change using machine learning for the deforestation in Paraguay
Authors: Muller Max , Vincent Shweta , Kumar Om Prakash
Published: August 2020

Northwestern Paraguay is being deforested at a very rapid rate. This article studies the rate of deforestation that has happened in this area using satellite images of LandSAT5 and LandSAT7. The rate of the deforestation is detected from 1986 to 2011, graphically using which future prediction is made. The images of LandSAT8 are used to validate the prediction made until 2018. An extrapolation of the graph shows that the process of deforestation is already 3 years ahead of its forecast. An overall accuracy of 98% has been achieved using this technique. The root mean squre error (RMSE) is around 0.011.