bluuebunny/crossref_metadata_2025 huggingface dataset:
Calculus Reform Sparks a Backlash
Authors: Cipra Barry
Published: October 2021

The nationwide movement to reform the teaching of calculus is being criticized by some mathematicians, who argue that new courses water down the subject and coddle students with computers. Their concerns got an airing recently at the winter meetings of the American Mathematical Society and the Mathematical Association of America, held in Orlando, Florida. Yet despite the divisive debate, both sides agree on one point: The reform movement has had a healthy effect on mathematicians' attitudes toward teaching. Computers/Math
Clinton's R&D Achievements Tilt Toward Technology
Authors: Lawler Andrew
Published: October 2021

As President Clinton gears up for his re-election campaign, how has his Administration treated science? An analysis of the White House's own list of accomplishments, outlined in a series of speeches by Vice President Al Gore, finds strong support for high technology—often in the face of fierce Republican opposition—but few radical departures from the policies and programs of its predecessors, and a bipartisan consensus in most areas of academic research.
Science and the Balanced Budget
Authors: Lawler Andrew
Published: October 2021

Vice President Al Gore says the Administration's 7-year balanced budget contains “generous amounts” for science and technology programs in comparison with Republican proposals. But is he promising more than he can deliver?
New Anticoagulant Prompts Bad Blood Between Partners
Authors: Friedly Jock
Published: October 2021

When scientists from Yale University and Corvas International Inc. joined forces in 1993, it seemed like a perfect marriage bound by a strong common interest. The Yale group wanted to develop a vaccine to protect people infected by a bloodsucking parasite, the hookworm; and the company was hoping to isolate a hookworm protein that could compete in the lucrative anticoagulant drug market. The birth announcement this month—a journal report of the amino acid sequences of hookworm anticoagulant proteins—should have been a joyous occasion. It was not. The partners have separated, and the rights to one molecule may become the object of a nasty custody battle. These wrangles seem to be happening more often as academic scientists increasingly work with for-profit corporations, and one observer calls them “special commercialization projects from hell.”
Precise Positioning for All Is Coming
Authors: Kerr Richard A.
Published: October 2021

Last week the White House announced that signals from the U.S. military's satellite-based Global Positioning System (GPS) will be broadcast throughout the world free of charge for the foreseeable future—a boon to commercial applications of GPS. But there was also a bonus for researchers: The military will end the practice of degrading the signal available to civilians within 4 to 10 years. Earth scientists measuring such processes as the drift of continents have already found ways to compensate for the fuzzy signal, but the corrections are expensive.
Europeans Move On From Yeast to TB
Authors: Williams Nigel
Published: October 2021

London—Britain's Wellcome Trust has announced plans to pump $2 million into projects aimed at sequencing the genomes of two of the world's biggest killers:
Mycobacterium tuberculosis
and
Plasmodium falciparum,
the organisms responsible for tuberculosis and malaria. The announcement coincides with the completion of a European project to sequence the genome of the yeast
Saccharomyces cerevisiae.
The entire genome of M. tuberculosis should be completed in a year; the
P. falciparum
project will start with chromosome 3 as a test. Both pose difficult technical challenges because the DNA tends to kink or rearrange during cloning.
Grim Budgets Spur Call to Action
Authors: Lawler Andrew
Published: October 2021

Amid the current fiscal uncertainties, the researchers, university administrators, and policy wonks who converged in Washington last week to discuss federal support for R&D reached consensus on two important points: R&D spending is almost certain to decline in the next few years, and science advocates must make their cause more visible if they hope to protect their programs. In addition, Democrats and Republicans alike agree that projections of what the government might spend on science in 2000 and beyond should be taken with more than a grain of salt.
Staff, Students Protest University Cuts
Authors: Koenig Robert
Published: October 2021

Berlin—Not since the heady days of anti-Vietnam War protests and student strikes had Berliners seen such a massive university demonstration. But this time the 30,000 students who took to the streets on 24 April were not railing against the academic establishment; they were marching in step with militant university presidents, deans, faculty members, and many scientists who joined in protesting deep cuts in Berlin's subsidies for higher education and university research. The planned cuts could hit science programs especially hard.
Report Backs Science, Not New Station
Authors: Mervis Jeffrey
Published: October 2021

The National Science Foundation's (NSF's) dream of building a new research station at the South Pole got a splash of cold water last week when a White House panel praised the foundation's Antarctic research program but stopped short of endorsing construction of the new station. Instead, it asked NSF to assemble outside experts to recommend ways to reduce the $196 million annual cost of the Antarctic program, and suggested that at least part of the new station may have to be funded from such savings.
Does SBIR Increase the R&D Pot?
Authors: Mervis Jeffrey
Published: October 2021

Winning a Small Business Innovation Research (SBIR) award appears to have a negative effect on the size of a company's R&D budget, and no effect on its ability to create jobs or increase sales. This surprising finding, from a new study of how the SBIR program affects small businesses, runs counter to the widely held notion that the awards are a much-needed catalyst for growth in R&D spending.
Varmus Proposes to Scrap the RAC
Authors: Marshall Eliot
Published: October 2021

Harold Varmus, director of the National Institutes of Health (NIH), told
Science
that he will announce this week a plan to “dissolve” NIH's Recombinant DNA Advisory Committee (RAC) and end the practice of subjecting each proposed new clinical gene therapy trial to public review. RAC—which includes lawyers, ethicists, public representatives, and clinicians—has served for 8 years as the final gauntlet that gene therapists must clear before getting approval to start a clinical trial. Instead, Varmus would like to leave detailed safety analyses to others—including the closed-door reviews of the Food and Drug Administration—while focusing NIH's attention on big issues in gene therapy.
New Launcher to Lift Off With Armada of Plasma Probes
Authors: Hellemans Alexander
Published: October 2021

Brussels—Next week, if all goes according to plan, Europe will launch the first of its new Ariane 5 rockets. But this event marks more than just the inauguration of a new launcher; it will also be a milestone in the European Space Agency's (ESA's) science program. Ariane 5's first payload consists of four identical satellites, collectively known as Cluster, which will fly in formation around Earth studying in unprecedented detail its magnetosphere and the solar wind which pounds it around the clock. Cluster and SOHO, a solar observatory ESA launched last December, together form the first large “cornerstone” mission of ESA's ambitious Horizon 2000 space science program.
A Shared European Concern
Authors: Koenig Robert
Published: October 2021

Berlin—The rising tide of infectious diseases around the world, coupled with the spread of HIV, have prompted European public health agencies to combine forces in detecting and handling outbreaks that might quickly spread across national borders. Infectious-disease experts have formed “networks of experts” in special fields such as
Salmonella, Legionella
infections, and HIV/AIDS.
Five-Year Plan to Boost Spending
Authors: Normile Dennis
Published: October 2021

Tokyo—Japanese scientists were looking for a big boost this week, when the government is expected to promise to spend $170 billion on science and technology over the next 5 years. Reaching the target would effectively double by 2000 the $21 billion that the government spent on science in 1992, and it would be a substantial increase over this year's budget of $27 billion. The fiscal goal is the centerpiece of a 5-year plan for science proposed by the Council for Science and Technology that also includes recommendations on how to improve the research environment, boost productivity, and apply more rigorous peer review. The plan fleshes out a new law setting national policy on basic science.
New Populations of Old Add to Poor Nations' Burdens
Authors: Holden Constance
Published: October 2021

As tough as things are for the elderly in the developed world, they are even worse in the developing countries. With a few possible exceptions, such as the Asian “tigers” with their booming economies, these countries are largely facing their growing elderly populations with few sources of help. Pensions are scarce, already inadequate health care systems are facing impossible demands, and families are becoming less and less able to take care of their own old, partly for economic reasons and partly because AIDS is killing off the young to middle-aged adults.
Chemokines Share Center Stage With Drug Therapy
Authors: Cohen Jon
Published: October 2021

Vancouver, British Columbia—Recent advances in drug therapies for AIDS got the bulk of the public attention at the 11th International Conference on AIDS here. But equally interesting to the attendees were the latest findings on the inflammatory proteins called chemokines and their receptors. The reason: recent work showing that a particular chemokine receptor is needed for HIV infection, at least of cells in culture, and that certain chemokines may block that infection. Still, the meeting also revealed an intellectual rift between leading AIDS researchers about whether the chemokine work will ever bring improved HIV treatments or vaccines.
Color Perception: A Correction
Authors: Baldwin J. Mark
Published: October 2021

I hasten to send this note of correction to my paper on ""Distance and Color Perception by Infants"" in
Science
, April 28—an error brought to my attention by a friend. In Tables I. and II. of that article (p. 231) I have taken the proportion of ""acceptances"" to the entire number of cases (the ratio A/N) after adding up the simple numbers for each color at all the distances. It is evident that the resulting percentages are wrong as representing comparative results for the different colors, since there are not an equal number of cases for each same color at different distances, nor for the different colors at each same distance. The proper method is, of course, to compound the percentages representing the relative attractiveness of each color at each distance. This gives the values (for A/N) in Table I. : Blue, .78; red, .75; white, .78; green, .68; brown, .43; and in Table II.: Newspaper, .76; color, .71. This brings white up to the level of blue and red. The same correction should be made for the values R/
n
, but in the result it is immaterial.

I wish to add, also, that I do not consider the results relative to the individual colors of much value, since the cases are so few. The experiments had to be broken off unexpectedly. I published the tables mainly to illustrate the working of the method of experimenting. For this reason I did not enter in my article into side considerations, such as color-brightness, fatigue, etc., which were duly provided for in the experiments themselves. I hope to discuss such points in the fuller treatment of the monograph on the infant's active life which I am preparing.
Mesabi Iron Range
Authors: Jennings E. P.
Published: October 2021

In my paper on the "Mesabi Iron Range," published in
Science
, Feb. 9, I should have given credit to Horace V. Winchell for the rock series, instead of to Prof. N. H. Winchell.
IoT-based Cloud Service for Secured Android Markets using PDG-based Deep Learning Classification
Authors: Ullah Farhan , Naeem Muhammad Rashid , Bajahzar Abdullah S. , Al-Turjman Fadi
Published: October 2021

Software piracy is an act of illegal stealing and distributing commercial software either for revenue or identify theft. Pirated applications on Android app stores are harming developers and their users by clone scammers. The scammers usually generate pirated versions of the same applications and publish them in different open-source app stores. There is no centralized system between these app stores to prevent scammers from publishing pirated applications. As most of the app stores are hosted on cloud storage, therefore a cloud-based interaction system can prevent scammers from publishing pirated applications. In this paper, we proposed IoT-based cloud architecture for clone detection using program dependency analysis. First, the newly submitted APK and possible original files are selected from app stores. The APK Extractor and JDEX decompiler extract APK and DEX files for Java source code analysis. The dependency graphs of Java files are generated to extract a set of weighted features. The
Stacked-Long Short-Term Memory (S-LSTM)
deep learning model is designed to predict possible clones.

Experimental results have shown that the proposed approach can achieve an average accuracy of 95.48% among clones from different application stores.
A Shared Two-way Cybersecurity Model for Enhancing Cloud Service Sharing for Distributed User Applications
Authors: Al-Otaibi Yasser D.
Published: October 2021

Cloud services provide decentralized and pervasive access for resources to reduce the complex infrastructure requirements of the user. In decentralized service access, the implication of security is tedious to match the user requirements. Therefore, cloud services incorporate cybersecurity measures for administering standard resource access to users. In this paper, a
shared two-way security model (STSM)
is proposed to provide adaptable service security for the end-users. In this security model, a cooperative closed access session for information sharing between the cloud and end-user is designed with the help of cybersecurity features. This closed access provides less complex authentication for users and data that is capable of matching the verifications of the cloud services. A deep belief learning algorithm is used to differentiate the cooperative and non-cooperative secure sessions between the users and the cloud to ensure closed access throughout the data sharing time. The output of the belief network decides the actual session time between the user and the cloud, improving the span of the sharing session. Besides, the proposed model reduces false alarm, communication failures, under controlled complexity.
Stable Model Semantics for Guarded Existential Rules and Description Logics: Decidability and Complexity
Authors: Gottlob Georg , Hernich André , Kupke Clemens , Lukasiewicz Thomas
Published: October 2021

This work investigates the decidability and complexity of database query answering under guarded existential rules with nonmonotonic negation according to the classical stable model semantics. In this setting, existential quantification is interpreted via Skolem functions, and the unique name assumption is adopted. As a first result, we show the decidability of answering first-order queries based on such rules by a translation into the satisfiability problem for guarded second-order formulas having the tree-model property. To obtain precise complexity results for unions of conjunctive queries, we transform the original problem in polynomial time into an intermediate problem that is easier to analyze: query answering for guarded disjunctive existential rules with stratified negation. We obtain precise bounds for the general setting and for various restricted settings. We also consider extensions of the original formalism with negative constraints, keys, and the possibility of negated atoms in queries. Finally, we show how the above results can be used to provide decidability and complexity results for a natural adaptation of the stable model semantics to description logics such as 
ELHI
and the
DL-Lite
 family.
DeepMTD: Moving Target Defense for Deep Visual Sensing against Adversarial Examples
Authors: Song Qun , Yan Zhenyu , Tan Rui
Published: October 2021

Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Most existing countermeasures against adversarial examples build their security on the attackers’ ignorance of the defense mechanisms. Thus, they fall short of following Kerckhoffs’s principle and can be subverted once the attackers know the details of the defense. This article applies the strategy of
moving target defense
(MTD) to generate multiple new deep models after system deployment that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples’ minor transferability across different models. The post-deployment of dynamically generated models significantly increase the bar of successful attacks. We also apply serial data fusion with early stopping to reduce the inference time by a factor of up to 5, as well as exploit hardware inference accelerators’ characteristics to strike better tradeoffs between inference time and power consumption. Evaluation based on three datasets including a road sign dataset and two GPU-equipped embedded computing boards shows the effectiveness and efficiency of our approach in counteracting the attack.
Network Embedding via Motifs
Authors: Shao Ping , Yang Yang , Xu Shengyao , Wang Chunping
Published: October 2021

Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification  [
16
,
31
,
42
]. Most existing methods leverage multi-similarities between nodes such as
connectivity,
which considers vertices that are closely connected to be similar and
structural similarity,
which is measured by assessing their relations to neighbors; while these methods only focus on static graphs. In this work, we bridge connectivity and structural similarity in a uniform representation via motifs, and consequently present an algorithm for Learning Embeddings by leveraging Motifs Of Networks (LEMON), which aims to learn embeddings for vertices and various motifs. Moreover, LEMON is inherently capable of dealing with inductive learning tasks for dynamic graphs. To validate the effectiveness and efficiency, we conduct various experiments on two real-world datasets and five public datasets from diverse domains. Through comparison with state-of-the-art baseline models, we find that LEMON achieves significant improvements in downstream tasks. We release our code on Github at https://github.com/larry2020626/LEMON.
Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks
Authors: Li Xingjian , Xiong Haoyi , Chen Zeyu , Huan Jun , Liu Ji , Xu Cheng-Zhong , Dou Dejing
Published: October 2021

Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly improve and accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this article, we propose a novel regularized transfer learning framework

\operatorname{DELTA}

, namely

DE
ep
L
earning
T
ransfer using Feature Map with
A
ttention

. Instead of constraining the weights of neural network,

\operatorname{DELTA}

aims at preserving the outer layer outputs of the source network. Specifically, in addition to minimizing the empirical loss,

\operatorname{DELTA}

aligns the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in a supervised learning manner. We evaluate

\operatorname{DELTA}

with the state-of-the-art algorithms, including

L^2

and

\emph {L}^2\text{-}SP

. The experiment results show that our method outperforms these baselines with higher accuracy for new tasks. Code has been made publicly available.

1
Ket Quantum Programming
Authors: Da Rosa Evandro Chagas Ribeiro , De Santiago Rafael
Published: October 2021

Quantum programming languages (QPL) fill the gap between quantum mechanics and classical programming constructions, simplifying the development of quantum applications. However, most QPL addresses the inherent quantum programming problem, neglecting quantum computer implementation constraints. We present a runtime architecture for classical-quantum execution that mitigates the limitation of interaction between classical and quantum computers originated from the cloud-based model of quantum computation provided by several vendors, which implies a quantum computer processing in batch. In the proposed runtime architecture, we introduce (i) runtime quantum code generation to enable generic quantum programming and dynamic quantum execution; and (ii) the concept of futures to handle dynamic interaction between classical and quantum computers. To support our proposal, we have implemented the Ket Quantum Programming framework that features a Python-embedded classical-quantum programming language named Ket, the C++ quantum programming library Libket, and Ket Bitwise (quantum computing) Simulator. The last one improves over the bitwise representation, making the simulation time not dependent on the number of qubits but the amount of superposition and entanglement of simulation.
A Normalizing Flow-Based Co-Embedding Model for Attributed Networks
Authors: Liang Shangsong , Ouyang Zhuo , Meng Zaiqiao
Published: October 2021

Network embedding is a technique that aims at inferring the low-dimensional representations of nodes in a semantic space. In this article, we study the problem of inferring the low-dimensional representations of both nodes and attributes for attributed networks in the same semantic space such that the affinity between a node and an attribute can be effectively measured. Intuitively, this problem can be addressed by simply utilizing existing variational auto-encoder (VAE) based network embedding algorithms. However, the variational posterior distribution in previous VAE based network embedding algorithms is often assumed and restricted to be a mean-field Gaussian distribution or other simple distribution families, which results in poor inference of the embeddings. To alleviate the above defect, we propose a novel VAE-based co-embedding method for attributed network, F-CAN, where posterior distributions are flexible, complex, and scalable distributions constructed through the normalizing flow. We evaluate our proposed models on a number of network tasks with several benchmark datasets. Experimental results demonstrate that there are clear improvements in the qualities of embeddings generated by our model to the state-of-the-art attributed network embedding methods.
DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical Relation
Authors: Chen Ling , Tang Xing , Chen Weiqi , Qian Yuntao , Li Yansheng , Zhang Yongjun
Published: October 2021

Temporal knowledge graph (TKG) representation learning embeds relations and entities into a continuous low-dimensional vector space by incorporating temporal information. Latest studies mainly aim at learning entity representations by modeling entity interactions from the neighbor structure of the graph. However, the interactions of relations from the neighbor structure of the graph are neglected, which are also of significance for learning informative representations. In addition, there still lacks an effective historical relation encoder to model the multi-range temporal dependencies. In this article, we propose a
d
ual gr
a
ph
c
onvolution network based TKG representation learning method using
h
istorical rel
a
tions (DACHA). Specifically, we first construct the primal graph according to historical relations, as well as the edge graph by regarding historical relations as nodes. Then, we employ the dual graph convolution network to capture the interactions of both entities and historical relations from the neighbor structure of the graph. In addition, the temporal self-attentive historical relation encoder is proposed to explicitly model both local and global temporal dependencies. Extensive experiments on two event based TKG datasets demonstrate that DACHA achieves the state-of-the-art results.
Balance-Subsampled Stable Prediction Across Unknown Test Data
Authors: Kuang Kun , Zhang Hengtao , Wu Runze , Wu Fei , Zhuang Yueting , Zhang Aijun
Published: October 2021

In data mining and machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because of the sample selection bias, which might induce the distribution shift from training data to test data. Such a model-agnostic distribution shift usually leads to prediction instability across unknown test data. This article proposes a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. It isolates the clear effect of each predictor from the confounding variables. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift, improving both the accuracy of parameter estimation and the stability of prediction across unknown test data. Numerical experiments on synthetic and real-world datasets demonstrate that our BSSP algorithm can significantly outperform the baseline methods for stable prediction across unknown test data.
Corpus-level and Concept-based Explanations for Interpretable Document Classification
Authors: Shi Tian , Zhang Xuchao , Wang Ping , Reddy Chandan K.
Published: October 2021

Using attention weights to identify information that is important for models’ decision making is a popular approach to interpret attention-based neural networks. This is commonly realized in practice through the generation of a heat-map for every single document based on attention weights. However, this interpretation method is fragile and it is easy to find contradictory examples. In this article, we propose a corpus-level explanation approach, which aims at capturing causal relationships between keywords and model predictions via learning the importance of keywords for predicted labels across a training corpus based on attention weights. Based on this idea, we further propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model prediction tasks. Our concept-based explanation method is built upon a novel Abstraction-Aggregation Network (AAN), which can automatically cluster important keywords during an end-to-end training process. We apply these methods to the document classification task and show that they are powerful in extracting semantically meaningful keywords and concepts. Our consistency analysis results based on an attention-based Naïve Bayes classifier (NBC) also demonstrate that these keywords and concepts are important for model predictions.
The Removal Method of the Blasting Vibration Signal Trend Item and Noise
Authors: Wang Hailong , Bai Haobo , Zhao Yan , Wang Dongsheng , Wang Xiaojing , Wang Shenghua
Published: October 2021

Aiming at the problem that the blasting vibration signal contains trend items and noise interference, a signal processing method based on the Fourier decomposition method (FDM) is introduced. Firstly, based on the FDM theory, the original blasting vibration signal is decomposed to obtain several modal components; then, the dominant component of the signal is filtered according to the dominant frequency of the modal component and its correlation with the original signal; finally, the dominant component is reconstructed to remove the trend item and noise at the same time; spectrum analysis is carried out to verify the effectiveness of the method. The results show that FDM can effectively disperse low‐frequency trend items, high‐frequency noise, and useful signal information while decomposing signals containing trend terms and noise; compared with the existing common methods, it can more effectively retain the original information, which provides a reference for the trend item and noise removal of similar blasting vibration signals.
HR Management Big Data Mining Based on Computational Intelligence and Deep Learning
Authors: Zhao Genliang , Xue Zhe
Published: October 2021

Decomposing the structure of a large number of existing posts through data mining will greatly improve the effect of enterprise human resource structure optimization. To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. Then an encoder-decoder LSTM is proposed to implement job requirement generation, and a competency-aware attention mechanism and a replication mechanism are proposed to guide the generation process to ensure that the generated job requirement descriptions comprehensively cover the relevant and representative competency and job skill requirements. A competency-aware strategy gradient training algorithm is then proposed to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants compared to state-of-the-art benchmarks.
A Robust Optimization Modeling for Mine Supply Chain Planning under the Big Data
Authors: Liu Wenbo
Published: October 2021

With the rapid development of information technology, large‐scale data is collected and stored, which provides a huge amount of information for decision‐making. This paper focuses on the planning of mine supply chain under the big data. The mine supply chain usually contains three stages, which is mining, processing, and ore product transportation. This paper tackles the difficulty of variable cut‐off grade by establishing a robust optimization model. To solve the robust optimization model, the nonlinear constraints in the model were linearized first. Then, the specific parameter values were determined through the employment of the hypothesis test in statistics, and the robust optimization model was solved finally. The analysis results show that the robust optimization model can be stabilized when the parameters are subject to disturbance. Finally, sensitivity analysis experiments are carried out for several parameters in the model to find out the influence of each parameter on the model. This paper combines mine supply chain planning with big data, which not only improves the production and transportation efficiency of ore products, but also reduces related costs.
Surgical Procedures Used for Correction of Scheuermann’s Kyphosis: A Meta-Analysis
Authors: Li Qingshan
Published: October 2021

Objectives. Scheuermann’s kyphosis can cause severe back pain and cosmetic disorders to patients. Previous studies on surgical procedure selection for correction of Scheuermann’s kyphosis have drawn controversial conclusions. Here, a meta-analysis was performed to figure out a better way between anterior-posterior (AP) combined procedures and posterior-only (PO) procedures. Methods. We searched PubMed database and Ovid database, as well as Cochrane Library (between January 2009 and December 2020, around recent ten years), for studies reporting Scheuermann’s kyphosis correction in an anterior way or a posterior way. Random effects meta-analysis regarding correction degrees and incidence of proximal junctional kyphosis (PJK) was performed. Results. Finally, 13 unique studies including 586 patients (AP: 300; PO: 286) were identified and included for this meta-analysis. Overall, 6 AP cohorts and 10 PO cohorts were pooled regarding the correction degrees of kyphosis in the analysis, respectively. Pooled correction degrees in AP cohorts were 33.31 (95% CI: 27.48–39.15; I2 = 86%,

P
<
0.001

) and in PO cohorts were 31.16 (95% CI: 26.97–35.35; I2 = 81.1%,

P
<
0.001

). Comparison of correction between AP and PO cohorts did not indicate any significant difference. Likewise, postoperative PJK incidence showed no difference. Back pain can be caused by both AP and PO procedures, but which causes less pain remains to be conclusive. The PO approach showed less blood loss and shorter surgical duration as compared to the AP approach. Conclusions. In summary, this meta-analysis shows similar treatment effects between AP and PO procedures in correcting Scheuermann’s kyphosis, suggesting the advantage of PO procedures due to less blood loss and surgical duration. However, the postoperative complications PJK and distal junctional kyphosis (DJK) cannot be well concluded due to the limitation of existing data.
Adaptive Clustering‐Based Marine Radar Sea Clutter Normalization
Authors: Xu Yong , Jia Tao , Cao Dong , Guo Pengyu , Ma Yue , Yan Hongtao
Published: October 2021

Radar sea clutters are echoes reflected from a patch of ocean surface, which may significantly interfere with the signals from targets, and seriously degrade the performance of marine radar remote sensing. Thus, it is vital to eliminate the effects of sea clutter. In this paper, we aim at normalizing sea clutter to a uniform level. Firstly, a detailed analysis about the characteristics and differences of clutter and targets is presented; then, we present a heuristic processing scheme which works by solving the task of sea clutter normalization as a classification problem followed by energy normalization. Multiscale and speed‐up strategies are incorporated into the dynamic clustering algorithm to found a robust real‐time normalization method. Finally, extensive experiments show state‐of‐the‐art results on challenging sea clutter echoes, which demonstrate the feasibility and robustness of the proposed adaptive clustering normalization method.
Stability of Encapsulated Lactobacillus reuteri during Harsh Conditions, Storage Period, and Simulated In Vitro Conditions
Authors: Soltani Lak Abdolazim , Marhamatizadeh Mohammad Hossein , Fattahi Hossein
Published: October 2021

Viability of probiotics in the foods and human bodies is important, because a certain minimum count of bacteria is necessary to impose health promoting effects. In the present work, we encapsulated Lactobacillus reuteri within whey protein isolate (WPI), soy protein isolate (SPI), WPI + inulin (WPI4I), and SPI + inulin (SPI4I) through spray drying method and investigated the efficiency of the microcapsules on the protection of the cells under different conditions (heat, salt, bile salt, penicillin, pH, simulated gastrointestinal condition, and storage). The particle size of the samples was in the range of 195.2–358.1 nm. The sensitivity of unencapsulated bacteria to heat was considerably higher than that to the encapsulated bacteria, so that, at 80°C, no growth (of unencapsulated type) was observed. At 60°C and 40°C, the cell count of free bacteria decreased to 5.81 and 8.04 log CFU/mL, respectively. The bacteria encapsulated within SPI4I showed the highest viability at these temperatures. A comparison between the effects of different pH values showed pH 1.5 more lethal than 2.5 and 7. The effect of NaCl at 4% concentration on decreasing the bacterial count was more notable than 2%. However, the used wall materials in all conditions resulted in higher viability of the cells compared to the free cells. Among different types of wall materials, it was observed that WPI4I imposed the best protective effect. The higher viability of cells within WPI4I wall material was also observed during the storage time. The viability of encapsulated cells decreased from 10.35 to 10.40 log CFU/g in the first week and to 8.93–9.23 log CFU/g in the last week of storage.
Study on Storage Conditions and Suitability Zoning of Shallow Floor Energy in Nanchang City
Authors: Zhang Hua , Hao Shuren , Yang Qing , Wang Zihong , Dong Wujun , Guo Liangliang
Published: October 2021

In the context of China’s carbon summit strategy, as renewable clean energy, the development and utilization of shallow geothermal energy has broad prospects. Based on the analysis of the occurrence conditions of shallow geothermal energy in Nanchang City, the suitability zoning of the ground source heat pump project in Nanchang City is studied by using the analytic hierarchy process. The results show that Nanchang is very suitable for the construction of the ground source heat pump system. Among them, the suitable area of the groundwater ground source heat pump is 252.17 km2, the basically suitable area is 133.64 km2, and the unsuitable area is 124.29 km2. The suitable area of the buried pipe ground source heat pump is 180.49 km2, the basically suitable area is 320.82 km2, and the unsuitable area is 8.79 km2. The evaluation results can provide a basis for the development and utilization of shallow geothermal energy in Nanchang.
A Pythagorean Fuzzy Approach for Sustainable Supplier Selection Using TODIM
Authors: Kaur Prabjot , Dutta Vaishnudebi , Pradhan Bijay Lal , Haldar Subhomoy , Chauhan Supriya
Published: October 2021

Several firms have become increasingly concerned with sustainability in recent decades and are thus implementing environmental and social changes in their businesses and supply networks. This article aims to assess suppliers based on green design, corporate social responsibility, energy consumption, and other sustainability factors that might aid the growth of a company. Characteristics used in this study will help to accomplish economic, environmental, and social responsibility for organizations to reduce global warming and natural resource depletion. We have used the data given in the article of Zolfani et al. by implementing Pythagorean fuzzy TODIM (an acronym in Portuguese for iterative multicriteria decision making) to calculate the rank of suppliers based on the Triple Bottom Line (TBL) sustainability framework. Both TODIM and PF-TODIM are simple to compute, stable, consistent, and accurate, but we have proved by calculations why Pythagorean fuzzy TODIM should be chosen over TODIM in such situations, where decision makers do not have access to a reliable data source. Finally, we performed a sensitivity analysis on both TODIM and PF-TODIM, and the results bolstered the utility of the model.
Surgical Exposure Technique for Volar Locking Plate Fixation of Distal Radius Fractures in Patients with Flexor Carpi Radialis Brevis Muscle Anomaly
Authors: Ninomiya Hiroshi , Watanabe Makito , Kamimura Kazunari
Published: October 2021

The flexor carpi radialis brevis (FCRB) muscle, considered a rare anomaly, is not well known among orthopedic surgeons. The indications for volar locking plates to treat distal radius fractures have recently expanded, and, as a result, encounters with the FCRB are becoming more common. However, few studies have described how to retract an FCRB. Here, we describe seven of 264 patients with FCRB who underwent surgery for distal radius fractures. In one case, the retracted FCRB interfered with the internal fixation. The presented cases demonstrate that the radial retraction of an FCRB with a large muscle belly enables favorable exposure of the distal radius.
Selective-Opening Security for Public-Key Encryption in the Presence of Parameter Subversion
Authors: Kang Burong , Huang Zhengan , Zhang Lei
Published: October 2021

In public-key encryption (PKE), ciphertexts received by a receiver may be possibly correlated and the security of a PKE relies on honestly generated system parameters. Security against selective opening attacks (SOA) for PKE guarantees that even when an attacker has broken into a subset of honestly generated ciphertexts and opened them (i.e., seeing plaintexts and random bits), the unopened ciphertexts remain secure. While security against parameter subversion attacks (PSA) for PKE requires that even when the public system parameters are maliciously generated, a PKE scheme should be secure. In this paper, we initiate the study of PKE secure against both SOA and PSA. To capture SOA and PSA simultaneously, we formulate a new security notion called indistinguishability under selective opening attacks and parameter subversion attacks (IND-SO-PSA). Further, we define the lossy trapdoor function and all-but-many lossy trapdoor function in the presence of PSA (LTF-PSA and ABM-LTF-PSA correspondingly) and propose an instantiation with the efficiently-embeddable group (EG). Applying these new primitives, we construct a PKE scheme that is proven to be IND-SO-PSA secure.
Properties of Total Transformation Graphs for General Sum‐Connectivity Index
Authors: Rani Anam , Imran Muhammad , Razzaque Asima , Ali Usman
Published: October 2021

The study of networks and graphs through structural properties is a massive area of research with developing significance. One of the methods used in studying structural properties is obtaining quantitative measures that encode structural data of the whole network by the real number. A large collection of numerical descriptors and associated graphs have been used to examine the whole structure of networks. In these analyses, degree‐related topological indices have a significant position in theoretical chemistry and nanotechnology. Thus, the computation of degree‐related indices is one of the successful topics of research. The general sum‐connectivity (GSC) index of graph is described as , where presents the degree of the vertex in and ρ is a real number. The total graph is a graph whose vertex set is , and two vertices are linked in if and only if they are either adjacent or incident in . In this article, we study the general sum‐connectivity index of total graphs for different values of ρ by using Jensen’s inequality.
Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System
Authors: Hou Run-Min , Shi Di-Fen , Gao Qiang , Hou Yuan-Long
Published: October 2021

In this study, a fuzzy wavelet neural network with the improved Levenberg–Marquardt algorithm (FWNN‐LM) is proposed to conquer nonlinearity and uncertain disturbance problems in the AC servo system. First of all, use the particle swarm optimization algorithm based on Levenberg–Marquardt (LM) to optimize parameters in the FWNN controller. Second, the potentiality of fuzzy rules (PFR) method is developed to optimize the structure of the FWNN by error reduction ratio (ERR). Furthermore, stability of FWNN‐LM is proved by the Lyapunov method. Finally, simulation and prototype test results show that this method can improve the accuracy and robustness of the system in presence of load disturbances and parameter perturbations.
A Fuzzy Interpolation Cross-Correlation Method for Time-Delay Estimation of Partial Discharge UHF Pulse Signals
Authors: Guo Bowen , Li Songyuan , Tang Qinghua , Li Lin , Li Pengfei
Published: October 2021

Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.
Effective Anomaly Detection Using Deep Learning in IoT Systems
Authors: Aversano Lerina , Bernardi Mario Luca , Cimitile Marta , Pecori Riccardo , Veltri Luca
Published: October 2021

Anomaly detection in network traffic is a hot and ongoing research theme especially when concerning IoT devices, which are quickly spreading throughout various situations of people’s life and, at the same time, prone to be attacked through different weak points. In this paper, we tackle the emerging anomaly detection problem in IoT, by integrating five different datasets of abnormal IoT traffic and evaluating them with a deep learning approach capable of identifying both normal and malicious IoT traffic as well as different types of anomalies. The large integrated dataset is aimed at providing a realistic and still missing benchmark for IoT normal and abnormal traffic, with data coming from different IoT scenarios. Moreover, the deep learning approach has been enriched through a proper hyperparameter optimization phase, a feature reduction phase by using an autoencoder neural network, and a study of the robustness of the best considered deep neural networks in situations affected by Gaussian noise over some of the considered features. The obtained results demonstrate the effectiveness of the created IoT dataset for anomaly detection using deep learning techniques, also in a noisy scenario.
A Generalized Definition of the Fractional Derivative with Applications
Authors: Abu-Shady M. , Kaabar Mohammed K. A.
Published: October 2021

A generalized fractional derivative (GFD) definition is proposed in this work. For a differentiable function expanded by a Taylor series, we show that

D

α

D

β

f

t

=

D

α
+
β

f

t

;
0
<
α
≤
1
;
0
<
β
≤
1

. GFD is applied for some functions to investigate that the GFD coincides with the results from Caputo and Riemann–Liouville fractional derivatives. The solutions of the Riccati fractional differential equation are obtained via the GFD. A comparison with the Bernstein polynomial method

BPM

, enhanced homotopy perturbation method

EHPM

, and conformable derivative

CD

is also discussed. Our results show that the proposed definition gives a much better accuracy than the well-known definition of the conformable derivative. Therefore, GFD has advantages in comparison with other related definitions. This work provides a new path for a simple tool for obtaining analytical solutions of many problems in the context of fractional calculus.