bluuebunny/crossref_metadata_2025 huggingface dataset:
Circuit-specific enteric glia regulate intestinal motor neurocircuits
Authors: Ahmadzai Mohammad M. , Seguella Luisa , Gulbransen Brian D.
Published: September 2021

Significance

Neurons have long been considered the main active cell type within the circuitry of the enteric nervous system (ENS), giving rise to a neurocentric paradigm that has tended to overlook the role of enteric glia as key regulators of gut motility. Understanding how simple synapse-level interactions give rise to complex, network-level behaviors remains a fundamental problem in neurogastroenterology. We show that enteric glia and neurons interact in a cell- and network-specific manner and that enteric glia display functional heterogeneity based on selective signaling with particular neuron subtypes and circuits belonging to overlapping ascending, descending, and circumferential pathways of the ENS. Enteric glia thus function as logic gates to modify neural network activity through purinergic and cholinergic mechanisms.
Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer
Authors: Bello Thomas , Paindelli Claudia , Diaz-Gomez Luis A. , Melchiorri Anthony , Mikos Antonios G. , Nelson Peter S. , Dondossola Eleonora , Gujral Taranjit S.
Published: September 2021

Significance

Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced prostate cancer with limited therapeutic options and poor patient outcomes. To investigate whether multitargeted kinase inhibitors (KIs) represent an opportunity for mCRPC drug development, we applied machine learning–based functional screening and identified two KIs, PP121 and SC-1, which demonstrated strong suppression of CRPC growth in vitro and in vivo. Furthermore, we show the marked ability of these KIs to improve on standard-of-care chemotherapy in both tumor response and survival, suggesting that combining multitargeted KIs with chemotherapy represents a promising avenue for mCRPC treatment. Overall, our findings demonstrate the application of a multidisciplinary strategy that blends bench science with machine-learning approaches for rapidly identifying KIs that result in desired phenotypic effects.
Structural and energetic analysis of metastable intermediate states in the E1P–E2P transition of Ca
2+
-ATPase
Authors: Kobayashi Chigusa , Matsunaga Yasuhiro , Jung Jaewoon , Sugita Yuji
Published: September 2021

Significance

Ion pumps (or P-type ATPases) are membrane proteins, which transport ions through biological membranes against a concentration gradient, a function essential for many biological processes, such as muscle contraction, neurotransmission, and metabolism. Molecular mechanisms underlying active ion transport by ion pumps have been investigated by biochemical experiments and high-resolution structure analyses. Here, the transition of sarcoplasmic reticulum Ca
2+
-ATPase upon dissociation of Ca
2+
is investigated using atomistic molecular dynamics simulations. We find intermediate structures along the pathway are stabilized by transient interactions between A- and P-domains as well as lipid molecules in the transmembrane helices.
Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression
Authors: Ray Dipanjan , Bezmaternykh Dmitry , Mel’nikov Mikhail , Friston Karl J. , Das Moumita
Published: September 2021

Significance

Research into neurobiology of depression primarily focuses on its complex psychological aspects. Here we propose an alternative approach and target sensorimotor alterations—a prominent but often neglected feature of depression. We demonstrated using resting-state functional MRI data and computational modeling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression-associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for an avenue of research into the neural underpinnings of mental health conditions.
Encoding hierarchical assembly pathways of proteins with DNA
Authors: Hayes Oliver G. , Partridge Benjamin E. , Mirkin Chad A.
Published: September 2021

Significance

Structural sophistication in nature would not be possible without hierarchical assembly: the concept that an initial building block (e.g., polypeptide) contains all necessary structural and chemical information to determine its assembly along a multistep pathway to generate a complex architecture (e.g., viral capsid). Translating this concept to synthetic systems is an outstanding challenge. Here, we use DNA—the blueprint of life—to direct the hierarchical assembly of proteins. Through DNA design, we can change the directionality of protein assembly and pathway by which protein–DNA conjugates will assemble as well as realize distinct structures by directing assembly along different pathways. These findings will facilitate the assembly of protein–DNA materials with structural complexity more closely approaching that observed in nature.
Ice needles weave patterns of stones in freezing landscapes
Authors: Li Anyuan , Matsuoka Norikazu , Niu Fujun , Chen Jing , Ge Zhenpeng , Hu Wensi , Li Desheng , Hallet Bernard , van de Koppel Johan , Goldenfeld Nigel , Liu Quan-Xing
Published: September 2021

Significance

Self-organization is increasingly recognized as fundamental to pattern formation in geomorphology. Relative to other fields, however, underlying mechanisms have received little attention from theoreticians. Here, we introduce phase separation theory to study the formation of sorted patterned ground in cold regions; “sorted” refers to the segregation of soil and stones due to feedbacks between stone concentration and recurring ice growth. Using detailed measurements of the concentration of stones in soil and their displacements, we demonstrate that phase separation accounts for the observed sorting and patterns. Our study highlights phase separation theory as a source of important insight into studying ground patterns in cold regions and their potential value in signaling important changes in ground conditions with the warming climate.
Glypican 4 mediates Wnt transport between germ layers via signaling filopodia
Authors: Hu Bo , Rodriguez Juan J. , Kakkerla Balaraju Anurag , Gao Yuanyuan , Nguyen Nhan T. , Steen Heston , Suhaib Saeb , Chen Songhai , Lin Fang
Published: September 2021

Glypicans influence signaling pathways by regulating morphogen trafficking and reception. However, the underlying mechanisms in vertebrates are poorly understood. In zebrafish, Glypican 4 (Gpc4) is required for convergence and extension (C&E) of both the mesoderm and endoderm. Here, we show that transgenic expression of GFP-Gpc4 in the endoderm of gpc4 mutants rescued C&E defects in all germ layers. The rescue of mesoderm was likely mediated by Wnt5b and Wnt11f2 and depended on signaling filopodia rather than on cleavage of the Gpc4 GPI anchor. Gpc4 bound both Wnt5b and Wnt11f2 and regulated formation of the filopodia that transport Wnt5b and Wnt11f2 to neighboring cells. Moreover, this rescue was suppressed by blocking signaling filopodia that extend from endodermal cells. Thus, GFP-Gpc4–labeled protrusions that emanated from endodermal cells transported Wnt5b and Wnt11f2 to other germ layers, rescuing the C&E defects caused by a gpc4 deficiency. Our study reveals a new mechanism that could explain in vivo morphogen distribution involving Gpc4.
Update of the BIPM comparison BIPM.RI(II)-K1.Co-60 of activity measurements of the radionuclide 60Co to include the 2019 result of the VNIIM (Russian Federation)
Authors: Michotte C , Courte S , Nonis M , Coulon R , Judge S , Alekseev I V , Zanevsky A V , Zhukov G V
Published: September 2021

Main text

Since 1976, 29 laboratories have submitted 73 samples of 60Co to the International Reference System (SIR) for activity comparison at the Bureau International des Poids et Mesures (BIPM), with comparison identifier BIPM.RI(II)-K1.Co-60. Recently, the VNIIM (Russian Federation) participated in the comparison and the key comparison reference value (KCRV) has been updated. The degrees of equivalence between each equivalent activity measured in the SIR and the updated KCRV have been calculated and the results are given in the form of a table. A graphical presentation is also given.

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Retrieval of the conductivity spectrum of tissues in vitro with novel multimodal tomography
Authors: Lehti-Polojärvi M , Räsänen M J , Viiri L E , Vuorenpää H , Miettinen S , Seppänen A , Hyttinen J
Published: September 2021

Objective: Imaging of tissue engineered three-dimensional (3D) specimens is challenging due to their thickness. We propose a novel multimodal imaging technique to obtain multi-physical 3D images and the electrical conductivity spectrum of tissue engineered specimens in vitro. Approach: We combine simultaneous recording of rotational multifrequency electrical impedance tomography (R-mfEIT) with optical projection tomography (OPT). Structural details of the specimen provided by OPT are used here as geometrical priors for R-mfEIT. Main results: This data fusion enables accurate retrieval of the conductivity spectrum of the specimen. We demonstrate experimentally the feasibility of the proposed technique using a potato phantom, adipose and liver tissues, and stem cells in biomaterial spheroids. The results indicate that the proposed technique can distinguish between viable and dead tissues and detect the presence of stem cells. Significance: This technique is expected to become a valuable tool for monitoring tissue engineered specimens’ growth and viability in vitro.
Electroforming free enhanced resistive switching in reduced graphene oxide films embedded with silver nanoparticles for nonvolatile memory applications
Authors: Singh Rakesh , Kumar Ravi , Kumar Anil , Kumar Dinesh , Kumar Mukesh
Published: September 2021

In this paper, the reduced graphene oxide (rGO)-based resistive random access memory (RRAM), embedded with Ag NPs, for nonvolatile memory applications has been investigated. The device exhibited enhanced I
on/I
off current ratio at lower switching voltage. The rGO-Ag thin film was deposited by spin-coating technique on an indium tin oxide coated glass substrate. Top contacts of aluminum (Al) were deposited by thermal evaporation technique for electrical measurements. The morphology of thin film has been characterized by scanning electron microscopy, and elemental quantification has been done using energy-dispersive x-ray spectroscopy. The structural and chemical analysis of thin film has been performed using x-ray diffraction (XRD) characterization and x-ray photoelectron spectroscopy, respectively. During applied potential, the device switches between two resistance states; low resistance state (LRS) and high resistance state (HRS). Initially when potential is applied across the device, the deposited thin film (rGO-Ag) was found in LRS which did not require initial forming voltage to activate the device. The requirement of this forming voltage may induce severe mechanical and electrical stress in the device and degrades the device performance. The rGO-Ag-based RRAM devices required (<1 V) to switch from LRS to HRS or vice versa with a large (∼103) on/off current ratio. The electrical stability of the devices has been tested for 4 × 103 s and repeatability for >60 cycles, which did not exhibit too much degradation. In rGO-Ag thin film-based RRAM devices, Ag NPs act as charge trapping sites, which results in enhanced switching characteristics. The trapping and detrapping mechanism of charges by Ag NPs is responsible for the enhanced switching characteristics in these films.
Electro-lattice actuator: a compliant high-contractile active lattice structure
Authors: Hoh Sam , Helps Tim , Diteesawat Richard Suphapol , Taghavi Majid , Rossiter Jonathan
Published: September 2021

Electro-ribbon actuators are high-performance electrically-driven artificial muscles with high flexibility, low mass, low power consumption, high contraction, and high force-to-weight ratio. They show great promise for driving the deployment of compact folding structures. This article presents the electro-lattice actuator (ELA), a compliant, three-dimensional, free-standing lattice structure that uses this phenomenon to contract to a flat sheet upon the application of a potential difference. The ELA was designed in the form of multiple interconnected buckled structures and fabricated using polyvinyl chloride sheets and tape and copper electrodes. The ELA structure was pre-set into an open-cell configuration by annealing in an oven. Isometric testing at varying compressions showed that the tensile stress of the proposed lattice actuator reaches a maximum of 184 Pa (a 472 Pa change in tensile stress compared with its unactuated state). A cuboid shaped ELA (13.6 cm length × 10.0 cm width × 5.4 cm height) achieved a contraction of 92.6% and a contraction rate of 35.6% s−1. The novel ELA opens up the use of electro-ribbon actuation to more complex and more effective 3D actuating and deploying structures.
Anomalous Hall effect of facing-target sputtered ferrimagnetic Mn4N epitaxial films with perpendicular magnetic anisotropy
Authors: Zhang Zeyu , Zhang Qiang , Mi Wenbo
Published: September 2021

Epitaxial Mn4N films with different thicknesses were fabricated by facing-target reactive sputtering and their anomalous Hall effect (AHE) is investigated systematically. The Hall resistivity shows a reversed magnetic hysteresis loop with the magnetic field. The magnitude of the anomalous Hall resistivity sharply decreases with decreasing temperature from 300 K to 150 K. The AHE scaling law in Mn4N films is influenced by the temperature-dependent magnetization, carrier concentration and interfacial scattering. Different scaling laws are used to distinguish the various contributions of AHE mechanisms. The scaling exponent γ > 2 for the conventional scaling in Mn4N films could be attributed to the residual resistivity ρ

xx0. The longitudinal conductivity σxx
falls into the dirty regime. The scaling of

ρ

AH

=
α

ρ

x
x
0

+
b

ρ

x
x

n

is used to separate out the temperature-independent ρ

xx0 from extrinsic contribution. Moreover, the relationship between ρ
AH and ρxx
is fitted by the proper scaling to clarify the contributions from extrinsic and intrinsic mechanisms of AHE, which demonstrates that the dominant mechanism of AHE in the Mn4N films can be ascribed to the competition between skew scattering, side jump and the intrinsic mechanisms.
Source space reduction for eLORETA
Authors: Faes A , de Borman A , Van Hulle M M
Published: September 2021

Objective. We introduce Sparse exact low resolution electromagnetic tomography (eLORETA), a novel method for estimating a nonparametric solution to the source localization problem. Its goal is to generate a sparser solution compared to other source localization methods including eLORETA while benefitting from the latter’s superior source localization accuracy. Approach. Sparse eLORETA starts by reducing the source space of the Lead Field Matrix using structured sparse Bayesian learning from which a Reduced Lead Field Matrix is constructed, which is used as input to eLORETA. Main results. With Sparse eLORETA, source sparsity can be traded against signal fidelity; the proposed optimum is shown to yield a much sparser solution than eLORETA’s with only a slight loss in signal fidelity. Significance. When pursuing a data-driven approach, for cases where it is difficult to choose specific regions of interest, or when subsequently a connectivity analysis is performed, source space reduction could prove beneficial.
Prediction of polar vortex intensity signal based on convolution smoothing and long short-term memory
Authors: Peng Kecheng , Cao Xiaoqun , Xiao Chaohao , Tian Wenlong
Published: September 2021

Polar vortex is an important weather system that affects the atmospheric circulation in the Northern Hemisphere and the climate change in the Arctic. The intensity variation of polar vortex is related to El Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO) and many other climate phenomena. However, there are few researches on the prediction of polar vortex intensity change, our study analyzes and predicts the intensity variation of the Northern Hemisphere stratospheric polar vortex, and further uses convolution smoothing and depth learning methods to improve the accuracy of the prediction. The result shows that the long-short time memory network method’s prediction accuracy is not enough high. After the convolution smoothing of the time series of intensity signal, the prediction accuracy of neural network has been significantly improved. The average absolute error of the traditional long short-term memory network method is 18.29, while the average absolute error of the smoothed prediction intensity and the actual intensity is 13.77. In addition, the correlation between the predicted results and the real values is also as high as 0.9981.
Analysis and Research on Radar Gesture Micro-motion Feature Extraction Method
Authors: Dong Yaoyao , Qu Wei , Gao Tianhao , Wang Pengda , Jiang Haohao
Published: September 2021

Radar gesture recognition technology is widely used in daily life, and the micro motion feature is the key information to distinguish the feature quantity of different gestures. The extraction of micro motion feature is the key step of radar gesture recognition. Joint time-frequency analysis is a common method for radar signal micro motion feature extraction. Firstly, this paper makes a theoretical analysis of the common joint time-frequency analysis methods; Then radar gesture model is completed, and the performance of different JTFA is compared by extracting the time-frequency features of the motion gesture echo; Finally, the measured data are collected under the condition of microwave anechoic chamber. After analysis, the theoretical simulation results are consistent with the measured data, which verifies the reliability of the simulation.
Residual Attention Fusion Network for Single Image Super-Resolution
Authors: Zhang Hao , Lan Chuwen , Gao Zehua
Published: September 2021

Recently, a very deep convolutional neural network (CNN) demonstrated influential performance in the field of single image super-resolution (SISR). However, most of the CNN-based methods focus on designing deeper and wider network structures alone and do not use the hierarchical and global features in the input image. Therefore, we proposed a residual attention fusion network (RAFN), which is an improved residual fusion (RF) framework, to effectively extract hierarchical features for use in single-image super-resolution. The proposed framework comprises two residual fusion structures composed of several residual and fusion modules, and a continuous memory mechanism is realized by adding a long and short jump connection. The network focuses on learning more effective features. Furthermore, to maximize the power of the RF framework, we introduced global context attention (GCA) module that can model the global context and capture long-distance dependencies. The final RAFN was constructed by applying the proposed RF framework to the GCA blocks. Extensive experiments showed that the proposed network achieved improved performance in the SISR method with fewer parameters, as compared to the methods proposed in previous studies.
Ultrasound Image Super-Resolution with Two-Stage Zero-Shot CycleGAN
Authors: Ding Jianrui , Zhao Shili , Tang Fenghe , Ning Chunping
Published: September 2021

Medical ultrasound imaging is widely used in clinical diagnosis because of its non-invasive, convenient and quick characteristics. However, due to its low image contrast, multiple artifacts, noise and lack of paired high-resolution and low-resolution image data sets, the task of super-resolution reconstruction of medical ultrasound images is more challenging. In this paper, the Two-Stage GAN network model was adjusted by CycleGAN generation and unsupervised learning methods, and the Two-Stage ZSSR (“Zero-Shot” Super-Resolution) CycleGAN network was proposed. The objective evaluation indexes PSNR and SSIM were raised to 40.8079 and 0.9953. The visual effect was also significantly improved.
An Investigation about All-optical Millimeter Wave Generation Technology for High-speed Communication
Authors: Li Qiwei
Published: September 2021

With the advent of the network information age, users have higher and higher requirements for the bandwidth and capacity of communication systems. Traditional cellular mobile communications can no longer meet the needs of modern communication systems. It is necessary to find a communication system with larger capacity, higher bandwidth and higher transmission rate. Millimeter wave (MMW) communication has high bandwidth, large capacity and high signal transmission rate. Nowadays, all-optical MMW technology generates high-frequency MMW signals in optical domain, which has become a research hotspot of MMW signal generation technology at home and abroad. In this paper, three typical all-optical MMW generation methods are discussed, including direct modulation method, optical heterodyne method and external modulation method. The advantages and disadvantages of each method are analyzed, and the development prospects of the all-optical MMW generation technology are prospected. It is helpful to research new optical MMW technology in the future.
Key Technologies for Beamforming in Millimeter Wave Communication System
Authors: Qian Guanyu , Liu Weikun , Hao Dengfeng , Li Wei
Published: September 2021

Millimeter wave (mmWave) is one of the key technologies of 5G. It can solve the problem of insufficient spectrum resources. However, mmWave links are easily affected by the environment, such as the user equipment (UE) mobility, link blocking, etc. Moreover, the path loss and atmospheric absorption will also cause a great loss to the channel quality. Dealing with these challenges, beamforming is an effective solution. This paper tracks the progress of beamforming in mmWave communication system, including hybrid beamforming structure, mmWave channel state information (CSI) acquisition, and beam management methods during UE movement. We explore the applicability of different beamforming technologies, and discuss the future challenges in this domain.
Automatic Target Recognition of Millimeter-Wave Radar Based on Deep Learning
Authors: Wang Chen Xi , Chen Xin , Zou Han Yu , He Song , Tang Xiao
Published: September 2021

All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection.
Comment information extraction based on LSTM and Neural Networks
Authors: Zhang Qingliang , Ma Binning , Zhong Xier , Mei Liangcai , Zhou Youyu
Published: September 2021

With the advent of the era of big data, the amount of data has also increased geometrically. People’s ability to obtain effective information has gradually declined. At present, most e-commerce platforms only focus on the sentiment analysis of positive and negative reviews. It is difficult for users and businesses to extract user opinions and views from the massive review data. For the product review data of a certain hard disk, use the LSTM model to train the sentiment classification model. Finally, the neural network is used to find the keywords of the comment data and the word cloud diagram is used to display the analysis results. Through the research, it can be found that LSTM emotion classifier can classify comments with high accuracy and words closely related to comment emotion tendency can be found according to the weight of neural network.
A Channel Selection Algorithm of Power Line Communication Network Base on Double-layer Cascade Artificial Neural Network
Authors: Qu Beibei , Wang Huifang , Chen Zhixiong , Zheng Zhong , Han Zhen , Zhang Lixia
Published: September 2021

A crucial issue in Power Line Communication (PLC) networks is how to improve the performance of power line channel in physical layer. In this paper, a multiple input multiple output (MIMO) PLC channel selection algorithm based on a double-layer cascaded artificial neural network model is proposed. After the model is trained, the best channel combination can be selected directly through the channel estimation information. The results show that compared with the communication system without channel selection, the model can improve the channel by 2-3dB, the probability of selecting the best channel is 95.02%, and the training has fast convergence speed and small amount of calculation.
On the exact solutions for a type of nonlinear Schrödinger equations with a harmonic potential
Authors: Qin Yamei , Wang Hui , Su Tao , Dai Wang , Zhang Tao
Published: September 2021

The nonlinear Schrödinger equation with harmonic potential (NLSE) plays an important role in quantum mechanics, so the exact solutions of this equation is studied in this paper. The NLSE is transformed into the classical nonlinear Schrödinger equation by a new class of traveling wave transformation. Next, the problem of exact solutions is changed into the solutions of ordinary differential equation (ODE) by the method of undetermined function. Then, through low-order sub-ODE method and hyperbolic function method, we get two class of solutions of the ODEs. Finally, a series of new exact solutions of the NLSE are obtained. Meanwhile the related numerical simulations are presented.
Research on Splicing Technology of Large Format Three Dimensional Printing
Authors: Li Qingming , Yao Fengwei
Published: September 2021

With the development and maturity of 3D printing technology, there are new requirements for printing accuracy, clarity, and frame size. This article mainly focuses on large-format printing technology, using projection splicing technology to realize large-format digital light processing projection splicing 3D printing technology. Aiming at the problem of projection distortion, the article implements multiple digital light processing projection distortion correction; Aiming at the problem of image stitching, the article uses secondary Alpha edge fusion to realize image stitching, and gives an experimental environment to compare the results. According to the above steps, combined with actual pictures, a good splicing effect is achieved.
Face occlusion detection algorithm based on yolov5
Authors: Zhao Yuanzhang , Geng Shengling
Published: September 2021

The current face-mask recognition detection algorithm during the epidemic only distinguishes between wearing or not wearing a mask. Such detection often has certain loopholes, such as using other objects to cover their mouths and noses instead of masks to cheat the detection. To address such problems, this paper proposes a YOLOv5 based face occlusion detection algorithm, which is modified based on the YOLOv5 algorithm by improving the loss function as DIoU and increasing the experimental samples by introducing multiple data sets to improve the object detection effect. The experimental results show that the improved YOLOv5 algorithm has improved the object detection effect for different kinds of face occlusions, which verifies the method’s effectiveness.
Differential search algorithm for biojective feature selection
Authors: Du HongJun , Yao JingJun , Sun Gang , Li Ming , Zhou WuMing , Yu HaiYan , Guo ChangBiao , Zhang Shan
Published: September 2021

Feature selection (FS) is a complex optimization problem with important real-world applications. Generally, the main target of FS is to reduce the dimensionality of features and enhance the effectiveness of solving problems. Due to the population characteristics, various evolutionary algorithms (EAs) have been proposed to solve feature selection problems over the past decades. However, the majority of them only consider single-objective optimization, while many real-world problems have multiple objectives, therefore, it is need to design more suitable and effective EAs to deal with multi-objective FS. In this paper, a biobjective FS algorithm based on differential search algorithm (DSA) is designed to solve FS problems, which minimizes the number of selected features and maximizes the classification accuracy. The results of simulation experiments and statistical analysis on 15 classification datasets compared with other four state-of-the-art EAs show that the proposed DSA algorithm can not only obtain better optimization performance, but also achieve competitive convergence accuracy.
Heart Failure Prediction with Machine Learning: A Comparative Study
Authors: Wang Jing
Published: September 2021

Heart failure is a worldwide healthy problem affecting more than 550,000 people every year. A better prediction for this disease is one of the key approaches of decreasing its impact. Both linear and machine learning models are used to predict heart failure based on various data as inputs, e.g., clinical features. In this paper, we give a comparative study of 18 popular machine learning models for heart failure prediction, with z-score or min-max normalization methods and Synthetic Minority Oversampling Technique (SMOTE) for the imbalance class problem which is often seen in this problem. Our results demonstrate the superiority of using z-score normalization and SMOTE for heart failure prediction.
Design and simulation of Sierpmski Triangular Fractal Antenna for S-Band
Authors: Vyas Jahanvi H. , Gohil NB , Dwivedi Ved Vyas
Published: September 2021

In current wireless communication scenarios, you need to reduce the size of the antenna and achieve better performance without affecting other antenna settings. A fractal antenna is an antenna that uses a fractal and similar design to maximize the effective length of a material that can transmit and receive electromagnetic radiation in a specific total area, or to augment its environment (internal components or external structures) or the volume. Fractal antennas offer better parameters while providing better performance at many different frequencies. The Sierpinski triangle is a fixed set of fractal attractions. Its general shape is an equilateral triangle, which is recursively divided into smaller equilateral triangles. Sierpinski’s triangular fractal geometry is used to reduce antenna size and improve antenna performance at required frequencies. In this proposed task, a simple technique is used to design a Sierpinski triangular fractal antenna. These antennas are lightweight and small in size. This design covers the S band and is suitable for wireless applications.
Deep Neural Network for Electroencephalogram based Emotion Recognition
Authors: Garg Shruti , Behera Soumyajit , Patro K Rahul , Garg Ashwani
Published: September 2021

Emotion recognition using electroencephalogram (EEG) signals is an aspect of affective computing. The EEG refers to recording brain responses via electrical signals by showing external stimuli to the participants. This paper proposes the prediction of valence, arousal, dominance and liking for EEG signals using a deep neural network (DNN). The EEG data is obtained from the AMIGOS dataset, a publicly available dataset for mood and personality research. Two features, normalized and power and normalized wavelet energy, are extracted using Fourier and wavelet transform, respectively. A DNN with three different activation functions (exponential linear unit, rectified linear unit [ReLU] and leaky ReLU) has been applied for single and combined features. The result of combined features with leaky ReLU is found to be the best, with a classification accuracy of 85.47, 81.87, 84.04 and 86.63 for valence, arousal, dominance and liking, respectively.
Real-time Flow Visualization using Projection Mapping Technique
Authors: Nishanka T , Prajwal D , Nishmitha M , Bhat Vinayambika S
Published: September 2021

Flow visualization is a fundamental topic in scientific visualization and has been the subject of active research for a long time. Usually, information begins from numerical simulations, for example, those of computational fluid dynamics, and should be investigated by methods for perception to acquire a comprehension of the flow. With the rapid rise of computational power for simulations, the interest in further developed visualization strategies has thrived. The idea is to attain advanced visualization through the means of projection mapping. When incorporated with the properties of the fluid, whose flow is to be studied, we can create a visual simulation by extracting the real-time variations data through sensors and feed it to the projector, which results in a visualization method in the 3D plane. The complete process is made visible as the image is projected through light and also in space. Real-time Flow Visualization using Projection Mapping methods will help attain the same results as the conventional methods at less cost. As this process is crucial for many industries in the testing and design sectors, the desired output must be obtained within the given time constraint. This method will help to optimize the complete process. The result can be used to derive the necessary conditions for the best design and performance of the object or vehicle.
Influence of pH and salinity on the photocatalytic dye degradation and heavy metal ion reduction with cobalt ferrite photocatalysts
Authors: Chakkunny Anna Heba , Thomas Bintu , Alexander Libu K
Published: September 2021

The magnetic nanoparticle assisted photocatalytic oxidation-reduction is an effective method to remove and detoxify the organic dyes and the heavy metal ions present in the industrial wastewater. In the present study, the photocatalytic degradation of Methyl Orange(MO) dye and Cr(VI) ions in the presence of cobalt ferrite (CoFe2O4) superparamagnetic photocatalysts under visible light illumination are investigated as a function of wide range of pH and salinity conditions. The effect of pH on the photodegradation rates and the dual effect observed in the presence of Cl- ions were discussed. The photocatalytic degradation rates in single and binary impurity systems were also examined and the synergetic effect of MO oxidation and Cr(VI) reduction were established at different salinity and pH conditions.
Simulation Tests of Tokyo Quantum Network
Authors: Ceylan Osman , Yilmaz Ihsan
Published: September 2021

In this modern era where secure communication is imperative, scientists have always been in search of a new solutions for this problem. One of the best solutions discovered in this century, uses the superiorities of the quantum world. This theorem, called no cloning, mainly uses the collapse of quantum state when observing attribute of qubits. In this study, we will examine the Tokyo Quantum Network [1], which is based on the protocols derived from the BB84 [2] and B92 protocols by implementing in three quantum network simulators.
Selection of an efficient wind turbine generator at Government science college premises Chitradurga, India
Authors: Smitha patil G H , Megha R , Ravikiran Y T , Prashantkumar M , Udaykumar K , Kumaraswamy B G
Published: September 2021

Wind speed modelling is very crucial in estimating the potential of wind energy for a particular site. In this paper, the wind energy potential at the premises of Government Science College area [Lat. 14°13’′ N and Long. 76°27′ E] in Chitradurga town (Karnataka State, India) is statistically analyzed for the year 2019 – 2020. The speed of wind has been assessed at the height of 60 m from the ground level at the site. The seasonal and annual wind speed distributions (WSD) have been modelled using Weibull probability distribution functions (WPDF) i.e., shape parameter, k and scale parameters, c. With the use of them, the wind power density (WPD) has been determined. The WPD estimated from Weibull and normal methods has been found to be comparable. Further, at a height of 60 m above the ground, the capacity factors (CPs) of various marketablly useable wind turbine generators have been estimated to achieve the optimal power generation for the chosen site.
Optimized design of hydraulic silicone pad for OCA lamination of bended cover window in OLED display
Authors: Jang Dong-Hoon , Han Kwan-Young
Published: September 2021

In this study, we report the results of a new experiment using a hydraulically enabled silicone pad for OCA bonding in a cover window with three edges bent with 3R curvature. The silicone pad was designed using computer simulation to set eight important parameters in the cross-section of the pad and to optimize the shape according to the change in the curvature and length of the parameters. The compression strength of the designed silicone pad was analysed using simulation based on a hydraulic load. In addition, the hydraulic silicone pad, which was designed and manufactured in an optimized shape, was adhered to a pad plate to enable hydraulic pressure without oil leakage or outflow, and the OCA lamination was performed. Thus, we developed an optimized hydraulic silicone pad lamination method based on computer simulation and experimental results and provided a solution that can overcome the problems.
Undocumented citizens and the making of ID documents in Nigeria: an ethnography of the politics of suspicion in Jos
Authors: Fourchard Laurent
Published: September 2021

For 40 years, Nigeria has separated its citizens into two categories, ‘indigenes’ and ‘non-indigenes’. Indigene citizens can trace their genealogical roots back to a community in a locality. All local governments (LGs) in Nigeria issue certificates of indigene, which give access to the job market and university. This issuance of certificate of indigene has received scant academic attention despite the centrality of the indigeneity issue in Nigeria. In the two last decades, issuing certificates has become part of the political tensions and mass violence in Plateau State (Jos) as politicians and bureaucrats have transformed certificates to exclude most of the population from the local citizenship. Through an ethnographic approach, this article explores how a politics of suspicion can contribute to the growing literature on legal identification. Focusing on local authorities helps in understanding the centrality of suspicion in the making of new undocumented citizens which are not minority groups (foreigners, migrants and asylum seekers) usually targeted by national authorities. The complicated procedures to get certificates and the production of fake ones are an outcome of this politics of suspicion. The article also shows that a locally paper-based bureaucracy could expand at the same time as—and while being disconnected from—biometric identification.
Comprehensive geriatric assessment in newly diagnosed older myeloma patients: a multicentre, prospective, non-interventional study
Authors: Yao Yuan , Sui Wei-Wei , Liao Ai-Jun , Wang Wei , Chen Li-Juan , Chu Xiao-Xia , Bao Li , Cen Xi-Nan , Fu Rong , Liu Hui , Sun Chun-Yan , Jin Feng-Yan , Yan Hua , Wang Lu-Qun , Yuan Cheng-Lu , Gao Guang-Xun , Gao Da , Zhang Jin-Qiao , He Jian-Xia , Hu Jian-Da , Ma Liang-Ming , Zhang Lu , Zhou Dao-Bin , Zou De-Hui , Li Jian
Published: September 2021

Background

Multiple myeloma is a disease of the older people, whose prognoses are highly heterogeneous. The International Myeloma Working Group (IMWG) proposed a geriatric assessment (GA) based on age, functional status and comorbidities to discriminate between fit and frail patients. Given the multidimensional nature of frailty and the relatively recent exploration of frailty in the field of MM, reaching a consensus on the measurement of frailty in MM patients remains challenging.

Objective

We sought to assess the feasibility of performing a comprehensive GA (CGA) in older MM patients in a real-world and multicentre setting and to evaluate their baseline CGA profiles.

Results

We studied 349 older patients with newly diagnosed MM (age range, 65–86 years). Our results showed that a CGA is feasible for older MM patients. Using the IMWG-GA criteria, we identified significantly more frail patients in our cohort comparing to in the IMWG cohort (43% vs 30%, P = 0.002). In the IMWG-GA ‘fit’ group, risk of malnutrition, depression and cognitive impairment remains. The median follow-up time was 26 months (range 1–38). The median overall survival (OS) was 34.7 months, and the estimated 3-year OS rate was 50%. A high MNA-SF score (MNA-SF ≥ 12), low GDS score (GDS ≤ 5) and high CCI score (CCI ≥ 2) can be used to predict the OS of older patients with newly diagnosed MM. This study is registered at www.clinicaltrials.gov (NCT03122327).

Conclusions

Our study justifies the need for a CGA in older patients with newly diagnosed MM.
Twenty-First-Century Jewish Writing and the World
Authors: Goldman Dara E , Kaplan Brett Ashley
Published: September 2021

This introduction situates the essays about twenty-first century Jewish writing and the world in light of the exciting line up of writers who joined us at the University of Illinois for a series bridging fiction and scholarship. Nicole Krauss, Ruby Namdar, David Bezmozgis, and Ayelet Tsabari--Jewish writers from Israel, the U.S., and Canada, span a range of modalities and thematic concerns that ultimately illuminate the complexities of Jewish writing.
Airborne Fumigants and Residual Chemicals in Shipping Containers Arriving in New Zealand
Authors: Hinz Ruth , ’t Mannetje Andrea , Glass Bill , McLean Dave , Douwes Jeroen
Published: September 2021

Background

Airborne fumigants and other hazardous chemicals inside unopened shipping containers may pose a risk to workers handling containers.

Methods

Grab air samples from 490 sealed containers arriving in New Zealand were analysed for fumigants and other hazardous chemicals. We also collected grab air samples of 46 containers immediately upon opening and measured the total concentration of volatile organic compounds in real-time during ventilation. Additive Mixture Values (AMV) were calculated using the New Zealand Workplace Exposure standard (WES) and ACGIH Threshold Limit Values (TLV) of the 8-h, time-weighted average (TWA) exposure limit. Regression analyses assessed associations with container characteristics.

Results

Fumigants were detectable in 11.4% of sealed containers, with ethylene oxide detected most frequently (4.7%), followed by methyl bromide (3.5%). Other chemicals, mainly formaldehyde, were detected more frequently (84.7%). Fumigants and other chemicals exceeded the WES/TLV in 6.7%/7.8%, and 7.8%/20.0% of all containers, respectively. Correspondingly, they more frequently exceeded ‘1’ for the AMV-TLV compared to the AMV-WES (25.7% versus 7.8%). In samples taken upon opening of doors, fumigants were detected in both fumigated and non-fumigated containers, but detection frequencies and exceedances of the WES, TLV, and AMVs were generally higher in fumigated containers. Detection frequencies for other chemicals were similar in fumigated and non-fumigated containers, and only formaldehyde exceeded both the WES and TLV in both container groups. Volatile compounds in container air reduced rapidly during ventilation. Some cargo types (tyres; personal hygiene, beauty and medical products; stone and ceramics; metal and glass; and pet food) and countries of origin (China) were associated with elevated airborne chemical and fumigant concentrations.

Conclusion

Airborne chemicals in sealed containers frequently exceed exposure limits, both in fumigated and non-fumigated containers, and may contribute to short-term peak exposures of workers unloading or inspecting containers.
English and Development: Voices from a Rural Bangladeshi Madrasa
Authors: Chowdhury Qumrul Hasan
Published: September 2021

Because of its global status, donors and states promote English for economic development of countries in the global South such as Bangladesh. Governments, donors, and western leaders propose that people in madrasas (Islamic educational institutes) should learn English for development and security. But voices from madrasas are little heard. Based on a linguistic ethnography, this article presents some of these neglected perspectives on English for development at a private and unreformed Qawmi madrasa in rural Bangladesh. The findings show that people in the madrasa from a reflexively traditional stance, reject economic progress and western development programmes, and largely resist English as a language of development. These positions are considerably shaped by their commitments to religion and Arabic, supporting their alternative language and development visions. This article, based on the findings and drawing on Southern theory, interrogates the promotion of English for development, and asks how alert it is to heterogenous and complex southern realities.
‘Did You See That?’—The Role of Repetition and Enhancement on Lexical Bundle Processing in English Learning Materials
Authors: Northbrook Julian , Allen David , Conklin Kathy
Published: September 2021

For second-language learners, the use of formulaic language can benefit processing. Previous studies have explored the development of a ‘processing advantage’ for lexical bundles and investigated whether learning materials can be optimized via repetition or enhancement in order to facilitate it. However, studies have tended to consider these factors in isolation, and little is understood about how repetition and enhancement interact as learning occurs. This is the focus of the current study. Japanese learners of English read a series of short stories repeating enhanced or un-enhanced, low-frequency lexical bundles. After each reading, participants performed a phrasal judgement task (PJT) including these lexical bundles, as well as control lexical bundles not presented in the story. Responses were faster to items repeated in the stories, and there was an initial benefit from input enhancement. A post-test indicated that students retained some knowledge of the lexical bundles from both the stories, as well as from items that only appeared in the PJTs. This study shows that including high-frequency lexical bundles in such texts is beneficial to learners.