bluuebunny/crossref_metadata_2025 huggingface dataset:
A spatially explicit model framework to predict the spread of the noisy miner (
Authors: Priday Steve
Published: September 2021

Context The noisy miner (Manorina melanocephala) is an aggressive Australian honeyeater that has been strongly implicated in the decline of assemblages of smaller passerine species in eastern Australia. Factors likely to promote the spread of noisy miners throughout modified landscapes have been identified using static correlative models. However, the underlying mechanisms resulting in the patterns of space-use implicit in such models remain largely unknown. Aims The aim of the study was to develop a modelling context that is better able to capture the underlying mechanisms driving the spread of Noisy Miners than are static correlative modelling techniques. Methods A spatially explicit, grid-based model framework was derived to estimate the vulnerability of assemblages of bird species to displacement or replacement by noisy miners. Data from an area of suburban and peri-urban Brisbane were used as a case study in the application of the model framework. Key results The model framework predicted that sensitive bird assemblages, occupying a range of habitats, were far more vulnerable overall to displacement or replacement by noisy miners within the case study landscape than vice versa. The spaces within this landscape occupied by sensitive bird species predicted to be most vulnerable to noisy miner spread were identified. Conclusions The spatially explicit context within which the model framework is set provides an opportunity to gain insights into the mechanisms underlying the spread of noisy miners that static correlative models have yet to fully identify. Implications The model framework has potential for application in land use and conservation management planning.
Fe-catalyzed sulfide oxidation in hydrothermal plumes is a source of reactive oxygen species to the ocean
Authors: Shaw Timothy J. , Luther George W. , Rosas Richard , Oldham Véronique E. , Coffey Nicole R. , Ferry John L. , Dias Dewamunnage M. C. , Yücel Mustafa , Thibault de Chanvalon Aubin
Published: September 2021

Significance

The Fe-catalyzed oxidation of sulfide by dioxygen in hydrothermal vent plumes is shown to be a source of reactive oxygen species (ROS) to the deep ocean. ROS are a class of powerful oxidants, the most reactive of which can react with recalcitrant organic molecules at near diffusion limited rates. ROS production in hydrothermal systems may be comparable to the known photochemical yields of ROS in surface waters. The discovery of this abundant hydrothermal source of ROS demonstrates a mechanism for the alteration of refractory organic matter in the deep ocean.
PRAMEF2-mediated dynamic regulation of YAP signaling promotes tumorigenesis
Authors: Ghosh Madhurima , Das Sanjeev
Published: September 2021

Significance

PRAMEF2 belongs to the PRAME multigene family of cancer testis antigens, which serve as biomarkers for several cancers. However, its role in tumorigenesis remains unexplored. Here, we delineate PRAMEF2 regulation under low-nutrient conditions. We also show that it promotes proteasomal degradation of LATS1 kinase of the Hippo/YAP pathway. LATS1 down-regulation triggers nuclear accumulation of the transcriptional coactivator YAP, which induces the expression of proliferative and metastatic genes. Thus, PRAMEF2 promotes malignant phenotype in a YAP-dependent manner. Taken together, our findings reveal a pivotal role for PRAMEF2 in determining YAP oncogenic signaling, which has key implications for tumorigenesis.
Electrochemical removal of amphoteric ions
Authors: Shocron Amit N. , Guyes Eric N. , Rijnaarts Huub H. M. , Biesheuvel P. M. , Suss Matthew E. , Dykstra Jouke E.
Published: September 2021

Significance

Water treatment is required for a sustainable potable water supply and can be leveraged to harvest valuable elements. Crucial to these processes is the removal of charge pH-dependent species from polluted water, such as boron, ammonia, and phosphate. These species can be challenging for conventional technologies. Currently, boron removal requires several reverse-osmosis stages, combined with dosing a caustic agent. Capacitive deionization (CDI) promises to enable effective removal of such species without chemical additives but requires a deep understanding of the coupled interplay of pH dynamics, ion electrosorption, and transport phenomena. Here, we provide a detailed theory tackling this topic and show both theoretically and experimentally highly counterintuitive design rules governing pH-dependent ion removal by CDI.
Targeting the coronavirus nucleocapsid protein through GSK-3 inhibition
Authors: Liu Xiaolei , Verma Anurag , Garcia Gustavo , Ramage Holly , Lucas Anastasia , Myers Rebecca L. , Michaelson Jacob J. , Coryell William , Kumar Arvind , Charney Alexander W. , Kazanietz Marcelo G. , Rader Daniel J. , Ritchie Marylyn D. , Berrettini Wade H. , Schultz David C. , Cherry Sara , Damoiseaux Robert , Arumugaswami Vaithilingaraja , Klein Peter S.
Published: September 2021

Significance

COVID-19 is taking a major toll on personal health, healthcare systems, and the global economy. With three betacoronavirus epidemics in less than 20 y, there is an urgent need for therapies to combat new and existing coronavirus outbreaks. Our analysis of clinical data from over 300,000 patients in three major health systems demonstrates a 50% reduced risk of COVID-19 in patients taking lithium, a direct inhibitor of glycogen synthase kinase-3 (GSK-3). We further show that GSK-3 is essential for phosphorylation of the SARS-CoV-2 nucleocapsid protein and that GSK-3 inhibition blocks SARS-CoV-2 infection in human lung epithelial cells. These findings suggest an antiviral strategy for COVID-19 and new coronaviruses that may arise in the future.
Electron–phonon superconductivity in C-doped topological nodal-line semimetal Zr5Pt3: a muon spin rotation and relaxation (μSR) study
Authors: Bhattacharyya A , Ferreira P P , Panda K , Masunaga S H , de Faria L R , Correa L E , Santos F B , Adroja D T , Yokoyama K , Dorini T T , Jardim R F , Eleno L T F , Machado A J S
Published: September 2021

In the present work, we demonstrate that C-doped Zr5Pt3 is an electron–phonon superconductor (with critical temperature T
C = 3.8 K) with a nonsymmorphic topological Dirac nodal-line semimetal state, which we report here for the first time. The superconducting properties of Zr5Pt3C0.5 have been investigated by means of magnetization, resistivity, specific heat, and muon spin rotation and relaxation (μSR) measurements. We find that at low temperatures, the depolarization rate is almost constant and it can be well described by a single-band s‐wave model with a superconducting gap of 2Δ(0)/k
B
T
C = 3.84, somewhat higher than the value of BCS theory. From the transverse field μSR analysis, we estimate the London penetration depth λ
L = 469 nm, superconducting carrier density n
s = 1.83 × 1026 m−3, and effective mass m* = 1.428m
e. The zero field μSR confirms the absence of any spontaneous magnetic field in the superconducting ground state. In order to gain additional insights into the electronic ground state of C-doped Zr5Pt3, we also performed first-principles calculations within the framework of density functional theory (DFT). The observed homogenous electronic character of the Fermi surface as well as the mutual decrease of T
C and density of states at the Fermi level are consistent with the experimental findings of this study. However, the band structure reveals the presence of robust, gapless fourfold-degenerate nodal lines protected by 63 screw rotations and glide mirror planes. Therefore, Zr5Pt3 represents a novel, unprecedented condensed matter system to investigate the intricate interplay between superconductivity and topology.
25 years of RIFE—a brief history and highlights
Authors: Dewar Alastair , Dal Molin Franck , Clyne Fiona , Leech Nigel , Pemberton Ray , Sinclair Gillian , Toner Mark , Wilson Stephen , Bramley Gillian , Thomas Christopher , Munro Will , Abbott Fiona , Lombardi Karl
Published: September 2021

In 2020, the UK environmental regulators and food safety agencies, published the 25th edition of the Radioactivity in Food and the Environment (RIFE) report. This marks a quarter of a century since the landmark RIFE report was first published by the Ministry of Agriculture, Fisheries and Food in 1996, which represented the first joint monitoring and assessment report for the United Kingdom. This paper provides a summary of the RIFE report, how it has evolved and presents some case studies from over the 25 year period.
Evaluation of the effect of radioactivity to the environment in wooden houses within the evacuation area at Fukushima
Authors: Nakayama Kazumi , Sugita Yoshihide , Tezuka Hideaki , Takizawa Koichi , Muto Shoichi
Published: September 2021

The accident at Fukushima Daiichi Nuclear Power Plant caused radioactive materials to spread outside the plant. To limit exposure in the area, air dose rates have been measured. When the dose rate exceeded the acceptable upper bound, the area was decontaminated by stripping the soil and washing roads. Immediately after the accident, it was not clear how much of an effect outdoor contamination had on dose rates inside houses. This paper discusses the effect of outdoor contamination on indoor air dose rates and factors influencing those dose rates based upon actual measurements taken of a wooden house standing on flat land in the area around the power plant. In addition, the effects of indoor horizontal distribution, height distribution, and decontamination are discussed using simple model simulations. As a result, the following was found, although within the range of air dose rates and location conditions measured in this study. Horizontal variation in indoor air dose rates decreases monotonically toward the centre of the house. Once decontamination has been performed, the overall dose rate decreases and variation becomes smaller. This trend is thought to be mainly due to expansion of a low-dose area. Vertical changes in indoor air dose rates are not straightforward. It is estimated that the dose rate decreases near the ground surface, then tends to increase going higher and finally decreases even higher up. This change in dose rates is thought to be due to the effect of radioactive materials permeating the soil. Recognition of the effects of these factors is important when estimating indoor air dose rates because indoor air dose rates are affected by the degree of decontamination, house size, and degree of penetration of radionuclides into the soil.
Effect of lithium vapour shielding on hydrogen plasma parameters
Authors: Romano F , Rindt P , Scholten J , Hayashi Y , Morgan T W
Published: September 2021

A liquid Li vapour-box divertor is an attractive heat exhaust solution for future fusion reactors. Previous works have established the ability of vapour shielding to protect the wall, but it has not been possible to directly determine the effects of Li vapour on the plasma parameters. Experiments to investigate this were carried out in Magnum-PSI, which is able to generate a plasma with DEMO-divertor relevant conditions. 3D printed tungsten capillary porous structures filled with Li have been used as targets. A reciprocating Langmuir probe was used to determine electron temperature and density close to the target, while the power reduction to the coolant due to vapour shielding was increased from 0% to 50%. The Langmuir probe measurements directly determined an increase of density by up to 50% while electron temperature could be inferred to have dropped by up to 33% compared to the solid target reference case.
Development of series SQUID array with on-chip filter for TES detector
Authors: Wu Wentao , Lin Zhirong , Ni Zhi , Li Peizhan , Liang Tiantian , Zhang Guofeng , Wang Yongliang , Ying Liliang , Peng Wei , Zhang Wen , Shi Shengcai , You Lixing , Wang Zhen
Published: September 2021

A cold preamplifier based on superconducting quantum interference devices (SQUIDs) is currently the preferred readout technology for the low-noise transition edge sensor (TES). In this work, we have designed and fabricated a series SQUID array (SSA) amplifier for the TES detector readout circuit. In this SSA amplifier, each SQUID cell is composed of a first-order gradiometer formed using two equally large square washers, and an on-chip low pass filter (LPF) as a radio-frequency (RF) choke has been developed to reduce the Josephson oscillation interference between individual SQUID cells. In addition, a highly symmetric layout has been designed carefully to provide a fully consistent embedded electromagnetic environment and achieve coherent flux operation. The measured results show smooth V–Φ characteristics and a swing voltage that increases linearly with increasing SQUID cell number N. A white flux noise level as low as 0.28 μ Φ
0/Hz1/2 is achieved at 0.1 K, corresponding to a low current noise level of 7 pA/Hz1/2. We analyze the measured noise contribution at mK-scale temperatures and find that the dominant noise derives from a combination of the SSA intrinsic noise and the equivalent current noise of the room temperature electronics.
Effect of viscosity on stability and accuracy of the two-component lattice Boltzmann method with a multiple-relaxation-time collision operator investigated by the acoustic attenuation model
Authors: Bai Le , Shan Ming-Lei , Yang Yu , Su Na-Na , Qian Jia-Wen , Han Qing-Bang
Published: September 2021

A two-component lattice Boltzmann method (LBM) with a multiple-relaxation-time (MRT) collision operator is presented to improve the numerical stability of the single relaxation time (SRT) model. The macroscopic and the momentum conservation equations can be retrieved through the Chapman-Enskog (C-E) expansion analysis. The equilibrium moment with the diffusion term is calculated, a diffusion phenomenon is simulated by utilizing the developed model, and the numerical stability is verified. Furthermore, the binary mixture channel model is designed to simulate the sound attenuation phenomenon, and the obtained simulation results are found to be consistent with the analytical solutions. The sound attenuation model is used to study the numerical stability and calculation accuracy of the LBM model. The simulation results show the stability and accuracy of the MRT model and the SRT model under different viscosity conditions. Finally, we study the influence of the error between the macroscopic equation of the MRT model and the standard incompressible Navier–Stokes equation on the calculation accuracy of the model to demonstrate the general applicability of the conclusions drawn by the sound attenuation model in the present study.
Light-modulated electron retroreflection and Klein tunneling in a graphene-based n–p–n junction
Authors: Zhou Xingfei , Wu Ziying , Bai Yuchen , Wang Qicheng , Zhu Zhentao , Yan Wei , Xu Yafang
Published: September 2021

We investigate the electron retroreflection and the Klein tunneling across a graphene-based n–p–n junction irradiated by linearly polarized off-resonant light with the polarization along the x direction. The linearly polarized off-resonant light modifies the band structure of graphene, which leads to the anisotropy of band structure. By adjusting the linearly polarized light and the direction of n–p–n junction simultaneously, the electron retroreflection appears and the anomalous Klein tunneling, the perfect transmission at a nonzero incident angle regardless of the width and height of potential barrier, happens, which arises from the fact that the light-induced anisotropic band structure changes the relation of wavevector and velocity of electron. Our finding provides an alternative and flexible method to modulate electron retroreflection and Klein tunneling.
Assessment of Multisource Remote Sensing Image Fusion by several dissimilarity Methods
Authors: Li Feiyan
Published: September 2021

Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. Remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details and valuable information from multi-source remote sensing images, which aim to obtain more comprehensive and precise observations of the ground object. By using aspects from multi-source image fusion, this review presents the current status and future trends in remote sensing image fusion. First, different image properties and their applications are presented for remote sensing datasets at home and abroad. Second, a general summary and inductive analysis of the challenging difficulty of different types of multisource image fusion methods is conducted. Third, experiments are tested on eight different methodological approaches, and experimental results demonstrate that GSA method is the best alternative in terms of obtaining high spatial resolution and retaining the spectral information.
A Fast Instantaneous Frequency Estimation for Underwater Acoustic Target Feature Extraction
Authors: Ma Yanxin , Zhang Yifan , Zhu Jiahua , Xu Ke , Cai Yujin
Published: September 2021

Traditional auditory features merely present the amplitude characteristics of target signals in frequency domain. Such features are susceptible to environmental noise, resulting in significant degradation of recognition stability. Inspired by instantaneous information applied in speech signal processing field, this paper proposed a feature extraction method using sub-based instantaneous frequency. A fast instantaneous frequency information extraction algorithm is proposed with the normalized Gammatone filterbanks. Experiments confirm that the proposed feature extraction method can effectively maintain the recognition accuracy under low SNR conditions while reduce the computation cost.
Two new methods for facial expression recognition using Convolutional Neural Networks
Authors: Wang Jinfeng , Wang Xuegang
Published: September 2021

In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.
Research and application of substation cable trench inspection robot communication system
Authors: Sun Tao , Ye Liangpeng , Xie Jia , Zhang Jiaqing , Fan Minghao , Fan Hao
Published: September 2021

The substation cable trench inspection robot is mainly used to inspect the high-voltage cables in the underground cable trench of the substation, so as to find various hidden dangers in time. However, due to serious electromagnetic interference in the cable trench and no communication network underground, the environment is more complicated. Aiming at the complex environment in the cable trench, this paper designs a WLAN-based cable trench inspection robot communication program, combining WLAN with directional antennas, and relaying through relay routing, so that the communication between the robot and the PC is divided into pipeline communication and ground communication Two parts. At the same time, the communication hardware, software, and protocol are designed based on the B/S architecture, which effectively improves the communication distance. Through field verification, the packet loss rate of the communication system proposed in the thesis is less than 0.5‰, the communication is stable, and the communication task of the robot in the cable trench of the substation can be completed well.
Speech error recognition based on broad learning
Authors: Yan Yicheng , Wang Liwen , Guo Wei
Published: September 2021

In recent years, with the increasing popularity of Chinese in the world, more foreigners have begun to learn Chinese. In this regard, this paper studies the phonetic errors in a single sentence, and designs a phonetic error classification model based on the broad learning network. First, the Laplace wavelet convolutional layer is designed as the first layer of the traditional CNN, and the Morlet-CNN model is used to extract the speech signal. Then, the extracted speech signal is input into broad learning network for training. Finally, the speech with different error types is tested. By comparing with the experimental results of a single speech amplitude feature and MFCC feature, the classification accuracy of the proposed model has been significantly improved, which verifies the effectiveness of the model.
Energy-Saving Technology in Finite Element Calculation
Authors: Wang Feng , Han Qianwen , Ma Yurong , Jiang Yaqiong , Li Dan , Zhou Wei
Published: September 2021

In the finite element calculation of three-dimensional impact mechanics, there are usually such problems as large memory consumption and more machine consumption, which leads to a great waste of power resources. Based on the analysis of the calculation process of three-dimensional penetration sliding surface, a method of improving the calculation efficiency is proposed, which is the cell search method, based on the comparison calculation and without affecting the calculation accuracy. This method only judges the main elements in the cell involved by the slave nodes in the penetration channel area, greatly reduces the calculation amount of the elements, and improves the efficiency of the program calculation. The method is applied to the programming of finite element, and the results are satisfactory. The application of cell search method has played a good role in promoting the development of energy-saving technology.
Research on online signature verification based on anomaly detection
Authors: Xia Xinghua , Wang Shuang
Published: September 2021

With the advancement of modern information technology, personal information security has become the focus of social attention. As a biological behavior feature, online signatures have been trained for a long time, have very personal characteristics, and are collected as a real-time sequence. In view of this characteristic, we can know that it is difficult for forgers to imitate the intrinsic and essential characteristics of the signer. In addition, a signature is a rapid writing process driven by the central nervous system and personal writing habits. Even the signer himself may write an unstable and similar forgery signature, so it is necessary to analyze the variability and stability of signatures. Therefore, this paper proposes a method of using the variance and characteristics of local features to find a stable segment of the user’s signature for authentication. In addition, we also adopt a two-stage authentication strategy that combines the above method with the traditional anomaly detection algorithm. An experiment was conducted on the open-access online signature database MCYT, and an EER of 2.63% was obtained. The experiment proved the effectiveness and robustness of the method.
Improving Non-Autoregressive Machine Translation via Autoregressive Training
Authors: Wang Shuheng , Shi Shumin , Huang Heyan , Zhang Wei
Published: September 2021

In recent years, non-autoregressive machine translation has attracted many researchers’ attentions. Non-autoregressive translation (NAT) achieves faster decoding speed at the cost of translation accuracy compared with autoregressive translation (AT). Since NAT and AT models have similar architecture, a natural idea is to use AT task assisting NAT task. Previous works use curriculum learning or distillation to improve the performance of NAT model. However, they are complex to follow and diffucult to be integrated into some new works. So in this paper, to make it easy, we introduce a multi-task framework to improve the performance of NAT task. Specially, we use a fully shared encoder-decoder network to train NAT task and AT task simultaneously. To evaluate the performance of our model, we conduct experiments on serval benchmask tasks, including WMT14 EN-DE, WMT16 EN-RO and IWSLT14 DE-EN. The experimental results demonstrate that our model achieves improvements but still keeps simple.
Ranking Consistency Analysis and Evaluation of Multiple Attribute Decision Making Method
Authors: Zhang Lixia , Qu Beibei , Gao Huisheng , Zhang Jianliang
Published: September 2021

In the actual decision-making process, different multiple attribute decision making methods used for the same decision-making problem may get different ranking results. This inconsistent ranking can easily lead to decision-making risks. This paper simulates the ranking consistency of six typical multiple attribute decision making methods, including the ranking consistency indexes, consistency probability, the overall consistency of the decision-making methods, and the influence of attribute number and attribute weight on ranking consistency. The results show that various methods have different ranking consistency indexes; the overall consistency of decision-making methods increases with the decrease of the number of attributes; under the condition of equal attribute weights, the decision-making methods have generally better ranking consistency. The research results have reference value for the selection of multiple attribute decision making methods and attribute optimization.
Analysis on Heterogeneous Computing
Authors: Song Changxu
Published: September 2021

In the Internet industry, with the popularization of informatization and the rapid increase in data volume, people have new requirements for storage space. At the same time, computer applications such as artificial intelligence and big data have rapidly increased demand for computing power and diversified application scenarios. Heterogeneous computing has become the focus of research. This article introduces the choice of architecture for heterogeneous computing systems and programming languages for heterogeneous computing. Some typical technologies of heterogeneous computing are illustrated, including data communication and access, task division and mapping between processors. However, this also brings difficulties. The challenges facing hybrid parallel computing, such as programming difficulties, poor portability of the algorithm, complex data access, unbalanced resource load. Studies have shown that there are many ways to improve the status quo and solve problems, including the development of a unified programming method, a good programming model and the integration of storage and computing, intelligent task allocation, as well as the development of better packaging technologies. Finally, the application prospects and broad market prospects of heterogeneous computing systems are prospected. In the next ten years, due to the various advantages of heterogeneous computing systems, innovation in more fields will be stimulated and heterogeneous computing systems will shine in the AI artificial intelligence fields such as smart self-service equipment, smart robots, and smart driving cars. Moreover, this emerging technology will bring new industries and new jobs, thereby driving economic prosperity and social development and even benefiting the entire human society.
Impact Time Control Guidance Law for Large Initial Lead Angles Based on Sliding Mode Control
Authors: Liu Shuangxi , Liu Wei , Yan Binbin , Liu Shijun , Yin Yingming
Published: September 2021

In view of the interception scenario where a missile intercepts a stationary target in the longitudinal plane, an impact time control guidance (ITCG) law is proposed by utilizing the sliding mode control (SMC). An amendatory approach of t

go
(time-to-go) is adopted where there is no need to take the small-angle assumption into consideration. Under the basis of SMC, the error of impact time is selected as a sliding mode variable, which can converge to be zero in finite time by using the fast power reaching law (FPRL). The guidance law performance has been conducted in one-to-one engagement with different desired impact time. Simulation results demonstrate that the proposed guidance scheme can operate effectively even with a large initial lead angle of the missile, which broadens the initial launch conditions of the missile as well as improving the application capability of the guidance law.
Beamforming against main lobe interference based on radial basis function neural network
Authors: Wang Shuai , Xiang Jianjun , Peng Fang , Li Zhijun , Li Haoyang
Published: September 2021

Aiming at the problem that the performance of traditional beamforming algorithm deteriorates sharply in the presence of main lobe interference, a beamforming algorithm based on radial basis function (RBF) neural network is proposed. Firstly, the minimum variance distortionless response (MVDR) is used to solve the optimal beam pattern in the presence of side lobe interference. Then, the training set of RBF neural network is constructed according to the optimal beam pattern and the direction information of main lobe interference to train the network, so that the trained RBF neural network can suppress the main lobe interference while maintaining the ability of optimal beamforming. The simulation results show that the method can overcome the limitations of traditional beamforming algorithm, suppress the main lobe interference and side lobe interference, and form the correct beam direction. At the same time, the algorithm also has good real-time performance.
Design and Implementation of an Early Warning System Based on the Risk Measurement Model
Authors: Feng Su , Li Jing , Wang Huan , Tang Qi , Liu Liqiong , Yin Fengnian
Published: September 2021

In recent years, there are still some defects in the risk management of China’s inspection and quarantine work, such as the omission of major factors in the conformity assessment of inbound and outbound commodities and the subjective and unscientific assignment of risk indicators, which affect the ability of risk monitoring. Therefore, this paper constructs an inspection and quarantine risk measurement model based on random forest algorithm, and develops a risk early warning system for inspection and quarantine business. Firstly, according to the target requirements, the training data set is extracted from the original rough data set, and the overall data is cleaned and modified. Secondly, the integrated machine learning model is used to select the feature values with better prediction ability. Then, the risk measurement model is constructed based on random forest algorithm and deployed in multi-model parallel mode. Finally, a complete risk early warning system is developed based on the risk prediction model. Through the operation of this system, the abilities of risk analysis and discrimination for inspection and quarantine have been greatly improved, and the comprehensiveness and accuracy of risk prevention and control have been effectively guaranteed.
Low Power Memory System Design Using Power Gated SRAM Cell
Authors: Pal Srijani , Salimath Divya S , Chandran Banusha , Anita Angeline A , Kanchana Bhaaskaran V S
Published: September 2021

Static Random-Access Memory (SRAM) is widely used in cache memory, microprocessors, general computing applications and electronic circuits involving ASIC, FPGA and CPLD. The most commonly used SRAM is the 6T SRAM. However, it incurs higher power consumption and degraded signal to noise margin (SNM) during write and read operations. To overcome these shortcomings, a single ended power gated 11T SRAM for low power operation is proposed. The power consumption reduction is achieved using power gating through virtual VSS (VVSS) signal and transmission gates. Due to the introduction of transmission gates, memory cells realize enhanced write margin characteristics as compared to existing technologies. The proposed cell realizes 33.33% lower power consumption and 50% improvement in read SNM as compared to existing SRAM technologies. To study the impact of technology scaling on our proposed design, the work is carried out in Cadence Virtuoso® tool using both 180nm CMOS technology and BPTM 32nm FinFET technology.
A Different Shaped Radiating Element Wide Band Multi-Band Massive MIMO Antenna for 5G/WLAN applications with Enhanced Correlation Coefficient
Authors: Sarade Shrenik Suresh , Ruikar S. D.
Published: September 2021

In this paper, a different shaped 8-element multiband multiple input multiple output (MIMO) massive antenna is investigated for 5G and various applications. It can be operated on various wireless communication application such as WLAN (5.15GHz to 5.30GHz and 5.73GHz to 5.85GHs) and 5G band & C-band (4 to 8GHz) for satellite communication with wideband. The 8-element Massive MIMO antenna is designed for 6GHz. This antenna is working on multiband with wideband bandwidth. The defected ground structure (DGS) systems are used for enhancement of return loss, isolation, total active reflection coefficients (TARC), correlation coefficients (CC) and envelope correlation coefficients (ECC). The rectangular shaped DGS is used in the ground plane for improvement of various parameter of antenna. This antenna provides return loss (RL) and isolation is less than -10dB.
Analysis of an algorithm used for finding Unipolar Orthogonal Codes
Authors: Vinutha J N , Trivedi Hitesh , Tarannum Suraiya , Harshavardhana M N
Published: September 2021

An Auto-Correlation and Cross-Correlation property of an Orthogonal Code enables the transfer of data with less interference. With more Orthogonal Codes, an Optical Fiber Communication channel can support a large number of users efficiently and reliably with high bandwidth. This paper analyses an algorithm used for finding Unipolar Orthogonal Codes using four major parameters (n, w, λa, and λc) where n is the length of a binary string, w is the weight of the string n (number of one’s in the string), λa is the Auto-Correlation, and λc is the Cross-Correlation. These four parameters jointly with the Johnson Bound (JA) condition are used in the algorithm. The paper illustrates the analysis of a possible number of Orthogonal Code Sets calculated using Orthogonal Codes generated from a software code that is based on the algorithm in consideration. The data analysis is carried out and the findings are summarized.
Symmetric Key Cryptosystem based on Sequential State Machine
Authors: Kodada Basappa B , D’Mello Demian Antony
Published: September 2021

Cryptography is a type of art that translate strings of plain text into strings of cipher text and is very important in sharing of secrete information in communication system. It is very essential to have cryptographic algorithms to provide high security by utilizing optimum computational resources during information sharing to achieve data confidentiality, privacy, integrity and other security services. The traditional conventional encryption algorithms (DES, AES) have high computational cost due to generating number of sub keys and bilinear concept. Hence this paper propose the sequential state machine (Mealy state Machine) based cryptosystem which is very light weight in nature and provide high security due to its weakly invertability concept with relatively smaller key size. The paper also presents the performance analysis on proposed algorithm and describes the security analysis on it.
Thermomechanical characterization of functionally stabilized nickel-titanium-copper shape memory alloy
Authors: Iyer Sanesh , Hubert Pascal
Published: September 2021

Shape memory alloy hybrid composites have promise in realizing the 21st century goal of morphing structures. There is considerable work to be done in the development of characterization and modeling techniques for these materials. The proposed characterization methodology adapts existing standards to include previously omitted factors required for the numerical modelling of shape memory alloys and their integration into end-use applications. A nickel-titanium-copper (NiTiCu) shape memory alloy is characterized using these methods and then numerically modelled. Samples’ mechanical behaviour is shown to stabilize after 43 cycles of mechanical loading. Thermomechanical properties measured before and after stabilization are shown to vary inconsistently by up to 72%, demonstrating the need for stabilization for accurate thermomechanical characterizations and consistency in end-use applications. Physical experiments are numerically replicated in Abaqus\Standard using the measured properties. Sufficient correlation is shown for the design of shape memory alloy hybrid composites. The result of this work is a comprehensive thermomechanical characterization approach for shape memory alloys which can be used to develop morphing SMA hybrid composite structures.
Effects of functional task exercise on everyday problem-solving ability and functional status in older adults with mild cognitive impairment—a randomised controlled trial
Authors: Law Lawla L F , Mok Vincent C T , Yau Matthew K S , Fong Kenneth N K
Published: September 2021

Objective

To investigate the effect of functional task exercise on everyday problem-solving ability and functional status in older adults with mild cognitive impairment compared to single exercise or cognitive training and no treatment control.

Design

A single-blind, four-arm randomised controlled trial.

Setting

Out-patient clinic and community centre.

Participants

Older adults with mild cognitive impairment aged ≥60 living in community.

Methods

Participants (N = 145) were randomised to 8-week functional task exercise (N = 34), cognitive training (N = 38), exercise training (N = 37), or wait-list control (N = 36) group. Outcomes measures: Neurobehavioral Cognitive Status Examination, Category Verbal Fluency Test, Trail Making Test, Problems in Everyday Living Test, Activities of Daily Living Questionnaire, Instrumental Activities of Daily Living Scale; Chair stand test, Berg Balance Scale, and Short Form-12 Health Survey were conducted at baseline, post-intervention and 5-months follow-up.

Results

Post-intervention results of ANCOVA revealed cognitive training improved everyday problem-solving (P = 0.012) and exercise training improved functional status (P = 0.003) compared to wait-list control. Functional task exercise group demonstrated highest improvement compared to cognitive training, exercise training and wait-list control groups in executive function (P range = 0.003–0.018); everyday problem-solving (P < 0.001); functional status (P range = <.001–0.002); and physical performance (P = 0.008) at post-intervention, with all remained significant at 5-month follow-up, and further significant improvement in mental well-being (P = 0.043).

Conclusions

Functional task exercise could be an effective intervention to improve everyday problem-solving ability and functional status in older adults with mild cognitive impairment. The findings support combining cognitive and exercise intervention may give additive and even synergistic effects.
Medical cost of advanced illnesses in the last-year of life—retrospective database study
Authors: Kaur Palvinder , Wu Huei Yaw , Hum Allyn , Heng Bee Hoon , Tan Woan Shin
Published: September 2021

Objective

This study aims to quantify medical care utilisation, and to describe the cost trajectories of individuals with advanced illnesses in the last-year of life, differentiated by advanced cancer, end-stage organ failure and progressive neurological disorders.

Methods

This retrospective database study included decedents who had previous inpatient or outpatient encounters at a public hospital in Singapore. Patients with advanced diseases were identified based on diagnostic codes and clinical criteria. Using a look-back approach, the amount of healthcare services utilised and the corresponding mean monthly and annual costs to the healthcare system in the last 12-months of life were quantified.

Results

The last 12-months of life among 6,598 decedents was associated with £20,524 (95% confidence interval: £20,013–£21,036) in medical costs, of which 80% was accounted for by inpatient admissions. Costs increased sharply in the last 2-months of life, with a large proportion of monthly costs accounted for by inpatient admissions which rose rapidly from 61% at 12-months prior to death to 94% in the last-month of life. Compared to patients with cancer, individuals diagnosed with non-cancer advanced illnesses accumulated 1.6 times more healthcare costs in the last-year of life with significant differences across patients with end-stage organ failure and progressive neurological disorders.

Conclusion

Healthcare costs varied across disease conditions at the end-of-life. With advance care planning and close collaboration between the inpatient clinical team and the community providers, it may be possible to re-direct some of the hospitalisation costs to community-based palliative care services.
StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides
Authors: Singh Vishakha , Shrivastava Sameer , Kumar Singh Sanjay , Kumar Abhinav , Saxena Sonal
Published: September 2021

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.
A survey of circular RNAs in complex diseases: databases, tools and computational methods
Authors: Xiao Qiu , Dai Jianhua , Luo Jiawei
Published: September 2021

Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.
iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization
Authors: Yu Yingying , He Wenjia , Jin Junru , Xiao Guobao , Cui Lizhen , Zeng Rao , Wei Leyi
Published: September 2021

Motivation

DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different DNA methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific DNA methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on Bidirectional Encoder Representations from Transformers (BERT) together with transductive information maximization (TIM).

Results

Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation types. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT.

Availability and implementation

Our proposed iDNA-ABT and data are freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available for downloading in the GitHub repository (https://github.com/YUYING07/iDNA_ABT).

Supplementary information

Supplementary data are available at Bioinformatics online.