bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Evolution of Attachment Glycoprotein (G) and Fusion Protein (F) Genes of Respiratory Syncytial Virus ON1 and BA9 Strains in Xiamen, China
Authors: Sun Yong-Peng , Lei Si-Yu , Wang Ying-Bin , Wang Yi-Zhen , Qiang Hong-Sheng , Yin Yi-Fan , Jiang Ze-Min , Zhu Min , Chen Xiao-Li , Ye Hui-Ming , Zheng Zi-Zheng , Xia Ning-Shao
Published: March 2022

Monitoring the amino acid diversity of the RSV G and F genes helps us to find the novel genotypes, key antigenic mutations affecting antigenicity, or neutralizing antibody-resistant variants produced by natural evolution. In this study, we analyzed the molecular evolution of G and F genes from RSV strains circulating in Xiamen, China. These data provide new insights on local and global transmission and could inform the development of control measures for RSV infections.
Longitudinal SARS-CoV-2 Testing among the Unvaccinated Is Punctuated by Intermittent Positivity and Variable Rates of Increasing Cycle Threshold Values
Authors: Hawken Shawn E. , Sellers Subhashini A. , Smedberg Jason R. , Ward Jeremy D. , Elliott Avian M. , Whinna Herbert C. , Fischer William A. , Miller Melissa B.
Published: March 2022

We describe the duration of positivity and the COVID-19 treatment and outcome characteristics of an unvaccinated population of patients with prolonged SARS-CoV-2 positivity. This investigation serves to highlight challenges in using

C
T

values to guide clinical decisions among unvaccinated individuals.
Unintended insulin delivery using Omnipod DASH during a high-altitude flight
Authors: Shah Muneeb , Hadian Yasmin , Peltsverger Maya , Crane Jonathan
Published: March 2022

Insulin pumps are an important tool in the management of type I diabetes mellitus. Omnipod DASH is a tubeless insulin pump that delivers insulin through customisable basal rate and bolus amounts. There is no data available to date on how this insulin pump delivery system is influenced by changes in atmospheric pressure during flight at high altitudes. We report a case of a 55-year-old woman who developed symptomatic hypoglycaemia while using this pump during flight in a non-pressurised cabin. This report demonstrates the risk of insulin pump therapy at high altitudes. Flight emergencies involving rapid depressurisation may lead to catastrophic hypoglycaemia. Caution should be exercised when using insulin pump therapy during flights and manual insulin delivery should be considered.
High energy expenditure in a patient with feeding problems and Noonan syndrome spectrum disorder
Authors: Tiemens Dagmar , Wegberg Annemiek van , Druten Debbie van , Draaisma Jos
Published: March 2022

Feeding problems are present in more than 50% of patients with a Noonan syndrome spectrum disorder in the first years of life. Food intake problems like severe or frequently vomiting may not only affect growth and development, but may also influence the process of learning how to eat and can have a great impact on the whole family. In addition to food intake problems, there is growing evidence that patients with a Noonan syndrome spectrum disorder may have a lower body mass index (BMI) due to a high energy expenditure, although little is known about the actual energy intake patients with a Noonan syndrome spectrum disorder need to maintain a healthy BMI. This article illustrates the challenge to recognise a high energy expenditure especially when a patient frequently vomits. Multidisciplinary attention is needed to manage food intake, vomiting and energy expenditure problems in patients with a Noonan syndrome spectrum disorder.
Diagnostic utility of ERG immunostaining in dermatofibroma
Authors: Yamada Yuichi , Ichiki Toshio , Susuki Yosuke , Yamada-Nozaki Yui , Tateishi Yuki , Furue Masutaka , Oda Yoshinao
Published: March 2022

Aims

Dermatofibroma/fibrous histiocytoma (DF/FH) is a common cutaneous mesenchymal neoplasm exhibiting benign biological behaviour. However, the immunohistochemical utility of erythroblast transformation-specific-related gene (ERG) for diagnosing DF remains unknown. The authors reviewed the immunohistochemical status of ERG in different subtypes of DF and in its differential diagnoses.

Methods

Overall, 97 cases of ordinary DF/FH, 6 cases of aneurysmal FH, 10 cases of cellular FH, 5 cases of angiomatoid FH, 2 cases of epithelioid FH, 64 cases of dermatofibrosarcoma protuberans (DFSP) and 52 cases of fibrous scar were retrieved. As the other histological types of cutaneous neoplasms, 6 cases of myxofibrosarcoma, 4 cases of undifferentiated pleomorphic sarcoma, 11 cases of atypical fibroxanthoma, 19 cases of malignant melanoma, 20 cases of nevocellular nevus, 20 cases of neurofibroma, 19 cases of schwannoma, 8 cases of angioleiomyoma and 1 case of pilar leiomyoma were included.

Results

Immunohistochemical positivity for ERG was demonstrated in 87 of 97 cases (89.6%) of ordinary DF/FH, 7 of 10 cases (70%) of cellular FH, 3 of 6 cases (50%) of aneurysmal FH, 1 of 5 cases (20%) of angiomatoid FH and 1 of 52 cases (0.1%) of fibrous scar. All cases of DFSP, epithelioid FH and other types of cutaneous neoplasms included in the current investigation were negative for ERG. The intensity of ERG immunohistochemical staining in spindle-shaped cells appeared weaker than that in endothelial cells.

Conclusions

DF/FH was frequently positive for ERG immunostaining. ERG immunostaining may thus be useful to distinguish DF/FH from DFSP.
Where the ethical action also is: a response to Hardman and Hutchinson
Authors: Emmerich Nathan
Published: March 2022

InWhere the ethical action is, Hardman and Hutchinson make some interesting and compelling points about the way in which ‘the ethical’—various values and various kinds of values—are embedded in everyday life, including the everyday life one finds in clinical interactions, understood as scientific or scientifically informed activities. However, even when one considers ‘the ethical’ from within the horizon of understanding adopted in their essay, they neglect several important features of healthcare and medical education. In this rejoinder, I argue that a fuller understanding would go some way to indicating the complexity of ethics and ‘ethical action’ in the clinic, as well as the nature of and need for ‘expert’ analysis and philosophical reflection on the ethical questions that modern healthcare continues to engender.
Electronarcosis protocols in broiler chickens for the halal market
Authors: Almeida Gustavo Henrique Joazeiro de , Komiyama Claudia Marie , Castilho Vivian Aparecida Rios de , Garcia Rodrigo Garófallo , Burbarelli Maria Fernanda de Castro , Souza Jacqueline Rosa , Porifio Erique Ferreira , Eberhart Bruna de Souza
Published: March 2022

This study considered the parameters of the halal market to evaluate different protocols for broiler electronarcosis regarding time to return of consciousness, bleeding efficiency, and carcass and meat quality at a broiler slaughterhouse. One hundred and eighty-four 43-day-old male broilers of the Cobb® strain were used. The experimental design used was completely randomized with 46 broilers for each protocol. The protocols used 80 V and 600 Hz, 50 V and 1000 Hz, 60 V and 1000 Hz, and 70 V and 1000 Hz. Time to return of consciousness, bleeding efficiency, bruising, presence of broken bones in pectoral muscle, and meat quality were evaluated. Birds subjected to stunning by the 50 V and 1000 Hz protocol showed the shortest time to return of consciousness after electronarcosis, and high incidence of breast hemorrhage. Birds subjected to the 60 V and 1000 Hz protocol had a higher percentage of broken bones in the pectoralis minor. The 50 V and 1000 Hz electronarcosis protocol fits better to the halal market due to the shorter time to return of consciousness and improved bleeding efficiency. This study suggests new proposals for stunning protocols to serve the halal market.
Dark fluxes from accreting black holes through several mechanisms
Authors: Cai Rong-Gen , Sun Sichun , Zhang Bing , Zhang Yun-Long
Published: March 2022

We discuss the possibility that accreting black hole systems can be sources for dark matter flux through several different mechanisms. We firstly discuss two types of systems‘: coronal thermal plasmas around supermassive black holes in active galactic nuclei (AGNs), and accretion disks of stellar-mass X-ray black hole binaries (BHBs). We explore how these black hole systems may produce keV light dark matter fluxes and find that the dark fluxes from those sources might be too weak to account for the current XENON1T excess. On the other hand, black holes can be good accelerators to accrete and boost heavy dark matter particles. If considering collisions or dark electromagnetism, those particles can then escape and reach the benchmark speed of 0.1c at the detector. We also extend the black hole mass region to primordial black holes (PBHs) and discuss the possibility of contributing to keV light dark flux via superradiance of PBHs.
Microstructure and corrosion performance of Zr–1Nb alloy laser welded joints treated by micro-arc oxidation
Authors: Liu Ruilin , Zhou Qiang , Lei Zheng , Chen Hui , Zhu Zongtao , Li Yuanxing
Published: March 2022

The protective coatings on laser-welded joints of Zr–1Nb alloy were prepared by the micro-arc oxidation (MAO) process. The morphology, microstructure and phase composition of MAO coatings at different regions of Zr–1Nb alloy laser-welded joints were investigated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were used to evaluate the corrosion behaviors of the coatings. The MAO coatings at the fusion zone (FZ) and heat-affected zone (HAZ) were smooth with some small holes, while MAO coatings at base metal (BM) were relatively rough with large holes. The cross-sectional topography at FZ coatings was quite dense with less distribution of pores and cracks, which can effectively improve the protective properties of the coatings. HAZ coatings were divided into outer loose layer and inner dense layer. The BM coating layer possessing many holes was a typical sparse layer structure. The phase compositions of welded joints treated by MAO were diverse at different zones of the joints, but all of them consisted of the monoclinic ZrO2 and tetragonal ZrO2 phases. The coatings of FZ had the best corrosion resistance, compared with HAZ and BM. The results of EIS indicated that the thickness and defects of the inner compact layer played a key role in improving the corrosion resistance of the MAO coatings on the surface of the Zr–1Nb alloy laser-welded joints.
Effect of heat treatment on corrosion resistance of supersonic atmospheric plasma-sprayed Cr3C2–NiCr coating
Authors: Chong Kai , Liang Xiubing , Zhang Zhibin , Zou Yong
Published: March 2022

Supersonic atmospheric plasma spraying (SAPS) is a newly developed technique in preparing high-performance Cr3C2–NiCr coatings with a high deposition efficiency. In this work, the effect of post-heat treatment on carbide precipitation and corrosion property of the SAPS Cr3C2–NiCr coatings was investigated. The results suggested that heat treatment induced the precipitation of secondary carbide grains, and the size of these carbides increased with the increasing heat treatment temperature. In addition, the self-corrosion current density of the coatings thermally treated at 500∘C was seven times smaller than that of the coating without heat treatment. The corrosion morphology showed that significant corrosion cracks were present on the as-sprayed coating. In contrast, the heat-treated coatings demonstrated small corrosion holes due to the formation of small corrosion galvanic cells between the precipitation of carbides and the substrate.
Effects of cyclic closed-die forging on the microstructural evolution and mechanical properties of SiC/AZ91D nanocomposites
Authors: Zhang Li , Ren Ruxin , Ren Jie , Liu Yanlian , Liao Wenjun , Li Taotao , Liu Chengzhi , Wang Qudong
Published: March 2022

A two-step cyclic closed-die forging (CCDF), with the first step to enhance the formability and the second step to refine the matrix grain, was implemented on a solution-treated SiC/AZ91D nanocomposites. Results show that the initial coarse grain is refined from ∼105 μm to ∼27 μm after the implementation of CCDF at 400∘C, and further refined to ∼6.8 μm after another 6-pass of CCDF at 300∘C. Mg17Al12 precipitates are gradually crushed with the progress of CCDF and eventually evolve into small particles uniformly distributed within the matrix. SiC clusters are effectively dispersed into smaller clusters or single particle. Yield stress (YS) and ultimate tensile strength (UTS) of the SiC/AZ91D nanocomposites increase after 1 pass of CCDF at 300∘C, while the elongation to fracture decreases. With the progress of CCDF, YS, UTS and elongation to fracture exhibit a continual increase, reaching their maximum values of 196 MPa, 371 MPa and 9.2% after 6 passes of CCDF.
Improvement of laser-induced damage thresholds of aluminum by using a unique electropolishing method
Authors: Yao Caizhen , Yuan Yuan , Jiang Yilan , Fang Zhenhua , Niu Longfei , Gan Yang
Published: March 2022

Surfaces of polycrystalline Al samples were treated by using electropolishing, etching or mechanical polishing methods. Surface morphologies, laser-induced damage thresholds (LIDTs) and ejected particle numbers of different samples were analyzed and compared. Results showed that several different surface morphologies of Al were prepared. A new surface electropolishing technique was developed and an improvement of 95% of LIDT of Al was obtained by using this method. Ejected particle numbers of different samples were monitored and compared to investigate their damage-resistant properties. The particle numbers ejected from the electropolished sample are much smaller than those of etched or mechanically polished samples under Q-switched 355-nm Nd:YAG laser irradiation at 6.8 ns pulses.
A novel modified Modica–Mortola equation with a phase-dependent interfacial function
Authors: Wang Jian , Kim Junseok
Published: March 2022

In this study, we propose a novel modified Modica–Mortola (MM) equation with a phase-dependent interfacial function. The classical MM functional has a multiple-well potential and the derived classical MM equation allows multiple local minima. The MM equation has a good property compared to its counterpart vector-valued Allen–Cahn (AC) system because the MM equation consists of a single equation. In the MM equation, one phase-field can represent multiple states. However, with a constant interfacial parameter, the interfacial transition layer is getting wider as the jump across two adjacent phases is higher. To overcome this unwanted phenomenon, we propose a phase-dependent interfacial function which has smaller value if gradient of phase-field is larger. We present several computational experiments to demonstrate the superior performance of the proposed modified MM equation over the conventional MM equation.
Enhancing Reusability: An Integrated Framework for Software Requirements Classification and Prioritization
Authors: Ali Tariq , Ur Rehman Saif , Nawaz Asif , Ahmed Munir
Published: March 2022

Requirements Prioritization (RP) is the process of planning, identifying, rating and organizing requirements to avoid breach of trust, contract or agreement. The need of RP is inevitable if the concerns, cost and budget estimation of the stakeholders are considered as primary parameters in a software project. In order to ensure the reusability of existing modules, the new requirements must be classified and coordinated with the current ones in the repository, which guarantees the monetary use of assets. In this research, we have suggested a new framework for software requirements classification and prioritization. This framework is called the Software Requirements Classification and Prioritization Framework (SCPF). It will help the software project managers in managing the requirements at the initial stage of software development. We have identified the key factors and developed a taxonomy in order to classify these factors. This study likewise devised a new RP algorithm, which is based on the normalized aggregation values of the identified factors, and also contributes for the requirements classification by using [Formula: see text]-Nearest-Neighbor (KNN) classifier based on the well-known Normalized Compression Distance (NCD) similarity measure. In addition to this, we have implemented the SCPF by developing a Web-based application which provides the computation features for requirements classification and prioritization. Furthermore, we have conducted a case study and an industry survey to evaluate the results of the SCPF. A comparative study is also presented to show the effectiveness of SCPF with respect to three different state-of-the-art RP approaches. The computed SCPF results are very promising and showed significant improvement in the quality of requirements classification and prioritization.
Investigation the elastic scattering of isobar nuclei 6Li and 6He by 12C using different nuclear potentials
Authors: Amar A. , El Mhlawy Ashraf M. , Amer A. H. , El Sayed A. R.
Published: March 2022

The available experimental data for the elastic scattering of isobar nuclei 6Li and 6He on [Formula: see text]C nuclei have been reanalyzed in a wide energy range of energy from 5.8[Formula: see text]MeV to 600[Formula: see text]MeV using the optical model with Distorted Wave Born Approximation (DWBA), and coupled reaction channel method (CRC) where inelastic scattering and transfer have been involved during calculations. A semi-microscopic potential, namely double-folding cluster model (DFC) has been used to investigate the cluster structure for both projectiles and target which was folded with Distorted Wave Born Approximation (DWBA) as a real part of potential where the imaginary part was taken in Woods–Saxon (WS) shape. The crystal model method has been applied to 6Li, 6He elastically scattered by [Formula: see text]C, and reliable analysis of the considered experimental data has been obtained.
Tail-Weighted Wind Speed Distribution by Mixture Model with Constrained Maximum Likelihood
Authors: Huang Guoqing , Xia Lili , Liu Min , Wu Teng , Wang Dahai , Zheng Haitao
Published: March 2022

In order to reduce the estimation uncertainty for the wind energy output, the accurate assessment of the wind speed distribution plays an important role. Due to the influence of the complex terrain, the wind speed data in mountainous area may show the heavy tail. The tail has an important effect on the annual energy production of the wind turbine because of the power output being the cubic of the wind speed. Hence the heavy tail should be highlighted before choosing the proper distributions. To characterize the heavy tail, the box plot, QQ plot and conditional mean exceedance are used. In addition, the wind climate in the mountainous area is very complicated and typically mixed by local storms and large-scale wind flows. The distribution of the wind speed is prone to exhibit bimodal or even multi-modal nature. The conventional unimodal distributions such as Weibull may not well portray these distributions. The mixture distribution has more flexibilities to handle this challenge. Both generalized extreme value distribution and generalized Pareto distribution are considered to formulate the mixture distribution in modeling the tail. The maximum likelihood method and the constrained maximum likelihood method are used to estimate distribution parameters. The comprehensive evaluation criteria are adopted to determine the most appropriate distribution of four sets of wind speed in the mountainous area of Yunnan, China. Graphical tools, such as histogram density curve and QQ plot, are also used for evaluation. Results show that heavy tail distribution should be considered for the wind data in mountainous areas and the proposed mixture model with the constrained maximum likelihood method for the parameter estimation will be superior for the wind energy potential estimation.
The Effect of Intermediate Ring Support on the Vibration Behavior of Functionally Graded Conical Shells
Authors: Zarei M. , Rahimi G. H.
Published: March 2022

In this study, the free vibrational behavior of a thin-walled functionally graded conical shell with intermediate ring support is investigated. Theoretical formulations were established based on the first-order shear deformation theory. The governing equations of motion were solved using the Galerkin method. Applying a set of displacement functions, the equations of motion result in an eigenvalue problem, by solving which, the natural frequencies of vibration are determined. Material properties are assumed to be varied in the thickness direction according to the power-law volume fraction function. It has been attempted to examine the effects of ring support position on the natural frequencies of vibration and to introduce the optimal scenarios of the support placement to achieve a higher frequency. In addition, a 3D FE model was built in the ABAQUS CAE software in order to validate the results of the analytical model. The analytical results were in close agreement with the literature and also the numerical ones. Moreover, the effects of some commonly used end conditions, variations in the shell geometrical parameters, changes in the ring support placement have been investigated on the vibrational behavior.
Analysis of a new stochastic Gompertz diffusion model for untreated human glioblastomas
Authors: Phan Tuan Anh , Wang Shuxun , Tian Jianjun Paul
Published: March 2022

In this paper, we analyze a new Ito stochastic differential equation model for untreated human glioblastomas. The model was the best fit of the average growth and variance of 94 pairs of a data set. We show the existence and uniqueness of solutions in the positive spatial domain. When the model is restricted in the finite domain [Formula: see text], we show that the boundary point 0 is unattainable while the point [Formula: see text] is reflecting attainable. We prove there is a unique ergodic stationary distribution for any non-zero noise intensity, and obtain the explicit probability density function for the stationary distribution. By using Brownian bridge, we give a representation of the probability density function of the first passage time when the diffusion process defined by a solution passes the point [Formula: see text] firstly. We carry out numerical studies to illustrate our analysis. Our mathematical and numerical analysis confirms the soundness of our randomization of the deterministic model in that the stochastic model will set down to the deterministic model when the noise intensity approaches zero. We also give physical interpretation of our stochastic model and analysis.
Shear-free gravitational collapse of dust cloud and dark energy
Authors: Pandey Anjali , Kumar Rajesh , Srivastava Sudhir Kumar
Published: March 2022

The present paper deals with the gravitational collapse of shear-free inhomogeneous spherical star consisting of dust fluid ([Formula: see text]) in the background of dark energy components ([Formula: see text]) with equation of state [Formula: see text] ([Formula: see text] being a nonzero constant parameter describing dark energy component). We discussed the development of apparent horizon to investigate the black hole formation in gravitational collapsing process. The collapsing process is examined first separately for dust cloud and dark energy and then under the combined effect of dust interacting with dark energy. It is obtained that when only dust cloud or dark energy is present the collapse leads to the formation of black hole under certain conditions. When both of them are present, the collapsing star does not form black hole. However, when dark energy is considered as cosmological constant [Formula: see text], the collapse leads to black hole formation.
Keyword Search Based on Unsupervised Pre-Trained Acoustic Models
Authors: Li Xiner , Zhao Jing , Zhang Wei-Qiang , Lv Zhiqiang , Huang Shen
Published: March 2022

Speech keyword search (KWS) is the task of automatically detecting the required keywords in continuous speech. Single-keyword detection can be regarded as the task of speech keyword wake-up. For many practical applications of these small vocabulary speech recognition tasks, it is costly and unnecessary to build a full large vocabulary speech recognition system. For tasks related to speech keyword search, insufficiency in data resources remains the main challenge so far. Speech pre-training has become an effective technique, showing its superiority in a variety of tasks. The key idea is to learn effective representations in settings where a large amount of unlabeled data is available to improve the performance while labeled data of downstream tasks are limited. This research focuses on the combination of unsupervised pre-training and keyword search based on the Keyword-Filler model and introduces unsupervised pre-training into speech keyword search. The research selects pre-trained model architecture Wav2vec2.0 including XLSR. The research results show that training with feature extracted by pre-trained model performs better than the baseline. In the case of low-resource condition, the baseline performance drops significantly, while the performance of the pre-trained tuned model does not decrease but even increases slightly in some intervals. It can be seen that the pre-trained model can be tuned to achieve better performance on very little data. This shows the advantage and application value of keyword search based on unsupervised pre-training.
MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction
Authors: Lee Wu , Shi Yuliang , Sun Hongfeng , Cheng Lin , Zhang Kun , Wang Xinjun , Chen Zhiyong
Published: March 2022

Accurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.
Turnover of Companies in OpenStack: Prevalence and Rationale
Authors: Zhang Yuxia , Liu Hui , Tan Xin , Zhou Minghui , Jin Zhi , Zhu Jiaxin
Published: March 2022

To achieve commercial goals, companies have made substantial contributions to large open-source software (OSS) ecosystems such as OpenStack and have become the main contributors. However, they often withdraw their employees for a variety of reasons, which may affect the sustainability of OSS projects. While the turnover of individual contributors has been extensively investigated, there is a lack of knowledge about the nature of companies’ withdrawal. To this end, we conduct a mixed-methods empirical study on OpenStack to reveal how common company withdrawals were, to what degree withdrawn companies made contributions, and what the rationale behind withdrawals was. By analyzing the commit data of 18 versions of OpenStack, we find that the number of companies that have left is increasing and even surpasses the number of companies that have joined in later versions. Approximately 12% of the companies in each version have exited by the next version. Compared to the sustaining companies that joined in the same version, the withdrawn companies tend to have a weaker contribution intensity but contribute to a similar scope of repositories in OpenStack. Through conducting a developer survey, we find four aspects of reasons for companies’ withdrawal from OpenStack: company, community, developer, and project. The most common reasons lie in the company aspect, i.e., the company either achieved its goals or failed to do so. By fitting the survival analysis model, we find that commercial goals are associated with the probability of the company’s withdrawal, and that a company’s contribution intensity and scale are positively correlated with its retention. Maintaining good retention is important but challenging for OSS ecosystems, and our results may shed light on potential approaches to improve company retention and reduce the negative impact of company withdrawal.
Resource-demand Estimation for Edge Tensor Processing Units
Authors: Herzog Benedict , Reif Stefan , Hemp Judith , Hönig Timo , Schröder-Preikschat Wolfgang
Published: March 2022

Machine learning has shown tremendous success in a large variety of applications. The evolution of machine-learning applications from cloud-based systems to mobile and embedded devices has shifted the focus from only quality-related aspects towards the resource demand of machine learning. For embedded systems, dedicated accelerator hardware promises the energy-efficient execution of neural network inferences. Their precise resource demand in terms of execution time and power demand, however, is undocumented. Developers, therefore, face the challenge to fine-tune their neural networks such that their resource demand matches the available budgets.

This article presents
Precious
, a comprehensive approach to estimate the resource demand of an embedded neural network accelerator. We generate randomised neural networks, analyse them statically, execute them on an embedded accelerator while measuring their actual power draw and execution time, and train estimators that map the statically analysed neural network properties to the measured resource demand. In addition, this article provides an in-depth analysis of the neural networks’ resource demands and the responsible network properties. We demonstrate that the estimation error of
Precious
can be below 1.5% for both power draw and execution time. Furthermore, we discuss what estimator accuracy is practically achievable and how much effort is required to achieve sufficient accuracy.
Optimus: An Operator Fusion Framework for Deep Neural Networks
Authors: Cai Xuyi , Wang Ying , Zhang Lei
Published: March 2022

The reduction of neural parameters and operations for the applications on embedded and IoT platforms in current deep neural network (DNN) architectures has received increasing attention. Relatively, the intermediate feature maps of such lightweight neural networks begin to grow and usually outsize the on-chip memory as the new bottleneck, which introduces considerable power-consuming off-chip memory accesses. To reduce the feature-induced memory accesses, operator fusion has been proposed to parallelize the execution of multiple convolutional layers and shown significant reduction of off-chip memory accesses. However, how to fuse the neural operators is still a challenging issue that heavily depends on both the neural network (NN) topology and the specific DNN accelerator configuration. In this work, we observed prior operator fusion approaches fail to guarantee memory-level optimality as they search in the constrained operator fusion design space. Considering the complexity of the NN topologies and the constrained resources of the DNN accelerators, we develop a novel operator fusion framework, Optimus. Optimus includes an accurate memory cost model dedicated to the scheduler to evaluate the potential operator-fusion schemes and a directed acyclic graph-based operator fusion algorithm for both off-line and on-line workload deployment scenarios, which altogether generates high-efficiency operator-fusion solutions for arbitrary network models running on DNN accelerators. The experimental results show that Optimus reduces 17–75% off-chip memory accesses and obtains 1.86×–3.66× energy efficiency on state-of-the-art DNN workloads when compared to the baselines and brings significant power-efficiency boost to the DNN accelerators of different architectures and dataflows.
Bitcoin and Beyond
Authors: John Kose , O'Hara Maureen , Saleh Fahad
Published: March 2022

We survey extant literature on the economics of blockchain fundamentals, with particular focus on Bitcoin, proof-of-work, and proof-of-stake. We formally clarify Bitcoin's economic significance in solving the double-spending problem without a centralized entity. We then transition to the economics literature, highlighting the key endogenous economic interactions among participants in the Bitcoin ecosystem as well as the economics of proof-of-stake and other potential consensus algorithms. Along the way, we discuss various literature that provides important insights regarding fees, forks, and price volatility. We conclude by reflecting on the next generation of blockchain innovations.
Crystalline Cholesterol: The Material and Its Assembly Lines
Authors: Varsano Neta , Capua-Shenkar Jenny , Leiserowitz Leslie , Addadi Lia
Published: March 2022

Cholesterol is an essential component of animal cell membranes because it influences and controls cell membrane fluidity. Cholesterol is also responsible for the most frequent lethal pathologies in developed countries because of its intimate association with atherosclerotic plaques, the rupture of which may cause heart attacks or strokes. The question is under which conditions cholesterol activity manifests itself, whether in physiology or in pathology. The answer is complex, and there is probably not one certain answer. This review article has its foundations in abundant published knowledge and evidence, but it cannot possibly be comprehensive, because the extent of cholesterol's involvement in chemistry, biology, biophysics, and medicine is so vast that we cannot embrace it all. We review cholesterol as a molecule and in its various crystalline polymorphs. We then examine cholesterol assembly pathways and, finally, cholesterol in biology and in pathology. We propose that cholesterol activity depends on its assembly states in cholesterol crystals or with other lipids in the form of more-or-less organized crystalline domains. In other words, we analyze cholesterol material properties because the assembly state of the cholesterol molecules profoundly affects the properties of the environment in which they reside.
Neural Algorithms and Circuits for Motor Planning
Authors: Inagaki Hidehiko K. , Chen Susu , Daie Kayvon , Finkelstein Arseny , Fontolan Lorenzo , Romani Sandro , Svoboda Karel
Published: March 2022

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion
Authors: Chen Yirun , Dai Wensheng
Published: March 2022

In recent years, under the background of stable economic operation, people's research on economic systems has become increasingly popular. The dynamic input-output model reflects the change and development process of the input-output relationship of the economic system over a period of time. The main purpose of tracking control is to design a suitable controller so that the output of the control system can track the output of the reference system as much as possible. In the economic system, data is an important factor. Based on this, this paper mainly studies the tracking control of dynamic input-output economic system based on data fusion. This research takes the data fusion of the dynamic input-output economic system as the starting point and takes the optimal control and tracking of the economic system as the research object of this research. Based on data fusion technology, a new dynamic input-output economic system tracking and control is proposed. This paper studies the finite-time optimal tracking control of linear systems. Through numerical examples, comparing the finite-time and infinite-time optimal control simulation results, it is proved that the algorithm can achieve good tracking control. Experimental data shows that the optimal and suboptimal performance indicators for a limited time are 0.7729412 and 1.5687310, respectively. Therefore, compared with the infinite-time optimal control, the performance loss and the final tracking error of the suboptimal control proposed in this study are reduced.
Dynamic Multiprojectile Attack and Killing Effects of Detonation Warheads
Authors: Jiang Ning , Yao Wen-Jin , Li Wen-Bin , Li Wei-Hang , Gao Da-Cheng
Published: March 2022

Dynamic spatial distribution and killing characteristics of warhead fragments are important topics in the field of weapon effectiveness and protection. However, there is little research on the fragment distribution formed by continuous dynamic attacks of multiple projectiles that explode above the ground. This study analyzes spatial distributions of warhead fragments using witness boards in a rectangular target test. The results show that the fragment distribution of multiple projectiles in continuous dynamic attacks demonstrates a spatial superposition characteristic. The superimposed distribution is the sum of the distributions of two independent fragment distributions. The distribution characteristics are consistent with fragment scattering behavior. Therefore, they can be used to analyze the killing effects of multiple projectiles conveniently. The effects of falling speed, falling angle, and explosion height on the damage range of fragments were explored by a fragment spatial distribution model obtained from experiments. Analysis indicates that the prefabricated fragment distribution conformed to a spatial superposition relationship under dynamic multiprojectile continuous attacks, and the superposition obeyed fragment scattering characteristics. As the projectile falling angle increased at the explosion center, or as the falling height decreased, the positive pressure duration increased gradually. The falling speed had the greatest impact on the specific impulse of overpressure. The falling angle had the greatest impact on the peak value of overpressure. Both the falling angle and the explosion height had the greatest impact on the positive pressure acting time.
Action Recognition, Tracking, and Optimization Analysis of Training Process Based on the Support Vector Regression Model
Authors: Zhu Mingjiang
Published: March 2022

In order to study the action recognition, tracking, and optimization of the training process based on the support vector regression model, a method of human action recognition based on support vector machine optimization is proposed. This method uses the improved strategy of support vector machine to realize the action recognition through the human action recognition based on the optimization of the vector machine. During the recognition, the DAG SVM strategy is improved according to the recognition accuracy of the classifier, and when outputting the result, output the recognition result and the corresponding confidence level, and use the confidence level to process the recognition result. Finally, through the experimental results, it is realized that the recognition rate based on support vector optimization is 98.7%, indicating that this method is effective and can improve the accuracy and efficiency of human body action recognition.
GC-MS Analysis, Heavy Metals, Biological, and Toxicological Evaluation of Reseda muricata and Marrubium vulgare Methanol Extracts
Authors: Ullah Riaz , Alqahtani Ali S.
Published: March 2022

The usage of herbal remedy is growing vividly all around the world. Though, ecological contamination particularly with heavy metals carriages thoughtful problem on quality of medicinal plants and their foodstuffs. In the world, 80% of the population depend on traditional medicine, while information on the levels of heavy metal such as Zn, Mn, Cu, Cr, Pb, As, Cd, and Cr in plants utilized for making of herbal remedies is unavailable. Therefore, the purpose of this study was to assess phytochemicals, biological activities, and heavy metal analysis of Reseda muricata and Marrubium vulgare grown in different parts of Saudi Arabia. Qualitative phytochemical analysis of R. muricata and M. vulgare confirmed the presence of alkaloids, flavonoids, tannins, phenol, and saponins. Methanol extracts of both Reseda muricata and Marrubium vulgare were characterized with the help of GC-MS. Antioxidants, antimicrobial, and brine sharp lethal toxicity of the both species were also evaluated.
Some New Fractional Inequalities Involving Convex Functions and Generalized Fractional Integral Operator
Authors: Neamah Majid K. , Ibrahim Alawiah , Mehdy Hala Shaker , Redhwan Saleh S. , Abdo Mohammed S.
Published: March 2022

In this manuscript, we are getting some novel inequalities for convex functions by a new generalized fractional integral operator setting. Our results are established by merging the

k
,
s

-Riemann-Liouville fractional integral operator with the generalized Katugampola fractional integral operator. Certain special instances of our main results are considered. The detailed results extend and generalize some of the present results by applying some special values to the parameters.
Effectiveness and Optimal Location of Real-Time Traffic Conflict Risk Warning System for Rural Unsignalized Intersections: A Driving Simulation Study
Authors: Hu Chengyu , Xiong Changan , Guo Fengxiang , Lee Jaeyoung , Yang Wenchen , Guo Zhongyin
Published: March 2022

The real-time traffic conflict risk warning system (RTCRWS) is proposed as a new proactive crash prevention and control strategy for intersections designed to reduce traffic on the main road to rural unsignalized intersections when a vehicle enters the access roads. This study aims at evaluating the effectiveness of the RTCRWS with different locations based on a driving simulation experiment. In this study, four types of the RTCRWS installation location schemes (i.e., no installation, 50 m/100 m/150 m away from the unsignalized intersection) are designed. Twenty-two experienced drivers participated in the driving simulation experiment, and seven evaluating indicators representing driving behavior data are proposed. Two methods to analyze the data are applied: (1) descriptive analysis of driving behavior characteristics different location schemes of the RTCRWS and (2) entropy weight-fuzzy comprehensive evaluation of the RTCRWS. The results show that the RTCRWS has a significant effect on slowing vehicles when approaching the rural unsignalized intersections. If the location of the RTCRWS is 50 m, 100 m, and 150 m from the intersection, the comprehensive score of fuzzy evaluation is 75.82, 74.91, and 77.22, respectively, which implies that the scheme with the RTCRWS 150 m ahead of the intersection is the most effective.
3D Localization for Mobile Node in Wireless Sensor Network
Authors: Javed Iram , Tang Xianlun , Saleem Muhammad Asim , Sarwar Muhammad Umer , Tariq Maham , Shivachi Casper Shikali
Published: March 2022

Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.
Global Dissipative Solution for an Extended Dullin–Gottwald–Holm Equation
Authors: Wang Yan , Yan Yongsheng , Wu Rui
Published: March 2022

The current paper devotes to the continuation of the solutions for the Dullin–Gottwald–Holm equation with forcing. Due to forcing, this equation loses the conservation of energy and cannot be regarded as the Hamiltonian system. Thus, through employing the characteristic method and exploiting the balance law, we prove the existence of a semigroup of a global dissipative solution, which is defined for each initial value u0 ∈ H1(ℝ) and depends continuity on it.