bluuebunny/crossref_metadata_2025 huggingface dataset:
About this title - The Carboniferous Timescale
Authors: Lucas S. G. , Schneider J. W. , Wang X. , Nikolaeva S.
Published: April 2022

The Carboniferous was the time of the assembly of Pangaea by the collision of the Gondwanan and Larussian supercontinents, and the principal interval of the late Paleozoic ice ages. These tectonic and climatic events caused dramatic sea-level fluctuations and climate changes and produced a Carboniferous world that was diverse topographically and climatologically, perhaps only rivalled in that diversity by the late Cenozoic world. Furthermore, the Carboniferous was a time of the accumulation of vast coal deposits of great economic and societal significance. The temporal ordering of geological and biotic events during Carboniferous time thus is critical to the interpretation of some unique and pivotal events in Earth history. This temporal ordering is based on the Carboniferous timescale, which has been developed and refined for nearly two centuries. This book reviews the history of the development of the Carboniferous chronostratigraphic scale and includes comprehensive analyses of Carboniferous radioisotopic ages, magnetostratigraphy, isotope-based correlations, cyclostratigraphy and timescale-relevant marine and non-marine biostratigraphy and biochronology.
Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach
Authors: Deng Liwei , Sun Hao , Sun Rui , Zhao Yan , Su Han
Published: April 2022

Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly.
Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage
Authors: Ren Bin , Chen Yuquiang , Wang Fujie
Published: April 2022

The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also calledIndustry 4.0, and the platform for processing huge amounts of data has an indispensable role. The massive data processing platform is like the brain of the entire factory, receiving all data from production line sensors via edge computing, processing, and analyzing, and finally making feedback decisions. With the innovation of production technology, the data that the platform needs to process has become diverse and complex, and the amount has become increasingly large. As well, many precision manufacturing industries have begun to enter the field of Industry 4.0. In addition to the accuracy and availability of data processing, there is emphasis on the real-time nature of data processing. After the sensor receives the data, the platform must provide feedback within a short period of time. This article proposes a massive data processing platform based on the Lambda architecture, which has the coexistence of stream processing and batch processing to meet real-time feedback needs of high-precision manufacturing. To verify the effectiveness of the optimization, it is based on real data from the manufacturing industry. To generate a large amount of test data to confirm the optimization of the storage of pictures. The results show that it optimizes the storage and optimization of the image data generated by the Automated Optical Inspection technology used in manufacturing today and optimizes the query for data storage. It also reduces the consumption of a large amount of memory as expected, and the query for Hive reduced the time spent.
A Biocentric Perspective on HCI Design Research Involving Plants
Authors: Fell Jan , Kuo Pei-Yi , Greene Travis , Wang Jyun-Cheng
Published: April 2022

HCI researchers increasingly involve plants in their interaction design research, such as using plants as novel interfaces or sensors. While research involving other living entities, namely humans and animals is regulated, there exists no guidance for plants involved in HCI research. Motivated by biocentric philosophy, the present article makes a first attempt at addressing this gap. Commencing with a review of the development of research ethics, we show that as ethical concern has expanded from humans to animals, there is an argument for its expansion to plants. Through the lens of Respect for Nature, we then review the state-of-the-art in HCI design research involving plants. Our focus lies with ethics in the process of conducting research and its implications for the plants involved. We derive a set of actionable ethical design considerations for researchers involving plants in their interaction design work. Finally, our work aims to stimulate discussion on the involvement of plants in interaction design research.
Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank
Authors: Li Zuchao , Zhao Hai , Zhou Junru , Parnow Kevin , He Shexia
Published: April 2022

The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalism/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this article, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role. Our work provides a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks.
Automatic Qubit Characterization and Gate Optimization with
QubiC
Authors: Xu Yilun , Huang Gang , Balewski Jan , Morvan Alexis , Nowrouzi Kasra , Santiago David I. , Naik Ravi K. , Mitchell Brad , Siddiqi Irfan
Published: April 2022

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using
QubiC
, which is an open source FPGA (field-programmable gate array)-based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the
QubiC
automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10
-4
and 1.4(3) × 10
-2
, respectively.
Metabolic Reprogramming and Longevity in Quiescence
Authors: Dworkin Jonathan , Harwood Caroline S.
Published: April 2022

Since Jacques Monod's foundational work in the 1940s, investigators studying bacterial physiology have largely (but not exclusively) focused on the exponential phase of bacterial cultures, which is characterized by rapid growth and high biosynthesis activity in the presence of excess nutrients. However, this is not the predominant state of bacterial life. In nature, most bacteria experience nutrient limitation most of the time. In fact, investigators even prior to Monod had identified other aspects of bacterial growth, including what is now known as the stationary phase, when nutrients become limiting. This review will discuss how bacteria transition to growth arrest in response to nutrient limitation through changes in transcription, translation, and metabolism. We will then examine how these changes facilitate survival during potentially extended periods of nutrient limitation, with particular attention to the metabolic strategies that underpin bacterial longevity in this state.
Gobernanza Criminal y la Crisis de los Estados Latinoamericanos Contemporáneos
Authors: Feldmann Andreas E. , Luna Juan Pablo
Published: April 2022

Crecientemente las sociedades latinoamericanas enfrentan el surgimiento de nuevos órdenes en que los funcionarios estatales y las autoridades políticas comparten el poder con organizaciones criminales. La gobernanza criminal (es decir, la creación de un orden paralelo basado en la imposición de reglas sobre el comportamiento ciudadano por parte de organizaciones criminales a menudo con la colaboración de agentes estatales) como se denomina a este tipo de configuración, plantea desafíos importantes para la democracia y el estado de derecho. También constriñe y a veces reconfigura el acceso a derechos de ciudadanía civil, política, y social por parte de la población. Este artículo revisa críticamente la bibliografía sobre la interacción entre estado y delincuencia en América Latina abordando cuestiones conceptuales y metodológicas. Al hacerlo, se distinguen tres líneas de trabajo que, en paralelo, han contribuido a mejorar nuestra comprensión de la gobernanza criminal: estudios sobre violencia, trabajos sobre estado y estatalidad, y trabajos que analizan específicamente los esquemas de gobernanza criminal. El artículo postula que, en conjunto, estas tres líneas de investigación han realizado un trabajo encomiable al describir y conceptualizar formas emergentes de gobernanza que se desvían de nociones tradicionales. Sin embargo, también argumenta que dichas líneas avanzaron en paralelo, lo que no solo ha redundado en una pobre integración, sino también, en la reproducción de puntos ciegos a nivel teórico y metodológico. Todo ello debilita lacapacidad analítica y heurística de cada una. El artículo también señala que se requiere más trabajo para entender las consecuencias que los nuevos ordenes de gobernanza criminal generan respecto al funcionamiento de los regímenes políticos e instituciones estatales. La conclusión propone pasos concretos para fortalecer el análisis y fomentar una agenda más integrada, sugiriendo también una agenda de investigación.
Ultrafine Bi Nanoparticles Confined in Hydrothermal Carbon-Modified Carbon Nanotubes for Highly Efficient CO2 Electroreduction to Formate
Authors: Yang Fangfang , Zhang Weifeng , Ma Xiaoyu , Zhang Daijun
Published: April 2022

Electroreduction of carbon dioxide (CO2) into formate is a sustainable and promising approach to solve the greenhouse effect. Herein, ultrafine Bi nanoparticles (Bi NPs) with a diameter of around 3.4 nm on hydrothermal carbons-modified carbon nanotubes were successfully obtained via dip-coating method, and could be used as an effective catalyst for electroreduction of CO2 into formate. As a result, the as-prepared catalyst exhibited an outstanding CO2 reduction performance with a faradaic efficiency of 94.8% and a higher current density of 17.8 mA cm−2. The high selectivity could maintain over a wide potential range of 400 mV, together with satisfactory durability for more than 24 h. Electrochemical results and density functional theory (DFT) calculation showed that the rich intrinsic active sites of Bi could effectively adsorb CO2 molecules and promote the CO2(ads) intermediate to form the

CO

2

·
−

intermediate, and resulting in the higher intrinsic activity of the CO2 reduction. Thus, our work demonstrated a synergistic effect between carbon supports and metal nanoparticles could broaden the activity and selectivity for CO2 electroreduction and offer useful perspectives to design efficient and low-cost electrocatalysts.
Design of a 6 kV Beta-Ga2O3 PN Heterojunction Diode with Etched Double-Layered NiO with a Figure of Merit of 10 GW cm−2
Authors: Zhang Xiaohan , Liao Fei , Huang Xuan , Yao Dan , Du ZiCong , Ma XiaoChuan , Chen YaFang , Liu JinHui
Published: April 2022

This study proposes a NiO/β-Ga2O3 etched heterojunction extension termination design for NiO/β-Ga2O3 PN diode to fulfill the application of 6 kV high-voltage with a considerably outstanding power figure of merit 10 GW cm−2, validated and optimized by sentaurus TCAD software. The results indicated the optimum termination parameters L = 20 μm and DJET = 2.3 × 1013cm−2 for the single junction termination extension structure with 4000 V breakdown voltage, and L1 = L2 = 20 μm, DJET1 = 4 × 1013cm−2 and DJET2 = 2.3 × 1013 cm−2 for the double junction termination extension structure with 6000 V. Moreover, to evaluate the power figure of merit of the optimized devices, the specific resistances were extracted from the forward characterizations with setting appropriate electron and hole mobility from experiment results, which exhibited a power figure of merit of 4.7 GW cm−2 for the single junction termination extension and 10 GW cm−2 for double junction termination extension. Meanwhile, utilizing the Poisson equation calculated the ideal one-dimension electric field at NiO/Ga2O3 interface in Ga2O3 for the optimized devices, the single junction termination extension with 5.3 MV cm−1 at reverse 4000 V, and 7.3 MV cm−1 of the double junction termination extension incredibly approached Ga2O3 critical breakdown electric field of 8 MV cm−1 at reverse 6000 V, demonstrating our designed device structures possess immense potential for high-voltage power application.
Translating the force—mechano-sensing GPCRs
Authors: Wilde Caroline , Mitgau Jakob , Suchý Tomáš , Schöneberg Torsten , Liebscher Ines
Published: April 2022

Incorporating mechanical cues into cellular responses allows us to experience our direct environment. Specialized cells can perceive and discriminate between different physical properties such as level of vibration, temperature, or pressure. Mechanical forces are abundant signals that also shape general cellular responses such as cytoskeletal rearrangement, differentiation, or migration and contribute to tissue development and function. The molecular structures that perceive and transduce mechanical forces are specialized cytoskeletal proteins, cell junction molecules, and membrane proteins such as ion channels and metabotropic receptors. G protein-coupled receptors (GPCRs) have attracted attention as metabotropic force receptors as they are among the most important drug targets. This review summarizes the function of mechano-sensitive GPCRs, specifically, the angiotensin II type 1 receptor and adrenergic, apelin, histamine, parathyroid hormone 1, and orphan receptors, focusing particularly on the advanced knowledge gained from adhesion-type GPCRs. We distinguish between shear stress and cell swelling/stretch as the two major types of mechano-activation of these receptors and contemplate the potential contribution of the force-from-lipid and force-from-tether models that have previously been suggested for ion channels.
Functional characterization of the fronto-parietal reaching and grasping network: reversible deactivation of M1 and areas 2, 5, and 7b in awake behaving monkeys
Authors: Goldring Adam B. , Cooke Dylan F. , Pineda Carlos R. , Recanzone Gregg H. , Krubitzer Leah A.
Published: April 2022

This is the first study to directly compare the results of cooling different portions of the fronto-parietal reaching and grasping network (motor cortex, anterior and posterior parietal cortex) in the same animals and the first to employ a complex, bimanual reaching and grasping task that is ethologically relevant. Whereas cooling area 7b or area 5L evoked deficits at distinct task phases, cooling M1 evoked a general set of deficits and cooling area 2 evoked proprioceptive deficits.
SCN2A-related epilepsy of infancy with migrating focal seizures: report of a variant with apparent gain- and loss-of-function effects
Authors: Yang Xiao-Ru , Ginjupalli Vamsi Krishna Murthy , Theriault Olivier , Poulin Hugo , Appendino Juan Pablo , Au Ping Yee Billie , Chahine Mohamed
Published: April 2022

Voltage-gated sodium channels play an important role in the central nervous system, mutations in which have been reported to be responsible for epilepsy. We report here an infant presenting with epilepsy of infancy with migrating focal seizures (EIMFS) in the neonatal period with a mosaic c.2635G>A, resulting in a Gly879Arg missense mutation on the SCN2A gene encoding NaV1.2 sodium channels. Biophysical characterization of this variant revealed a mixture of gain- and loss-of-function effects.
Hyperglycemia and hyperinsulinemia effects on anterior cingulate cortex myoinositol—relation to brain network functional connectivity in healthy adults
Authors: Bolo Nicolas R. , Jacobson Alan M. , Musen Gail , Simonson Donald C.
Published: April 2022

Hyperinsulinemia-related decreases of brain anterior cingulate cortex (ACC) myoinositol independent of plasma glucose levels and the association of low ACC myoinositol with increased functional connectivity between sensorimotor regions and ACC/insula-related networks suggest involvement of myoinositol in insulin-modulated brain network function in healthy adults. In diabetes, elevated brain myoinositol may be due to reduced brain insulin levels or action, rather than hyperglycemia, and may be involved in brain network dysfunctions leading to cognitive or mood disorders.
A TMS-induced cortical silent period delays the contralateral limb for bimanual symmetrical movements and the reaction time delay is reduced on startle trials
Authors: Teku Faven , Maslovat Dana , Carlsen Anthony N.
Published: April 2022

Bilateral responses are typically planned synchronously and performed symmetrically. Here, we show that delaying the initiation of one limb using transcranial magnetic stimulation (TMS) to produce a cortical silent period does not impact the other limb during bimanual movements. Also, the TMS-induced delay is reduced when a startling acoustic stimulus (SAS) triggers the movement. These results confirm that tightly coupled bilateral responses can be dissociated by contralateral TMS- and SAS-triggered responses involve greater reticulospinal output.
Solving Traveling Salesman Problems Based on Artificial Cooperative Search Algorithm
Authors: Liu Guangjun , Xu Xiaoping , Wang Feng , Tang Yangli
Published: April 2022

The traveling salesman problem is a typical NP hard problem and a typical combinatorial optimization problem. Therefore, an improved artificial cooperative search algorithm is proposed to solve the traveling salesman problem. For the basic artificial collaborative search algorithm, firstly, the sigmoid function is used to construct the scale factor to enhance the global search ability of the algorithm; secondly, in the mutation stage, the DE/rand/1 mutation strategy of differential evolution algorithm is added to carry out secondary mutation to the current population, so as to improve the calculation accuracy of the algorithm and the diversity of the population. Then, in the later stage of the algorithm development, the quasi-reverse learning strategy is introduced to further improve the quality of the solution. Finally, several examples of traveling salesman problem library (TSPLIB) are solved using the improved artificial cooperative search algorithm and compared with the related algorithms. The results show that the proposed algorithm is better than the comparison algorithm in solving the travel salesman problem and has good robustness.
A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma
Authors: Yang Jian , Feng Zhien , Yuan Xiaohong , Han Zhengxue
Published: April 2022

Objective. To explore a novel Immune-associated gene signature for overall survival (OS) in patients with oral squamous cell carcinoma (OSCC). Methods. Expression profiles of genes and corresponding clinical materials of OSCC patients were obtained through the TCGA database. With a LASSO Cox regression model, a multigene signature was established to predict the OS of OSCC patients. Some molecular experiments including RNA interference, MTT, and Transwell assay were applied to verify the role of the risk gene FGF9 in OSCC. Results. 43 immune-related prognostic DEGs were identified in OSCC. A 17-gene signature was established to assign the patients to either a high-risk group (HG) or a low-risk group (LG). The HG presented a shorter OS than the LG (

P
<
0.05

). According to multivariate Cox regression analyses, the risk score was considered an independent factor for OS prediction (training set: HR = 3.485, 95% CI = 2.037–5.961,

P
<
0.001

; test set: HR = 4.531, 95% CI = 2.120–9.682,

P
<
0.001

). ROC curve-based analysis revealed the signature’s ability for prediction. According to functional analysis, the immune cell expression and immune function of the HG were significantly inhibited. After knocking down the high-risk gene FGF9, the migration, proliferation, and invasion capabilities of OSCC cells HSC6 were significantly suppressed (

P
<
0.05

). Conclusion. A novel immune-associated gene signature was identified for predicting the prognosis of OSCC. These risk genes show great potential as targets for OSCC treatment.
Mitochondria-Targeting and ROS-Responsive Nanocarriers via Amphiphilic TPP-PEG-TK-Ce6 for Nanoenabled Photodynamic Therapy
Authors: Wang Yangjian , Zheng Junshui , Lin Jian , Ye Kai , Wei Peng
Published: April 2022

Designing targeted-delivering and stimuli-responsive nanocarriers for photodynamic therapy (PDT) is an appealing method, especially, targeting delivery of photosensitizers to mitochondria as the most sensitive cellular organelles to reactive oxygen species (ROS) could significantly enhance the therapeutic efficacy of PDT. In this study, we synthesized triphenylphosphonium bonded PEG-NH2 (TPP-PEG-NH2) and bridged to chlorin e6 (Ce6) via thioketal (TK) linkage to obtain red light-triggered, amphiphilic copolymer (TPP-PEG-TK-Ce6), which could self-assemble into micelles with an average size of 160 nm and zeta potential of +20.1 mV. The in vitro release behavior of TPP-PEG-TK-Ce6 nanocarriers showed a light-activated way and was dependent on the H2O2 concentration. TPP-PEG-TK-Ce6 nanocarriers exhibited high cytotoxicity against C6 cells with illumination. Confocal laser scanning microscopy observation indicated that TPP-PEG-TK-Ce6 nanocarriers were efficiently internalized into the mitochondrion of C6 cells, released Ce6 via light activated. By contrast, in the case of TPP-PEG-NH2 directly bonded Ce6 (TPP-PEG-Ce6) nanocarriers, little Ce6 was found in the mitochondrion. The stronger fluorescence in the mitochondrion of TPP-PEG-TK-Ce6 nanocarriers originated from the mitochondrial-targeting capability of TPP and the cleavage of TK linkages activated by light irradiation, which greatly improved the cellular uptake of TPP-PEG-TK-Ce6 nanocarriers and released more Ce6 in the mitochondrion. This work provided a facile strategy to improve PDT efficacy.
Chemical Characterization and Metabolic Profiling of the Compounds in the Chinese Herbal Formula Li Chang Decoction by UPLC-QTOF/MS
Authors: Lin Baofu , Guo Shaoju , Hong Xinxin , Jiang Xiaoyan , Li Haiwen , Li Jingwei , Guo Linglong , Li Mianli , Chen Jianping , Huang Bin , Xu Yifei
Published: April 2022

Background. Li Chang decoction (LCD), a Chinese medicine formula, is commonly used to treat ulcerative colitis (UC) in clinics. Purpose. This study aimed to identify the major components in LCD and its prototype and metabolic components in rat biological samples. Methods. The chemical constituents in LCD were identified by establishing a reliable ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF/MS) method. Afterwards, the rats were orally administered with LCD, and the biological samples (plasma, urine, and feces) were collected for further analyzing the effective compounds in the treatment of UC. Result. A total of 104 compounds were discriminated in LCD, including 26 flavonoids, 20 organic acids, 20 saponins, 8 amino acids, 5 oligosaccharides, 5 tannins, 3 lignans, 2 alkaloids, and 15 others (nucleosides, glycosides, esters, etc.). About 50 prototype and 94 metabolic components of LCD were identified in biological samples. In total, 29 prototype components and 22 metabolic types were detected in plasma. About 27 prototypes and 96 metabolites were discriminated in urine, and 34 prototypes and 18 metabolites were identified in feces. Conclusion. The flavonoids, organic acids, and saponins were the major compounds of LCD, and this study promotes the further pharmacokinetic and pharmacological evaluation of LCD.
Chaotic Enhanced Genetic Algorithm for Solving the Nonlinear System of Equations
Authors: Algelany A. M. , El-Shorbagy M. A.
Published: April 2022

Many engineering and scientific models are based on the nonlinear system of equations (NSEs), and their effective solution is critical for development in these domains. NSEs can be modeled as an optimization problem. So, the goal of this paper is to propose an optimization method, to solve the NSEs, which is called a chaotic enhanced genetic algorithm (CEGA). CEGA is a chaotic noise-based genetic algorithm (GA) that improves performance. CEGA will be configured so that it uses a new definition which is chaotic noise to overcome the drawbacks of optimization methods such as lack of diversity of solutions, the imbalance between exploitation and exploration, and slow convergence of the best solution. The goal of chaotic noise is to reduce the number of repeated solutions and iterations to speed up the convergence rate. In the chaotic noise, the chaotic logistic map is utilized since it has been used by numerous researchers and has proven its efficiency in increasing the quality of solutions and providing the best performance. CEGA is tested using many well-known NSEs. The suggested algorithm's results are compared to the original GA to prove the importance of the modifications introduced in CEGA. Promising results were obtained, where CEGA’s average percentage of improvement was about 75.99, indicating that it is quite effective in solving NSEs. Finally, comparing CEGA’s results with previous studies, statistical analysis by Friedman and Wilcoxon’s tests demonstrated its superiority and ability to solve this kind of problem.
Extracting Overtaking Segments by Unsupervised Clustering and Predicting Nonmotorized Vehicle’s Trajectory
Authors: Yin Ailing , Chen Xiaohong , Yue Lishengsa
Published: April 2022

Interpretation of flexible cycling behavior has always been a tough task. It is meaningful to understand the overtaking behavior of cyclists for its threats to safety and its high frequency on shared roads. Advanced unsupervised nonparametric clustering methods are compared to distinguish the overtaking segments from the whole trajectory based on the cycling characteristics of nonmotorized two-wheelers, while the hierarchical Dirichlet process hidden Markov model (HDPHMM) outperforms the mixture model via the Dirichlet process (DP mixture model) and topic model via the hierarchical Dirichlet process (HDP topic model). HDPHMM clusters each record into different states and results in more continuous segments. Based on marked vehicle types, which state of clustering model represents the overtaking condition is deduced. The overtaking segments resulted from HDPHMM show the highest homogeneity in cycling features with actual overtaking behavior. Another practical task is to predict the overtaking trajectory and respond to overtaking behavior in advance. Comparing original trajectory and subdivided trajectory, it is found that training model with grouped data, which have homogeneous features, can improve prediction accuracy. With enough trainable samples, CNN + LSTM hybrid structure can achieve trajectory prediction with a mean absolute error of 3 cm. The segmentation produces trajectory segments with similar characteristics. The model is trained with overtaking trajectory segments. With tens of times less trainable data, the prediction on overtaking trajectory still keeps a mean absolute error of about 5 cm. Subdividing trajectory into segments with homogeneous features can improve the prediction accuracy and reduce the requirement of trainable data volume.
Reliable SINS/SAR/GNSS Integrated Navigation System for the Supersonic Vehicle
Authors: Yang Pengxiang , Zhu Qiju , Mei Chunbo , Fan Zhenhui , Lu Baofeng , Jin Honglong
Published: April 2022

In the GNSS-challenged/denied environments, compared with other aided navigation systems, synthetic aperture radar- (SAR-) aided navigation systems can achieve all-weather, all-day, and global positioning. This paper is aimed at designing a reliable navigation system for the supersonic vehicle in the whole flight phase. Considering the SAR is of great significance for the reliable and precise navigation of the supersonic vehicle, we introduce it to the terminal phase to cope with the interference of the global navigation satellite system (GNSS). At the same time, to accelerate the speed of SAR positioning, a positioning method based on the area feature and stack sequential decoding algorithm (SSDA) is designed. Then, applying the SAR positioning under the framework of the Kalman filter (KF), we propose a tightly coupled strapdown inertial navigation system (SINS)/SAR/GNSS integration algorithm, which can achieve positioning without a complicated fusion method between the front phase and terminal phase. The flight experiment results show that the proposed method can correct the average position error to 1.7 m within 1 cycle.
BSD-Guard: A Collaborative Blockchain-Based Approach for Detection and Mitigation of SDN-Targeted DDoS Attacks
Authors: Jiang Shanqing , Yang Lin , Gao Xianming , Zhou Yuyang , Feng Tao , Song Yanbo , Liu Kexian , Cheng Guang
Published: April 2022

Software-Defined Networking (SDN) enhances the flexibility and programmability of networks by separating control plane and data plane. The logically centralized control mechanism makes the control plane vulnerable in both single and multiple controller scenarios. Malicious third parties can exploit vulnerabilities of reactive forwarding mode to launch distributed denial-of-service (DDoS) attacks against SDN controllers. Unfortunately, existing DoS/DDoS solutions under single controller can not afford effective performance under multiple controllers due to the absence of cooperative detection and mitigation. To solve the above problem, we propose a blockchain-based SDN-targeted DDoS defense framework (BSD-Guard) that can provide cooperative detection and mitigation mechanism to protect SDN controllers. BSD-Guard introduces a blockchain-based secure middle plane between control plane and data plane. The secure middle plane calculates the suspect rate of new flows based on the collected packets’ information and reports suspect lists to blockchain for immutably storing and sharing. Besides, the smart contract deployed on blockchain in advance constitutes collaborative defense strategies based on the suspect lists reported from multiple SDN domains. When receiving defense strategies, the secure middle plane converts them to specific flow table actions and installs actions into relevant switches. The experimental results indicate that BSD-Guard can efficiently detect DoS/DDoS attacks in multiple controllers scenario and issue precise defensive strategies near the source of attack by identifying the attack path.
Controlling Shareholders’ Equity Pledge and Listed Companies’ Stock Repurchase
Authors: Wang Zhaolin
Published: April 2022

Based on the unbalanced panel data of China’s A‐share listed companies from 2013 to 2021, this paper empirically examines the influence of controlling shareholders’ equity pledge on the listed companies’ stock repurchase. It is found that listed companies will stabilize the share price through stock repurchase to prevent the risk of control right transfer due to stock price decline. There is a positive correlation between equity pledge and stock repurchase. Moreover, this positive correlation is more significant among nonstate‐owned listed companies and listed companies in the eastern region. Further research shows that the positive influence of equity pledge on stock repurchase is more significant under a good information environment and good corporate governance. However, the earnings management behavior of listed companies has no significant influence on the positive influence of equity pledge on stock repurchase.
Prediction of Prognosis and Recurrence of Bladder Cancer by ECM-Related Genes
Authors: Zhao Hongfan , Chen Zihao , Fang Yunze , Su Mingqiang , Xu Yipeng , Wang Zhifeng , Gyamfi Michael Adu , Zhao Junfeng
Published: April 2022

Background. Bladder cancer (BLCA) is one of the most common cancers and ranks ninth among all cancers. Extracellular matrix (ECM) genes activate a number of pathways that facilitate tumor development. This study is aimed at providing models to predict BLCA survival and recurrence by ECM genes. Methods. Expression data from BLCA samples in GSE32894, GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts were downloaded and analyzed. The ECM-related genes were obtained by differentially expressed gene analysis, stage-associated gene analysis, and random forest variable selection. The ECM was constructed in GSE32894 by the hub ECM-related genes and validated in GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts. The correlations of the ECM score with cells (T cells, fibroblasts, etc.) and the response to immunotherapeutic drugs were investigated. Four machine learning models were selected and used to construct models to predict the recurrence of BLCA. A total of 15 paired BLCA and normal tissue specimens, human immortalized uroepithelial cell lines, and bladder cancer cell lines were selected for the validation of the difference in expression of FSTL1 between normal tissues and BLCA. Results. Six ECM genes (CTHRC1, MMP11, COL10A1, FSTL1, SULF1, and COL5A3) were recognized to be the hub ECM-related genes. The ECM score of each BLCA patient was calculated using these six selected ECM-related genes. BLCA patients with a high ECM score group had significantly lower overall survival rates than patients in the low ECM score group. We found that the ECM score was positively associated with immune cells and fibroblasts and negatively correlated with tumor purity. When treated with immunotherapy, BLCA patients with a high ECM score presented a high response rate and better prognosis. We also found that the combination of FSTL1, stage, age, and gender achieved an AUC value of 0.76 in predicting bladder cancer recurrence. Based on the RT-qPCR results of FSTL1 gene expression, there was an overall decrease in the mRNA expression of FSTL1 in cancer tissues compared to their adjacent normal tissues. Subsequent in vitro validation demonstrated that the FSTL1 expression was downregulated at the gene and protein level compared to that in SVH cells. Conclusion. Taken together, our results indicate that ECM-related genes correlate with immune cells, overall survival, and recurrence of BLCA. This study provides a machine learning model for predicting the survival and recurrence of BLCA patients.
An ALOHA-Based Algorithm Based on Grouping of Tag Prefixes for Industrial Internet of Things
Authors: Zhong Dongbo
Published: April 2022

Nowadays, radio frequency identification (RFID) technology has been widely used in logistics, warehousing, urban transportation, medicine, and other fields due to its advantages of fast identification speed, low cost, and high security. The multitag collision problem causes the reader to fail to complete the identification of the tags in its coverage in time, thus seriously affecting the work efficiency of the entire RFID system. Therefore, the study of an efficient multitag identification algorithm is the basic premise to ensure the industrial application of RFID. Aiming at the problems of the low slot utilization rate of the existing DFSA algorithm in a large-scale tag recognition environment, we proposed a dynamic frame slotting anticollision algorithm based on tags prefix grouping called G-DFSA. G-DFSA uses the tag prefix to recognize the tag set and constructs the probe frame based on the grouping. Then, the slot statistics results of the probe frame are used to process the frames whose frame length does not match the number of tags. As shown in the simulation results, the system efficiency of the proposed algorithm is still close to the theoretical optimal throughput rate of DFSA algorithm 0.368 in the large-scale tag set. Compared with the existing methods, G-DFSA has obvious advantages in system throughput.
Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU
Authors: Cao Bo , Li Chenghai , Song Yafei , Fan Xiaoshi
Published: April 2022

To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.
A Mechanism for Detecting the Intruder in the Network through a Stacking Dilated CNN Model
Authors: Nirmala P. , Manimegalai T. , Arunkumar J. R. , Vimala S. , Rajkumar G. Vinoth , Raju Raja
Published: April 2022

Rapid advancements in the technology and telecommunication areas have led to a massive expansion of network density and information. As a consequence, numerous intruder assaults are being attempted, making it difficult for cybersecurity to identify intruders effectively. The increasing amount of network traffic data has poses a major problem for conventional intrusion detection systems. Moreover, intruders with the intent of launching various assaults inside the networks could not be overlooked. The classification in the article is based on the DL methodologies used in constructing network-based IDS technologies, and it first describes the idea of intrusion detection system. The effectiveness of extracted features and classifications is closely related to detection accuracy, yet typical extraction of features and classification techniques do not function well in this situation. Basic traffic data is also uneven that has a significant effect on classifications findings. A novel intrusion detection model using stacked dilated convolutional autoencoders is proposed and tests it on two additional intrusion detection databases. Many tests have been conducted to define the effectiveness of the strategy. The use of the concept in extensive and practical network systems has a lot of potentiality and possibility. The CTU-UNB database as well as CTU-UNB database is made up of trafficking data from multiple sources. The suggested efficiency of the algorithm is used to evaluate, three types of classification. The deep learning strategy is compared to other ways that were similar. The implications of a number of key hyperparameters are investigated further. The comparison experimental findings show that the suggested approach can reach significantly high efficiency, fulfilling the needs for network intrusion detection systems (NIDS) with higher accuracy.
The Construction of Corporate Financial Management Risk Model Based on XGBoost Algorithm
Authors: Qin Rongyuan
Published: April 2022

Corporate financial management is a tedious task, and it is a complicated thing to rely solely on the human resources of financial personnel to manage. With the continuous development of intelligent algorithms and machine learning algorithms, new ideas have been brought to enterprise financial risk assessment. This method will not only save a lot of financial and material resources but also improve the accuracy of enterprise financial risk assessment. Compared with machine learning algorithms such as random forests and support vector machines, the extreme gradient boosting (XGBoost) algorithm is more widely used, and it has unique advantages in terms of speed and accuracy. This study selects the XGBoost learning algorithm to predict the risk assessment in corporate finance. In this study, the data preprocessing method is used to preprocess and classify the enterprise financial data source effectively, and then the XGBoost algorithm is used to assess the risk of enterprise financial data, and finally a set of enterprise financial risk assessment model is established. The research results show that the XGBoost model selected in this paper has high reliability in predicting the financial risk assessment of enterprises, and the prediction errors are all within 3%. The largest forecast error is only 2.68%, which comes from the profit and loss of the enterprise’s financial situation. The smallest error is only 0.56%, which is a trustworthy enough error for corporate financial forecasting. There is a high correlation between the type of enterprise financial risk assessment and the actual type of risk. At the same time, this paper also has a good dependence on the preprocessing method of enterprise financial data.
Analysis of the miRNA-mRNA Regulatory Network Reveals the Biomarker Genes in the Progression of Myocardial Ischemic Reperfusion
Authors: Li Nini , Chen Qing
Published: April 2022

Objective. Cardiac injury induced by myocardial ischemic reperfusion (MI/R) is still an intractable question in clinical, and it has been confirmed as a major reason for the development of cardiovascular disease. Bioinformatics analysis has been widely used for revealing the pathogenic mechanism of diseases. This study attempted to identify the biomarkers and reveal the regulation mechanism of MI/R injury via bioinformatics analysis. Methods. The GSE67308 and GSE74951 were obtained from the GEO database. The datasets were analyzed with GEO2R tool, and the genes with |logFC| > 2 and

p

value <0.05 were identified as the differentially expressed genes (DEGs). The enrichment analysis of the DEGs was performed with the DAVID database and R language. Moreover, the protein-protein interaction (PPI) network of DEGs was performed with the STRING database and then visualized with Cytoscape. Result. The results showed that 195 downregulated mRNAs and 240 downregulated mRNAs were found in GSE67308, and 11 miRNAs were found in GSE7495. 152 common genes were screened in DEGs of GSE67308 and the targets of 11 miRNAs in GSE7495. Moreover, the enrichment analysis showed that the common genes were related with inflammatory response, immune response, PI3K/AKT, NF-κB, and TNF pathways. Besides, mmu-miR-92a-3p and mmu-miR-27b-3p were identified as the hubs miRNAs, and TNF, IL1B, and IFG1 were screened as the key nodes. Conclusion. This study established a miRNA-mRNA network for cardiac injury induced by MI/R and provided the evidence concerning the molecular mechanism of MI/R injury, which provided some reference for MI/R treatment.
Optimal Feedback Control of Cancer Chemotherapy Using Hamilton–Jacobi–Bellman Equation
Authors: Jeong Yong Dam , Kim Kwang Su , Roh Yunil , Choi Sooyoun , Iwami Shingo , Jung Il Hyo
Published: April 2022

Cancer chemotherapy has been the most common cancer treatment. However, it has side effects that kill both tumor cells and immune cells, which can ravage the patient’s immune system. Chemotherapy should be administered depending on the patient’s immunity as well as the level of cancer cells. Thus, we need to design an efficient treatment protocol. In this work, we study a feedback control problem of tumor‐immune system to design an optimal chemotherapy strategy. For this, we first propose a mathematical model of tumor‐immune interactions and conduct stability analysis of two equilibria. Next, the feedback control is found by solving the Hamilton–Jacobi–Bellman (HJB) equation. Here, we use an upwind finite‐difference method for a numerical approximate solution of the HJB equation. Numerical simulations show that the feedback control can help determine the treatment protocol of chemotherapy for tumor and immune cells depending on the side effects.
Damage Characteristics Caused by Deep-Hole Blasting near Normal Fault and Its Effects on Coal and Gas Outbursts
Authors: Gao Kui , Qiao Guodong , Liu Zegong , Xia Wei
Published: April 2022

To understand the stability of coal and rock in areas of normal faults disturbed by the dynamic loads of blasting, the damage characteristics of coal and rock within a normal fault are investigated using similarity simulation tests. The mechanism for coal and gas outbursts within a normal fault is analyzed theoretically, and the results indicate that the maximum tensile stress in the vertical direction of the blasting hole in the normal-fault model is 1.17 times that in the no-fault model. The propagation of cracks near the blasting hole produces crushing circles, and more cracks are produced in the normal-fault model, causing severe damage to the coal seam and floor rock adjacent to the upper wall of the normal fault. Meanwhile, coal on the surface of the coal seam falls off. The cracks extend to the roof rock through the footwall of the normal fault. Cracks in the adjacent strata and coal seam interpenetrate those around the blasting hole, which is a potentially dangerous area for coal and gas outbursts. The cumulative damage caused by blasting vibrations increases the extent and scope of the damage to coal and rock, and broken coal and rock provide weak surfaces and gas flow channels that can lead to dynamic gas disasters. The research results will provide a theoretical basis for gas dynamic disasters induced by blasting disturbance in normal fault structures. Based on cases of coal and gas outbursts in the Didaoshenghe Coal Mine in Heilongjiang Province, China, an important reason for such incidents is considered to be the blasting areas of normal faults being disturbed by air-powered coal drilling.