bluuebunny/crossref_metadata_2025 huggingface dataset:
Differences in Petrophysical and Mechanical Properties Between Low- and Middle-Rank Coal Subjected to Liquid Nitrogen Cooling in Coalbed Methane Mining
Authors: Du Menglin , Gao Feng , Cai Chengzheng , Su Shanjie , Wang Zekai
Published: September 2021

Exploring the damage differences between different coal rank coal reservoirs subjected to liquid nitrogen cooling is of great significance to the rational development and efficient utilization of coalbed methane (CBM). For this purpose, the mechanical properties, acoustic emission (AE) characteristics, and energy evolution law of lignite and bituminous coal subjected to cooling treatment were investigated based on Brazilian splitting tests. Then, pore structure changes were analyzed to reveal the differences in microscopic damage between lignite and bituminous coal after a cooling treatment. The results showed that compared with bituminous coal, the pore structure of lignite coal changed more obviously, which manifested as follows: significant increases in porosity, pore diameters, and pore area and a larger transformation from micropores and transition pores to mesopores and macropores. After the cooling treatment, the thermal damage inside lignite and bituminous coal was 0.412 and 0.069, respectively. Thermal damage reduced the cohesive force between mineral particles, leading to the deterioration of the macroscopic physical and mechanical properties. Simultaneously, denser acoustic emission ringing counts and larger accumulated ringing counts were observed after cooling. Moreover, the random distribution of thermal damage enhanced the randomness of the macrocrack propagation direction, resulting in an increase in the crack path tortuosity. With more initial defects inside coal, a more obvious thermal damage degree and wider damage distribution will be induced by cooling treatment, leading to more complicated crack formation paths and a higher fragmentation degree, such as that of lignite coal.
Design and Optimization of Integrated Distributed Energy Systems for Off-Grid Buildings
Authors: Zhang Jian , Cho Heejin , Mago Pedro J.
Published: September 2021

Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies; however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various climate regions. Distributed energy systems consisting of renewable and nonrenewable power generation technologies with energy storage are used to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power (CHP) systems, solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building’s electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building’s thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of distributed energy resources (DERs) and each component’s size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively and widely applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a trade-off between economic and environmental performance.
How soil abiotic factors affect the population fluctuation of Leitneria pugio (Acari: Mesostigmata: Halolaelapidae)
Authors: Ghasemi-Moghadam Sahebeh , Ahadiyat Ali , Fathipour Yaghoub , Saboori Alireza
Published: September 2021

Abiotic factors, such as temperature, humidity, soil elements, along with biotic factors affect terrestrial arthropod communities. Since the application of agricultural fertilizers with the entrance of different elements causes changes in the soil physicochemical conditions, fertilizers may cause fluctuation in the population density of edaphic arthropods. Given this opinion, in this research, the effect of common fertilizers on the population fluctuation and distribution pattern of the predatory mite Leitneria pugio (Karg) was investigated. The experimental treatments were granular urea fertilizer with two doses (200 and 400 kg per ha), poultry manure, zinc sludge (to simulate the accumulation of zinc sulfate fertilizer in the soil), and control in five blocks. Humidity, pH, temperature, NO3– and NO2–, and elements (organic Carbon, K, Na, P, Fe, Cu, Pb, Cr, Cd, Ni, Zn, and As) of soil were measured, and their relationship with the predatory mite population was determined by Pearson correlation coefficient. Iwao's patchiness, Taylor's power law, and Morisita's coefficient of dispersion were used for determining the spatial distribution pattern of L. pugio. The spatial pattern of L. pugio was aggregated, and the highest population density of the predator was observed in December in zinc sludge and control treatments, and in May, in zinc sludge treatment, on the average 60, 35, and 110 per m2, respectively. The relationship between mite's population and Cu and Pb was significantly positive, and L. pugio had a significantly negative relationship with P, NO3– and soil temperature. Nevertheless, the application of different fertilizers did not affect the spatial distribution pattern of the predator.
Biological responses of Tetranychus urticae to five pepper cultivars at two phenological stages of host plants
Authors: Khodayari Samira , Hashemian Shalilvand Maryam
Published: September 2021

The two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) is an important pest of pepper (Capsicum annuum L.). Control of this pest is usually dependent on chemical pesticides. The use of resistant plant cultivars in conjugation with other control tactics generates more effective and long lasting results in management of many pests. In this study we used two-sex life table to evaluate the performance of T. urticae on five pepper cultivars (11983, ES4, Hashemi, Mahali and USL) before and after blooming of host plants. The results showed that the immature time, adult longevity and fecundity differed significantly among cultivars. Before blooming on 11983, the values for the intrinsic rate of increase (r) and the finite rate of increase (λ) were 0.0645 and 1.066 day-1 respectively, which were significantly the lowest; the highest r and λ values was recorded on ES4 which were 0.1347 and 1.144 day-1 respectively. After blooming there was no significant difference in these values among cultivars except for Mahali in which these values were significantly the highest (0.0751 and 1.078 day-1 respectively). Comparing between the phenological stages of host plants (before and after blooming) indicated that after blooming all of the tested pepper cultivars were more resistant to T. urticae. In summary, among the different cultivars tested, the most suitable was ES4 and the least suitable was 11983 before blooming of host plants. After blooming the suitability of host plants changed and decreased significantly.
Interface quality in GaSb/AlSb short period superlattices
Authors: Alam Md Nazmul , Matson Joseph R. , Sohr Patrick , Caldwell Joshua D. , Law Stephanie
Published: September 2021

Heterostructures including the members of the 6.1 Å semiconductor family (AlSb, GaSb, and InAs) are used in infrared optoelectronic devices as well as a variety of other applications. Short-period superlattices of these materials are also of interest for creating composite materials with designer infrared dielectric functions. The conditions needed to create sharp InAs/GaSb and InAs/AlSb interfaces are well known but the AlSb/GaSb interface is much less well-understood. In this article, we test a variety of interventions designed to improve interface sharpness in AlSb/GaSb short-period superlattices. These interventions include the substrate temperature, the III:Sb flux ratio, and the use of a bismuth surfactant. Superlattices are characterized by high-resolution x-ray diffraction and infrared spectroscopy. We find that AlSb/GaSb short-period superlattices have a wide growth window over which sharp interfaces can be obtained.
Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects
Authors: Power Scott , Lengaigne Matthieu , Capotondi Antonietta , Khodri Myriam , Vialard Jérôme , Jebri Beyrem , Guilyardi Eric , McGregor Shayne , Kug Jong-Seong , Newman Matthew , McPhaden Michael J. , Meehl Gerald , Smith Doug , Cole Julia , Emile-Geay Julien , Vimont Daniel , Wittenberg Andrew T. , Collins Mat , Kim Geon-Il , Cai Wenju , Okumura Yuko , Chung Christine , Cobb Kim M. , Delage François , Planton Yann Y. , Levine Aaron , Zhu Feng , Sprintall Janet , Di Lorenzo Emanuele , Zhang Xuebin , Luo Jing-Jia , Lin Xiaopei , Balmaseda Magdalena , Wang Guojian , Henley Benjamin J.
Published: September 2021

A decades-long affair

Decadal climate variability and change affects nearly every aspect of our world, including weather, agriculture, ecosystems, and the economy. Predicting its expression is thus of critical importance on multiple fronts. Power
et al
. review what is known about tropical Pacific decadal climate variability and change, the degree to which it can be simulated and predicted, and how we might improve our understanding of it. More accurate projections will require longer and more detailed instrumental and paleoclimate records, improved climate models, and better data assimilation methods. —HJS
Achieving net-zero greenhouse gas emission plastics by a circular carbon economy
Authors: Meys Raoul , Kätelhön Arne , Bachmann Marvin , Winter Benedikt , Zibunas Christian , Suh Sangwon , Bardow André
Published: September 2021

Reducing net emission

The great majority of plastics in current use are sourced from fossil fuels, with additional fossil fuels combusted to power their manufacture. Substantial research is focused on finding more sustainable building blocks for next-generation polymers. Meys
et al
. report a series of life cycle analyses suggesting that even the current varieties of commercial monomers could potentially be manufactured and polymerized with no net greenhouse gas emissions. The cycle relies on combining recycling of plastic waste with chemical reduction of carbon dioxide captured from incineration or derived from biomass. —JSY
The stress-sensing domain of activated IRE1α forms helical filaments in narrow ER membrane tubes
Authors: Tran Ngoc-Han , Carter Stephen D. , De Mazière Ann , Ashkenazi Avi , Klumperman Judith , Walter Peter , Jensen Grant J.
Published: September 2021

Keeping tabs on ER stress

In the endoplasmic reticulum (ER), oligomerization is a central step in modulating signaling during the unfolded protein response. Tran
et al
. visualized the active, oligomeric form of the ER stress sensor IRE1α residing in an ER subdomain in stressed human cells. This ER region was composed of narrow (diameter about 28 nanometers) anastomosing ER tubes with a high degree of branching and was continuous with surrounding ER structures. Within the lumen of these narrow IRE1α subdomain tubes, regular protein densities were observed, consistent with ordered oligomers of the lumenal stress–sensing domain of IRE1α. This arrangement may suggest a positive feedback mechanism involved in signaling by IRE1α. —SMH
New Chinese vaccine could bolster global arsenal
Authors: Cohen Jon
Published: September 2021

Protein-based shots developed by Clover shown to protect against five variants
The DoC IT: a Professional Development Tool to Support and Articulate Alignment of One’s Course with the Five Dimensions of CUREs
Authors: Olimpo Jeffrey T. , Kern Amie M.
Published: September 2021

Course-based undergraduate research experiences (CUREs) offer a powerful approach to engage students at all academic levels in the process of scientific discovery. In comparison to prescriptive laboratory exercises, CUREs have been shown to promote students’ science process skill development, positive attitudes toward scientific research, and persistence in STEM.
There Is More than Multiple Choice: Crowd-Sourced Assessment Tips for Online, Hybrid, and Face-to-Face Environments
Authors: Drew Jennifer C. , Grandgenett Neal , Dinsdale Elizabeth A. , Vázquez Quiñones Luis E. , Galindo Sebastian , Morgan William R. , Pauley Mark , Rosenwald Anne , Triplett Eric W. , Tapprich William , Kleinschmit Adam J.
Published: September 2021

Developing effective assessments of student learning is a challenging task for faculty and even more difficult for those in emerging disciplines that lack readily available resources and standards.
Complete Genome Sequence of a Novel Thermoacidophilic and Facultatively Anaerobic Crenarchaeon,
Stygiolobus
sp. Strain KN-1
Authors: Nakamura Koichi , Kurosawa Norio , Sakai Hiroyuki D.
Published: September 2021

The complete genome sequence of the thermoacidophilic crenarchaeon
Stygiolobus
sp. strain KN-1 was determined and annotated. The genome was 2,958,410 bp in size, with a GC content of 40.1%. It contained 2,973 coding sequences, a single copy of the 16S-23S rRNA operon, 47 tRNA genes, and 9 CRISPR repeat sequences.
Chromosome-Scale Assembly of the Complete Genome Sequence of
Leishmania
(
Mundinia
) sp. Ghana, Isolate GH5, Strain LV757
Authors: Almutairi Hatim , Urbaniak Michael D. , Bates Michelle D. , Kwakye-Nuako Godwin , Al-Salem Waleed S. , Dillon Rod J. , Bates Paul A. , Gatherer Derek
Published: September 2021

Leishmania
(
Mundinia
) sp. Ghana is a kinetoplastid parasite isolated in 2015 in Ghana. We report the complete genome sequence of
L.
(
M.
) sp. Ghana, sequenced using combined short-read and long-read technologies. This will facilitate greater understanding of this novel pathogen and its relationships within the subgenus
Mundinia
.
Complete Genome Sequence of
Caulobacter
sp. Strain S6, Obtained Using Oxford Nanopore and Illumina Sequencing
Authors: Zhang Cuiyang , Huang Jun , Ouyang Wei , Li Yongmei , Tang Ying , Guo Zhaohui , Lei Ping , Liu Qingshu
Published: September 2021

We report the complete genome sequence of
Caulobacter
sp. strain S6, generated from Oxford Nanopore and Illumina sequencing. The assembled genome comprises a chromosome with a length of 5.5 Mb and a plasmid of 96,014 bp.
Complete Genome Sequence of Acinetobacter pittii OCU_Ac17, Isolated from Human Venous Blood
Authors: Matsumoto Yuki , Sakiyama Arata , Tsubouchi Taishi , Suzuki Masato , Niki Makoto , Niki Mamiko , Kakeya Hiroshi , Kaneko Yukihiro
Published: September 2021

Acinetobacter pittii
isolate OCU_Ac17 was obtained from the venous blood of a patient at a hospital in Japan. We present its complete 4.108-Mbp genome sequence (1 chromosome plus 3 plasmids), analyzed by combining long-read (Flongle) and short-read (MiniSeq) sequencing.
Exploring the locking stage of NFGAILS amyloid fibrillation via transition manifold analysis
Authors: Bittracher Andreas , Moschner Johann , Koksch Beate , Netz Roland , Schütte Christof
Published: September 2021

We demonstrate the application of the transition manifold framework to the late-stage fibrillation process of the NFGAILS peptide, a amyloidogenic fragment of the human islet amyloid polypeptide (hIAPP). This framework formulates machine learning methods for the analysis of multi-scale stochastic systems from short, massively parallel molecular dynamical simulations. We identify key intermediate states and dominant pathways of the process. Furthermore, we identify the optimally timescale-preserving reaction coordinate for the dock-lock process to a fixed pre-formed fibril and show that it exhibits strong correlation with the mean native hydrogen-bond distance. These results pave the way for a comprehensive model reduction and multi-scale analysis of amyloid fibrillation processes.

Graphic Abstract
On the Lyapunov–Perron reducible Markovian Master Equation
Authors: Szczygielski Krzysztof
Published: September 2021

We consider an open quantum system in [Formula: see text] governed by quasiperiodic Hamiltonian with rationally independent frequencies and under the assumption of Lyapunov–Perron reducibility of the associated Schrödinger equation. We construct the Markovian Master Equation and the resulting CP-divisible evolution in the weak coupling limit regime, generalizing our previous results from the periodic case. The analysis is conducted with the application of projection operator techniques and concluded with some results regarding stability of solutions and existence of quasiperiodic global steady state.
A novel memristor-based chaotic system with line equilibria and its complex dynamics
Authors: Yan Dengwei , Ji’e Musha , Wang Lidan , Duan Shukai
Published: September 2021

Memristor, as a nonlinear element, provides many advantages thanks to its superior properties to design different chaotic circuits. Thus, a novel four-dimensional double-scroll chaotic system with line equilibria as well as two unstable equilibria based on the flux-memristor model is proposed in this paper. The effects of initial values and parameters on the dynamic characteristics of the system are studied in detail by means of phase diagrams, Lyapunov exponent spectrums, bifurcation diagrams, two-parameter bifurcation diagrams and basins of attraction. Besides, a series of complex phenomena in the system, such as sustained chaos, bistability, transient chaos and coexisting attractors are observed, proving that the chaotic system has rich dynamic characteristics. Also, spectral entropy (SE) complexity algorithm and [Formula: see text] complexity algorithm based on structure complexity are adopted to analyze the complexity of the system. Additionally, PSPICE circuit simulation and Micro-Controller Unit (MCU) hardware experiment are carried out to verify the correctness of theoretical analysis and numerical simulation. Finally, the pulse chaos synchronization is achieved from the perspective of maximum Lyapunov exponent, and numerical simulations demonstrate the occurrence of the proposed system and practicability of the pulse synchronization control.
A Novel Rail-to-Rail Input Swing Threshold-Inverter Multi-Bit Quantizer Using Interpolation Technique for Sampled-Data Circuits
Authors: Miremadi Amir Hossein , Hashemipour Omid
Published: September 2021

In this paper, a novel and simple multi-bit quantizer based on the threshold inverter quantization (TIQ) approach is presented for use in sampled-data circuits. The key part is a front-end signal conditioning circuit with the aid of the interpolation technique that makes it possible to realize a rail-to-rail input range operation over the conventional threshold inverter-based quantizer circuits while maintaining the benefits of the TIQ structure without employing any analog-intensive circuits such as current sources or amplifiers, thus achieving a digital-compatible implementation. Simulation results in TSMC 90-nm CMOS technology at a power-supply voltage of 1[Formula: see text]V confirm the efficient performance of the proposed circuit.
Modeling virus pandemics in a globally connected world a challenge towards a mathematics for living systems
Authors: Bellomo N. , Brezzi F. , Chaplain M. A. J.
Published: September 2021

This editorial paper presents the papers published in a special issue devoted to the modeling and simulation of mutating virus pandemics in a globally connected world. The presentation is proposed in three parts. First, motivations and objectives are presented according to the idea that mathematical models should go beyond deterministic population dynamics by considering the multiscale, heterogeneous features of the complex system under consideration. Subsequently, the contents of the papers in this issue are presented referring to the aforementioned complexity features. Finally, a critical analysis of the overall contents of the issue is proposed, with the aim of providing a forward look to research perspectives.
The knot invariant Υ using grid homologies
Authors: Földvári Viktória
Published: September 2021

According to the idea of Ozsváth, Stipsicz and Szabó, we define the knot invariant [Formula: see text] without the holomorphic theory, using constructions from grid homology. We develop a homology theory using grid diagrams, and show that [Formula: see text], as introduced this way, is a well-defined knot invariant. We reprove some important propositions using the new techniques, and show that [Formula: see text] provides a lower bound on the unknotting number.
A first look at the bosonic master-field equation of the IIB matrix model
Authors: Klinkhamer F. R.
Published: September 2021

The bosonic large-[Formula: see text] master field of the IIB matrix model can, in principle, give rise to an emergent classical spacetime. The task is then to calculate this master field as a solution of the bosonic master-field equation. We consider a simplified version of the algebraic bosonic master-field equation and take dimensionality [Formula: see text] and matrix size [Formula: see text]. For an explicit realization of the pseudorandom constants entering this simplified algebraic equation, we establish the existence of a solution and find, after diagonalization of one of the two obtained matrices, a band-diagonal structure of the other matrix.
Analysis of cosmological tachyon and fermion model and observation data constraints
Authors: Tsyba Pyotr , Razina Olga , Suikimbayeva Nurgul
Published: September 2021

In this work, we investigate a cosmological model with the tachyon and fermion fields with barotropic equation of state, where pressure [Formula: see text], energy density [Formula: see text] and barotropic index [Formula: see text] are related by the relation [Formula: see text]. We applied the tachyonization method which allows to consider cosmological model with the fermion and the tachyon fields, driven by special potential. In this paper, tachyonization model was defined from the stability analysis and exact solution standard of the tachyon field. Analysis of the solution via statefinder parameters illustrated that our model in fiducial points with deceleration parameter [Formula: see text] and statefinder [Formula: see text] corresponds to the matter-dominated universe (SCDM) but ends its evolution at a point in the future [Formula: see text] which corresponds to the de Sitter expansion. Comparison of the model parameters with the cosmological observation data demonstrates that our proposed cosmological model is stable at barotropic index [Formula: see text].
NUMERICAL SOLUTION OF TRAVELING WAVES IN CHEMICAL KINETICS: TIME-FRACTIONAL FISHERS EQUATIONS
Authors: WANG FUZHANG , KHAN MUHAMMAD NAWAZ , AHMAD IMTIAZ , AHMAD HIJAZ , ABU-ZINADAH HANAA , CHU YU-MING
Published: September 2021

This paper addresses the numerical solution of nonlinear time-fractional Fisher equations via local meshless method combined with explicit difference scheme. This procedure uses radial basis functions to compute space derivatives while Caputo derivative scheme utilizes for time-fractional integration to semi-discretize the model equations. To assess the accuracy, maximum error norm is used. In order to validate the proposed method, some non-rectangular domains are also considered.
THE COMPLEXITY OF SUPPLY CHAIN SYSTEM’S LOGISTICS FINANCIAL MANAGEMENT AND DYNAMIC NONLINEAR SYSTEM
Authors: MIAO YU , JIN XIN , ALSULAMI ABDULAZIZ A. , KONG MENGLEI , XU PANG
Published: September 2021

The aim of this paper is to discuss the accidental events’ impacts on the complicated supply chain systems and reveal the dynamic nonlinear system’s role in enterprises’ logistics financial management. First, the logistics distribution network and nonlinear system are analyzed and introduced, revealing the complexity of enterprises’ supply chain systems. Second, considering the situation of enterprises’ financial management, four major entities, the supplier, the distributor, the retailer, and the manufacturer, are introduced to construct a dynamic nonlinear model for the supply chain system. Finally, the model before and after accidental events are simulated and analyzed using the AnyLogic software. Results demonstrate that in normal states, each node’s inventory in the system changes periodically, while the distributor’s state is the most stable. A notable decrease in market demand has the largest impact on the distributor. Once the market demand is recovered, the decrease in the distributor’s inventory is the most apparent, and the inventories of the supplier, the retailer, and the manufacturer also decrease. The supplier has the highest cost at the beginning of system operation, and the distributor’s cost is the highest after the operation becomes stable. Once an accidental event caused a sharp decrease in market demand, the costs of all major entities in the system show an increasing trend. Once the market demand is recovered, the costs of different node entities will increase instead. As for the profits, the distributor has the highest profit initially, which is significantly influenced by the accidental events. The results provide a reference for revealing the changing laws of complicated supply chain systems and promoting the development of enterprises’ financial management.
SECURITY APPLICATION OF NEURAL NETWORKS UNDER THE INSPECTION OF NONLINEAR DYNAMIC SYSTEMS
Authors: CHEN XIAOBING , ZHU LIEHUANG , ALGHAZZAWI DANIYAL M. , WANG ZHONGRU , GUO QING
Published: September 2021

Based on the improved BP neural network, this paper establishes an adaptive online controlling model and adopts the model to optimize the controlling accuracies in discrete nonlinear dynamic systems and inverted pendulum systems. To avoid the local minimum problem of the BP neural network’s objective function in the training process, this paper proposes a neural network training method based on the quasi-Newton method (BFGS) optimization algorithm. Compared with other control methods, the neural network-based inverted pendulum control method proposed in this paper has higher control accuracy. Through the control simulation of its power system uses a discrete control method and the control of the inverted pendulum model system, this paper verifies the validity and good control has significantly improved the control method.
OPTION PRICING USING STOCHASTIC VOLATILITY MODEL UNDER FOURIER TRANSFORM OF NONLINEAR DIFFERENTIAL EQUATION
Authors: LIU ZHICHAO , WANG YUNCHEN , CHENG YA , SAEED TAREQ , YE YONG
Published: September 2021

The purposes are to solve the option pricing problem in financial derivatives and provide a practical basis for reducing the risk rate of the financial market. First, the classification of fractal theory and option pricing is outlined, the connotation of stochastic volatility is analyzed, and a stochastic volatility model is established. According to the Fourier transform and nonlinear differential equation, the pricing method in the volatility swap process and the pricing method of volatility derivatives are analyzed. Finally, the option pricing parameters are set, and the Origin 75 software is employed to visually analyze the mathematical results. The forward prices under stochastic volatility are compared monthly and weekly, and the resulted values are all close to 235. There is a gap between the forward prices of each month and each quarter, and the difference between the forward prices of each quarter is large, close to 50. The average relative percentage errors (ARPEs) within and out of the sample calculated by the stochastic volatility model are close to 16%, and the error gaps between different monetary intervals are also large. With the increase in strike price, the change rate of options under the random volatility model is faster than that of options under VIX index. When the strike price increases to 0.6, the two options are equal. The fitting trend of the stochastic volatility model for implied volatility at different maturity dates first decreases and then tends to a fixed value. The implied volatility of the 60-day-expiring option has changed significantly, showing a trend of increasing first and then tending to a fixed value (0.8). In contrast, the implied volatility of the 90-day-expiring option and the 120-day-expiring option almost have no change. When [Formula: see text] (fractional operator) takes 1 and [Formula: see text] takes 1.6 and 1.3, the solution to the stochastic volatility model is consistent with that to the classic CGMY model. When [Formula: see text] takes 1, the stochastic volatility model has the same solution as the classic CGMY and BS models. As the fractional order of time increases, the trend of asset price changes becomes smaller.
THE USE OF NONLINEAR DYNAMIC SYSTEM AND DEEP LEARNING IN PRODUCTION CONDITION MONITORING AND PRODUCT QUALITY PREDICTION
Authors: LI JUN , WANG RUOQI , ALHODALY MOHAMMED SH. , HUANG JINGFENG , QI LIN
Published: September 2021

To improve the product quality and production efficiency in industrial production, the industrial production process is explored. First, for the production state in industrial production, the radio frequency identification technology (RFID) is used to monitor the state of the nodes in the production process and collect data. Based on the problem of blind area in industrial production, an adaptive controller design based on uncertain nonlinear dynamic systems is introduced; second, based on this, for the problem that product quality is difficult to guarantee in the process flow, the improved multi-layer feedforward network (back propagation, BP) algorithm is used to predict it. Finally, the control diagnosis algorithm based on multi-order least squares support vector machine (LS-SVM) is used to analyze the abnormal cause of the product. The results show that the use of the RFID can monitor the production process in real time and obtain real-time product quality information data. The proposed adaptive controller based on uncertain nonlinear dynamic system has good applicability in solving the problem of blind area. The improved BP neural network algorithm improves the prediction accuracy in the process of product quality prediction and increases the convergence speed of the algorithm. The control diagnosis algorithm based on multi-order least squares that support vector machine (SVM) can classify and learn the historical data of products, as well as perform multi-order search and analysis on the causes of abnormal quality. It helps relevant personnel to carry out process improvement and control, thereby achieving the purpose of improving product quality. Through this investigation, it is found that the method proposed can monitor the state of industrial production in real time and help relevant personnel to control and predict product quality. It can be applied in actual processes.
CONSTRUCTION OF SWITCH NONLINEAR DYNAMIC SYSTEM USING ADAPTIVE NEURAL NETWORK TECHNOLOGY
Authors: XING TINGYAN , KHADIDOS ADIL OMAR , LI LIN
Published: September 2021

In order to improve the intelligent tracking efficiency of switched nonlinear dynamic system, first, the optimized backpropagation neural network (BPNN) is proposed and the switched nonlinear dynamic system is constructed. Then, the optimized BPNN is extended and the adaptive control is studied. Finally, the extended neural network system related to the error is formed, and then the simulation analysis is carried out. The results show that under different reference signal conditions, the output peak values of the system are 1.7 and 1.5 at 2[Formula: see text]s, respectively, while the response peak value of the reference signal is 1. However, after 4[Formula: see text]s, the peak values of the two signals are very close; under different reference signals, the scaling factor decreases with the increase of time. After 6[Formula: see text]s, the value of scaling factor is 0. Under the condition of reference signal 1, the estimated values of parameters decrease with the increase of time, and tend to 0 after 6[Formula: see text]s. However, under the condition of reference signal 2, the estimated values of parameters in the first 6[Formula: see text]s increase with the increase of time; the peak values of the state variable [Formula: see text] and the adaptive parameters in the system are in a stable state, while the peak values of the system controller and the system tracking error gradually decrease with the increase of time, and finally tend to a stable value. It suggests that the technique proposed in this work can guarantee the signal in switched nonlinear dynamic system to be bounded.
ENVIRONMENTAL LANDSCAPE ART DESIGN USING DYNAMIC NONLINEAR PARAMETERIZATION
Authors: YU CHUANDONG , ALTALBE ALI , DU NAN
Published: September 2021

For the problem of parametric design of anisotropic curves and cambers in the practical application field of environmental landscape design, in this exploration, the modeling of design object is carried out through computer parametric technology. It is based on the information collection and data extraction of design object and the formulation of logical relationship between parametric variable factors related to design. For nonlinear systems, a necessary condition to judge parameters is proposed. This criterion is a natural extension of the PBH criterion, which makes it easy to identify the parameters of the pseudo-linear system in the generalized case, and avoids a lot of complex operations; at the same time, a parametric design method of dynamic environment landscape is proposed systematically. In the first order, second order, high order and generalized nonlinear model, the complete parametric optimization method of environmental landscape design is established. The [Formula: see text] search function in MATLAB optimization toolbox is used to solve the generalized nonlinear system. The performance of the algorithm is tested by the examples of pseudo velocity, pseudo velocity uniformity and parameter optimization of the parametric curve called by the software environment. The results show that system dynamic process to simulate the dynamic environmental changes in landscape design by using the optimized parameter method is faster than that by using the unoptimized method. Meanwhile, the optimization method in this exploration can reduce the amplitude of change when external factors interfere, which shows that the optimized parametric design model has better transient performance; the curve generated by calling the drawing function provided in this exploration is smoother when the number of drawing points is the same.
THREE-DIMENSIONAL ANIMATION NONLINEAR SYSTEM MODAL IDENTIFICATION USING WAVELET TRANSFORM
Authors: YAN PING , ALYOUBI KHALED H. , SHAN CHUNXIAO
Published: September 2021

The single-degree-of-freedom and three-degree-of-freedom viscous damping systems are simulated based on the Morlet wavelet function transformation in an effort to study the wavelet transform and further promote the optimization of nonlinear system modal identification. In the meantime, the modal animation display technology is studied using the Visual Basic (VB) 6.0 software and Open GL three-dimensional graphics library. The research object is a thin plate member with five degrees of freedom. The research results are as follows. In the single-degree-of-freedom viscous damping system, the identification frequency is 11.545[Formula: see text]rad/s, and the damping ratio is 2.87%. The simulation result has a small gap with the set damping ratio, and the identification in the system is accurate and reliable. In the three-degree-of-freedom damping system, the recognition accuracy of the first-order wavelet coefficient model is higher. Besides, the recognition accuracy of the natural frequency in the second-order is better, and the damping ratio error value is 11.08%. In the third-order, the natural frequency and the damping ratio have a large error from the theory; the error values are 24.53% and 32.11%, respectively. In the meantime, VB 6.0 software and Open GL software can effectively identify the actual shape of the research object, showing a good application effect. The above results can provide scientific and effective reference materials for subsequent research on nonlinear system modal identification.
HUMAN MODULATED HEART RATE VARIABILITY SIGNAL IN SPORTS USING MULTIFRACTAL ANALYSIS
Authors: CHU JIE , BASHERI MOHAMMED , WEI JIANSHE
Published: September 2021

The purpose is to apply the multifractal analysis method to the research of human physiology in the process of sports. The mass index spectrum is used for multifractal analysis of heart rate variability signal based on the heart rate variability signal analysis theory and fractal theory. Finally, 10 healthy college students are selected as the experimental subjects. RAC-3003 portable electronic measuring instrument is used to collect heart rate signals in different exercise stages. Finally, the data are analyzed by [Formula: see text] and Lo-[Formula: see text] analysis methods. The results show that the lowest value of ln[Formula: see text] is 3.1 and the highest value is 7.0 in different stages in the morning, and the lowest value is 3.3 and the highest value is 7.5 in different stages in the afternoon. The average value of random signal ln[Formula: see text] gradually increases from 2.6 to 3.7; whether in the morning or in the afternoon, the average Hurst exponent during exercise is lower than that before and after exercise, and the average Hurst exponent after exercise is slightly higher than that before exercise; the long-range correlation index of heart rate variability signal in each exercise stage first increases and then decreases, and the changes of short-range correlation index and long-range correlation index are opposite; the average of fitting intercept of [Formula: see text] curve is lower than that of Lo-[Formula: see text] curve in the first and third stages; the fractal coefficient of the original data in the first and third stages of exercise is significantly higher than that in the second stage, which indicates that the overall fractal degree of heart rate variability signal before and after exercise is higher.
TRAINING METHOD OF SPORTS ATHLETES USING THE NONLINEAR SYSTEM OF MOVING HUMAN BODY COMPETITIVE ABILITY
Authors: HE CHAOHU , YE LIAOKUN , SULAIMANI HANI JAMAL , HU WENWU
Published: September 2021

The purpose is to optimize the training methods of athletes and improve their competitive ability. First, the corresponding research model is established by analyzing the influencing factors of the athlete training system and combining with the dynamic nonlinear theory. Next, based on the deterministic learning theory, the dynamic nonlinear state identification model of athletes is established to obtain the state information of athletes in the process of competitive sports training and competition. Then, based on the established nonlinear state model, the nonlinear staged athlete training mode is designed. The experimental results show that the designed gait recognition algorithm based on deterministic learning can effectively collect the nonlinear signals generated by the movement process; the accuracy of gait recognition is about 90%, and it is less affected by the training and test datasets. The nonlinear staged training mode can effectively improve the overall index of maximum strength and strength endurance of tennis players, and effectively improve the training effect of players. This exploration provides a reference for the study of nonlinear competitive ability training mode of sports athletes.
USE AND RESEARCH OF ERP IN FINANCIAL MANAGEMENT OF LARGE ENTERPRISES USING NONLINEAR SYSTEM
Authors: SHAO CHEN , CHEN XIAOCHEN , ALMALKI MAZEN A. , ZHANG LONG
Published: September 2021

The purpose of this paper is to improve the financial management efficiency of large enterprises and enhance the overall operation vitality of enterprises. First, the connotation and characteristics of enterprise resource planning (ERP) are analyzed, and the financial ERP system is established. Then, the relevant dynamic models of nonlinear systems are classified and their characteristics are analyzed. Moreover, the system model of enterprise financial risk management is constructed based on the key success factors of project implementation risk and control flow chart of project life cycle. Finally, based on MATLAB software, [Formula: see text] large enterprise is taken as an example to evaluate the implementation effect of analytical hierarchy process (AHP) algorithm and back propagation neural network (BPNN) algorithm in ERP system. The results reveal that compared with 2019, the capital concentration in 2020 increases by 8%, the operating cost decreases by 23.6%, and the expense reimbursement process time decreases from 60–80 days to about 6 days. The expected output and assessment result of AHP are 6.912 and 6.823, respectively, and the error between them is 0.0196. The expected output and assessment result of BPNN are 6.798 and 6.675, respectively, and the error between them is 0.0121. The error value of BPNN in ERP implementation effect assessment is less than that of AHP, which indicates that the assessment effect of BPNN is better than that of AHP.
EXPLORING DYNAMIC NONLINEAR INFORMATION SYSTEM WITH SATURATION CONSTRAINT AND UNKNOWN INFORMATION
Authors: HUANG SIXING , WEI PENGCHENG , SUHAIM MAJED AHMAD , ZHANG XIANGHUA
Published: September 2021

The dynamic nonlinear system with saturation constraints and unknown information is studied to design a better control system. The controller of the general n-order affine nonlinear system is constructed utilizing the backstepping method and the improved radial basis function (RBF) that can approximate the unknown parameters. Then, the appropriate Lyapunov function is selected. The mature stability judgment theorem can prove that the designed system is semi-globally uniform and ultimately bounded. Finally, the controller is added to the system for the experiment on MATLAB. The experimental results show that the controller is reliable and the system output can approach the ideal value through the limited range of control signal. During the simulation, the reference value can be quickly tracked by the system output, and the tracking error is small only within 0.04. The proposed method can provide a reference for the design of control systems in practical engineering.
RESEARCH ON THE TOURIST FLOW FEATURE OF SCENIC AREA BASED ON FRACTAL STATISTICAL MODEL — A CASE OF ZHANGJIAJIE
Authors: DU SIYUAN , BAHADDAD ADEL A. , JIN MAOZHU , ZHANG QIAN
Published: September 2021

The purpose is to apply the fractal statistical model to analyze tourist flow feature of scenic area, and promote the effective performance of scenic area. Zhangjiajie scenic area is taken as an example. First, its natural resources characteristics, ecological environment and problems in the development of scenic area are analyzed in detail; moreover, fractal theory and fractal dimension calculation method are elaborated; finally, wavelet transform is proposed to analyze the nonlinear transformation characteristics of tourist flow. The results show that the changes of the high-frequency layer [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] all have cyclical fluctuations in the change of tourist flow. The change range of tourist flow in spring and autumn is relatively large, and the tourist flow in winter is low. Inbound tourism tourist flow has a certain seasonal regularity, with obvious characteristics of low and peak seasons. Wulingyuan scenic spot and Tianmen Mountain scenic spot of Zhangjiajie have three non-scale ranges, which are related to the scale and change of tourist flow. The concentration of inbound tourist flow in the peak season still exists in both scenic spots, and it is more and more serious. Tourist flow of inbound tourists from April to May and from September to October is large, accounting for more than half of the annual tourist flow, while most of the other periods are in the off-season.
DYNAMIC NONLINEAR CORRECTION PROCESSING OF PRESSURE SENSOR TEST SIGNAL IN INTERNET OF THINGS SYSTEM
Authors: WANG WEI , ALAHMADI DIMAH
Published: September 2021

In order to further explore the intelligent research of the pressure sensor in the pipeline, based on the existing pipeline detection methods of the Internet of Things (IoT), the signal processing method and correction research in the wireless sensor network of the IoT are explored. A multi-pressure sensor detection method of negative pressure wave is proposed. Scientific and intelligent means are used to explore the pipeline remote intelligent detection. The specific research results are as follows. In the simulation study, when the working condition of pump station 1# changes, the pressure wave first reaches A1 and B2 sensors, and finally reaches C1 and C2 sensors. If there is leakage in the pipeline, the simulation results show that when the pressure of pump station rises to 5[Formula: see text]MPa, the pressure of B1 and B2 test points decreases to about 4.9[Formula: see text]MPa, and the pressure of A1, A2, C1 and C2 test points is about 3[Formula: see text]MPa; when the pressure of the pump station drops to 3[Formula: see text]MPa, the pressure of all test points decreases. Generally speaking, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of the back end increases when the pressure increases; when the pressure decreases, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of all the back-end pressure sensor decreases. After correction, the signal data is obviously smooth and the error is obviously reduced. This study can provide scientific and effective reference for the pressure monitoring of IoT sensor network.
A FACIAL EXPRESSION RECOGNITION METHOD USING LOCAL NONLINEAR FEATURES
Authors: WEI PENGCHENG , WANG BO , ALMALKI MOHANAD AHMED , DAI XIAOJUN , ZHANG XIANGHUA
Published: September 2021

The purposes are to recognize the facial expression information and establish a fast and accurate facial expression recognition algorithm. First, the noise caused by face image shooting is eliminated by analyzing the primary methods of face recognition and preprocessing the images. Then, the Nonlinear Support Vector Machine (NSVM) is combined with the decision tree to obtain the Nonlinear Support Vector Machine Decision Tree (NSMMD) algorithm. By introducing the binary tree, NSVM has the ability of multi-classification and can extract the feature information of the face image to obtain the characteristic face. Then, the algorithm flow is introduced. Second, the characteristic face data are recognized and classified based on Generalized Regression Neural Network (GRNN), and the expression data contained in the face images are obtained to recognize the expressions of the face image. Finally, the designed face recognition algorithm and facial expression recognition algorithm are tested. The results show that the designed NSMMD algorithm has a weak dependence on feature dimension, and the face recognition rate for different datasets is above 80.1%. The GRNN algorithm will be affected by smooth parameters, its optimal parameter value is 0.1, and its recognition rate of expressions on various databases is above 92%. Therefore, the designed face recognition algorithm and facial expression recognition algorithm can provide sound recognition effects, which can reference the study of facial image feature recognition.