bluuebunny/crossref_metadata_2025 huggingface dataset:
Loading Rate Dependence of Reducer Hysteresis and Its Influence on Lost Motion Test
Authors: Shi Zhaoyao , Cheng Huiming , Yu Bo , Li Haobin
Published: September 2022

The study of hysteresis can be traced back to the 19th century. The key characteristics of hysteresis are rate independence and rate dependence. A typical system with hysteresis characteristics is the reducer. In the industrial sector, the hysteresis phenomenon of a reducer is often illustrated by the lost motion, which is always consistent with the rate independence by default. In the paper, a hysteretic model of reducer considering geometric errors is established. Theoretically, the hysteretic characteristics of the reducer are found to mostly maintain the rate dependence rather than the rate independence. The loading rate will lead to a change in the hysteretic characteristics of the reducer, thereby affecting the lost motion test and the design of the reducer. Through further experimental research, it is verified that the dependence of the loading rate will have an impact on the results of the lost motion test, and different gear materials have different effects on the dependence of the loading rate.
Quantitative Expression of a Slight Deviation of the Impact Angle in a Collision Atomizer
Authors: Ma Yarui , Cui Jiwen , Tan Jiubin
Published: September 2022

In the processing of colliding atomizers, a small change in the inclination of each orifice will double the impact angle, seriously affecting its atomization performance. In this paper, the influence of slight impact angle deviation on atomization performance was studied in steps of 1°, quantitatively, for the first time. The cavitation effect of the flow field was combined with the shape and parameters of the atomization field. FLUENT was used to simulate the internal flow field, and an independently designed atomizer with transparent nozzles was used to detect the internal flow field in real time. The collision atomization experimental platform and the laser interference particle measurement platform were built independently, and the collision angle was adjusted through a high-precision rotating table to establish the relationship between collision-angle deviation (60° ± 5°) and the atomization field performance (Sauter mean diameter, atomization cone angle, and spatial distribution of droplets). The experimental results showed that under the same injection pressure, the increase in the collision angle led to an decrease in the Sauter mean diameter and an increase in the atomization cone angle. Taking 60° as the benchmark, the particle size distribution was concentrated at ~150 μm to 300 μm within the variation range of ±2°, and the peak positions were very similar.
Multistage Cloud-Service Matching and Optimization Based on Hierarchical Decomposition of Design Tasks
Authors: Ding Shuhui , Guo Zhongyuan , Wang Haixia , Ma Fai
Published: September 2022

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.
Ontology-Based Linked Data to Support Decision-Making within Universities
Authors: Ashour Ghadeer , Al-Dubai Ahmed , Romdhani Imed , Alghazzawi Daniyal
Published: September 2022

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities.
Blow-Up of the Solution for a Semilinear Parabolic Problem with a Mixed Source
Authors: Chan Wai Yuen
Published: September 2022

A semilinear parabolic equation with the Dirichlet boundary condition is examined. The reaction source is a mixed nonlinear function. This paper investigates the existence and uniqueness of a solution. A sufficient condition for the solution being blow-up in finite time is found.
Algebraic Constructions for Novikov–Poisson Algebras
Authors: Bao Naping , Hong Yanyong
Published: September 2022

A Novikov–Poisson algebra (A,∘,·) is a vector space with a Novikov algebra structure (A,∘) and a commutative associative algebra structure (A,·) satisfying some compatibility conditions. Give a Novikov–Poisson algebra (A,∘,·) and a vector space V. A natural problem is how to construct and classify all Novikov–Poisson algebra structures on the vector space E=A⊕V such that (A,∘,·) is a subalgebra of E up to isomorphism whose restriction on A is the identity map. This problem is called extending structures problem. In this paper, we introduce the definition of a unified product for Novikov–Poisson algebras, and then construct an object GH2(V,A) to answer the extending structures problem. Note that unified product includes many interesting products such as bicrossed product, crossed product and so on. Moreover, the special case when dim(V)=1 is investigated in detail.
Machine Learning Human Behavior Detection Mechanism Based on Python Architecture
Authors: Zhu Jinnuo , Goyal S. B. , Verma Chaman , Raboaca Maria Simona , Mihaltan Traian Candin
Published: September 2022

Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.
Preliminary Results on the Preinduction Cervix Status by Shear Wave Elastography
Authors: Torres Jorge , Muñoz María , Porcel María Del Carmen , Contreras Sofía , Molina Francisca Sonia , Rus Guillermo , Ocón-Hernández Olga , Melchor Juan
Published: September 2022

The mechanical status of the cervix is a key physiological element during pregnancy. By considering a successful induction when the active phase of labor is achieved, mapping the mechanical properties of the cervix could have predictive potential for the management of induction protocols. In this sense, we performed a preliminary assessment of the diagnostic value of using shear wave elastography before labor induction in 54 women, considering the pregnancy outcome and Cesarean indications. Three anatomical cervix regions and standard methods, such as cervical length and Bishop score, were compared. To study the discriminatory power of each diagnostic method, a receiver operating characteristic curve was generated. Differences were observed using the external os region and cervical length in the failure to enter the active phase group compared to the vaginal delivery group (p < 0.05). The area under the ROC curve resulted in 68.9%, 65.2% and 67.2% for external os, internal os and cervix box using elastography, respectively, compared to 69.5% for cervical length and 62.2% for Bishop score. External os elastography values have shown promise in predicting induction success. This a priori information could be used to prepare a study with a larger sample size, which would reduce the effect of any bias selection and increase the predictive power of elastography compared to other classical techniques.
Set-Based B-Series
Authors: Alexandre dit Sandretto Julien
Published: September 2022

B-series were defined to unify the formalism of solutions for ordinary differential equations defined by series. Runge–Kutta schemes can be seen as truncated B-series, similar to Taylor series. In the prolific domain of reachability analysis, i.e., the process of computing the set of reachable states for a system, many techniques have been proposed without obvious links. In the particular case of uncertain initial conditions and/or parameters in the definition of differential equations, set-based approaches are a natural and elegant method to compute reachable sets. In this paper, an extension to B-series is proposed to merge these techniques in a common formalism—named set-based B-series. We show that the main properties of B-series are preserved. A validated technique, based on Runge–Kutta methods, able to compute such series, is presented. Experiments are provided in order to illustrate the proposed approach.
Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control
Authors: Bejarano Guillermo , Lemos João M. , Rico-Azagra Javier , Rubio Francisco R. , Ortega Manuel G.
Published: September 2022

This work addresses the energy management of a combined system consisting of a refrigeration cycle and a thermal energy storage tank based on phase change materials. The storage tank is used as a cold-energy buffer, thus decoupling cooling demand and production, which leads to cost reduction and satisfaction of peak demand that would be infeasible for the original cycle. A layered scheduling and control strategy is proposed, where a non-linear predictive scheduler computes the references of the main powers involved (storage tank charging/discharging powers and direct cooling production), while a low-level controller ensures that the requested powers are actually achieved. A simplified model retaining the dominant dynamics is proposed as the prediction model for the scheduler. Economic, efficiency, and feasibility criteria are considered, seeking operating cost reduction while ensuring demand satisfaction. The performance of the proposed strategy for the system with energy storage is compared in simulation with that of a cycle without energy storage, where the former is shown to satisfy challenging demands while reducing the operating cost by up to 28%. The proposed approach also shows suitable robustness when significant uncertainty in the prediction model is considered.
Multi-Objective Instantaneous Center of Rotation Optimization Using Sensors Feedback for Navigation in Self-Reconfigurable Pavement Sweeping Robot
Authors: Yi Lim , Le Anh Vu , Hoong Joel Chan Cheng , Hayat Abdullah Aamir , Ramalingam Balakrishnan , Mohan Rajesh Elara , Leong Kristor , Elangovan Karthikeyan , Tran Minh , Bui Minh V. , Duc Phan Van
Published: September 2022

Pavement in outdoor settings is an unstructured environment with sharp corners, varying widths, and pedestrian activity that poses navigation challenges while cleaning for autonomous systems. In this work, an approach towards navigating without collision in constrained pavement spaces using the optimal instantaneous center of rotation (ICR) is demonstrated using an in-house developed omnidirectional reconfigurable robot named Panthera. The Panthera reconfigurable design results in varying footprints, supported by passive linear joints along the robot width, with locomotion and steering action using four wheels independent steering drive (4WISD). The robot kinematics and perception sensors system are discussed. Further, the ICR selection is carried out using multi-objective optimization, considering functions for steering, varying width, distance, and clearance to avoid a collision. The framework is incorporated in a local navigation planner and demonstrated experimentally in real pavement settings. The results with optimal selection of ICR in two dimensional space within the robot footprint successfully perform smooth navigation in the constraint space. It is experimentally highlighted with four different scenarios, i.e., constraint conditions encountered by a robot during navigation. Moreover, the formulation of optimal selection of ICR while avoiding collision is generic and can be used for other mobile robot architectures.
A Line Planning Optimization Model for High-Speed Railway Network Merging Newly-Built Railway Lines
Authors: Zhou Wenliang , Huang Yujun , Chai Naijie , Li Bo , Li Xiang
Published: September 2022

This paper is devoted to developing a line-planning approach for high-speed railway networks merging newly built railway lines, which result in the change of the network’s original structure and some passengers’ travel routes. In order to exactly describe the choice of time-varying passengers and the operation of the trains, a passenger travel network with time information is constructed based on the pre-generated candidate train set. Following this, a line-planning optimization model for optimizing trains on both the existing railway network and the merged new railway line is established under the considered constraints, such as transportation resources on the network. It does not aim to only provide higher service level for passengers and increase revenue of railway enterprise, but also to ensure the continuity of the existing trains to facilitates passengers and train organization. A framework of the Simulated Annealing Algorithm is designed to solve the proposed model by combining the neighboring solution search strategies with evaluation method based on the allocation of passengers. The case of a partial high-speed railway network in China is studied to test the practicability and validity of the proposed approach.
Study on a Strong and Weak n-Connected Total Perfect k-Dominating set in Fuzzy Graphs
Authors: Elavarasan Krishnasamy , Gunasekar Tharmalingam , Cepova Lenka , Cep Robert
Published: September 2022

In this paper, the concept of a strong n-Connected Total Perfect k-connected total perfect k-dominating set and a weak n-connected total perfect k-dominating set in fuzzy graphs is introduced. In the current work, the triple-connected total perfect dominating set is modified to an n-connected total perfect k-dominating set nctpkD(G) and number γnctpD(G). New definitions are compared with old ones. Strong and weak n-connected total perfect k-dominating set and number of fuzzy graphs are obtained. The results of those fuzzy sets are discussed with the definitions of spanning fuzzy graphs, strong and weak arcs, dominating sets, perfect dominating sets, generalization of triple-connected total perfect dominating sets of fuzzy graphs, complete, connected, bipartite, cut node, tree, bridge and some other new notions of fuzzy graphs which are analyzed with a strong and weak nctpkD(G) set of fuzzy graphs. The order and size of the strong and weak nctpkD(G) fuzzy set are studied. Additionally, a few related theorems and statements are analyzed.
Improved Polymer Crystal Phase Field Model and Numerical Simulation
Authors: Yang Binxin , Wang Zhifeng , Meng Zhijuan
Published: September 2022

The existing phase field model of polymer crystallization contains many parameters that lack actual physical meaning. Although the value of these parameters can be adjusted to obtain results consistent with the experiment, it cannot correspond to the experimental conditions. In this paper, a new phase field model is established. By adjusting the latent heat, various forms of isotactic polystyrene crystals, such as dendrites, spherulites, lamellas, etc., can be simulated. Latent heat refers to the heat absorbed or released by a substance from one phase to another and has important physical meaning during the solidification process. The finite difference method was used to solve the model, and then the data were used to visualize. The simulation results were consistent with the experiment. Numerical simulation results under pure diffusion conditions show that the newly established phase field model can qualitatively predict the polymer growth process and provide a theoretical basis for the preparation and optimization of high-performance polymers. In order to make the simulation result closer to the actual growth of the crystal, the flow velocity is added in the simulation to make the melt convection. Under forced convection, the simulated polymer crystal image is no longer symmetrical.
Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting
Authors: Liu Wen-Jie , Bai Yu-Ting , Jin Xue-Bo , Su Ting-Li , Kong Jian-Lei
Published: September 2022

Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting. To fully utilize the learning capability of machine learning in time series forecasting, an adaptive broad echo state network (ABESN) is proposed in this paper. Firstly, the broad learning system (BLS) is used as a framework, and the reservoir pools in the echo state network (ESN) are introduced to form the broad echo state network (BESN). Secondly, for the problem of information redundancy in the reservoir structure in BESN, an adaptive optimization algorithm for the BESN structure based on the pruning algorithm is proposed. Thirdly, an adaptive optimization algorithm of hyperparameters based on the nonstationary test index is proposed. In brief, the structure and hyperparameter optimization algorithms are studied to form the ABESN based on the proposed BESN model in this paper. The ABESN is applied to the data forecasting of air humidity and electric load. The experiments show that the proposed ABESN has a better learning ability for nonstationary time series data and can achieve higher forecasting accuracy.
Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter
Authors: Azman Shafiqah , Pathmanathan Dharini , Thavaneswaran Aerambamoorthy
Published: September 2022

During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.
Combined Convective Energy Transmission Performance of Williamson Hybrid Nanofluid over a Cylindrical Shape with Magnetic and Radiation Impressions
Authors: Alwawi Firas A. , Al Faqih Feras M. , Swalmeh Mohammed Z. , Ibrahim Mohd Asrul Hery
Published: September 2022

This analysis focuses on extending and developing some previous studies of energy transport through nanofluids to include the states of combined convection flow of a Williamson hybrid nanofluid that flows around a cylinder. Mathematical models that simulate the behavior of these upgraded nanofluids are constructed by expanding the Tiwari and Das model, which are then solved numerically via Keller box approaches. The accuracy of the results is emphasized by comparing them with the previous published outcomes. Nanosolid volume fraction 0≤χ≤0.1, combined convection −1≤λ≤5, radiation factor 0.1≤R≤6, Weissenberg number 0.2≤We≤ 0.9, and magnetic factor  0.1≤M≤1 are the factors that have been taken into consideration to examine the energy transfer performance of Williamson hybrid nanofluid. Numerical and graphical outcomes are obtained using MATLAB, analyzed, and discussed in depth. According to the outcomes, the Weissenberg number reduces energy transfer and friction forces. Both the combined convective coefficient and the radiation factor improved the rate of energy transfer and increased the velocity of the host fluid. The fluid velocity and rate of energy transfer can be reduced by increasing the magnetic factor. The nanoparticle combination of silver and aluminum oxide (Ag-Al2O3) has demonstrated superiority in enhancing the energy transfer rate and velocity of the host fluid.
Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends
Authors: Aljedaani Wajdi , Saad Eysha , Rustam Furqan , de la Torre Díez Isabel , Ashraf Imran
Published: September 2022

Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.
Lie Symmetry Classification and Qualitative Analysis for the Fourth-Order Schrödinger Equation
Authors: Paliathanasis Andronikos , Leon Genly , Leach Peter G. L.
Published: September 2022

The Lie symmetry analysis for the study of a 1+n fourth-order Schrödinger equation inspired by the modification of the deformation algebra in the presence of a minimum length is applied. Specifically, we perform a detailed classification for the scalar field potential function where non-trivial Lie symmetries exist and simplify the Schrödinger equation. Then, a qualitative analysis allows for the reduced ordinary differential equation to be analysed to understand the asymptotic dynamics.
Pollutant Migration Pattern during Open-Pit Rock Blasting Based on Digital Image Analysis Technology
Authors: Yin Jiangjiang , Lu Jianyou , Tian Fuchao , Wang Shaofeng
Published: September 2022

Previous studies have revealed that toxic gases and dust (smoke dust) are the most common pollutants generated by the blasting operations in open-pit mines, which might lead to a threat to the environment’s condition, health and safety, and properties protection around the blasting site. In order to deal with the problems, a pollution evaluation system was established based on the fractal dimension theory (Dbox(P)) and grayscale average algorithm (Ga) in digital image-processing technology to recognize and analyze the distributions of the smoke-dust cloud, and subsequently determine the pollution degrees. The computation processes of Dbox(P) and Ga indicate three fitted correlations between the parameters and diffusion time of smoke dust. Then, a pollution index (Pi) is put forward to integrate the global and local features of Dbox(P) and Ga, and develop a hazard classification mechanism for the blasting pollutants. Results obviously denote three diffusion stages of the pollutants, mainly including generation stage, cloud-formation stage, and diffusion stage. In addition, it has been validated that the proposed system can also be utilized in single-point areas within a whole digital image. Besides, there are variation trends of the thresholds T1 and T2 in binarization with the diffusion of pollutants. With this identification and evaluation system, the pollution condition of smoke dust can be obviously determined and analyzed.
New Generalized Contractions by Employing Two Control Functions and Coupled Fixed-Point Theorems with Applications
Authors: Hammad Hasanen A. , Zayed Mohra
Published: September 2022

In this study, we obtain certain coupled fixed-point results for generalized contractions involving two control functions in a controlled metric space. Additionally, we establish some coupled fixed-point results in graph-enabled controlled metric spaces. Many well-known results from the literature will be expanded upon and modified by our results. In order to demonstrate the validity of the stated results, we also offer some examples. Finally, we apply the theoretical results to obtain the solution to a system of integral equations.
Analysis of Electromagnetic Scattering from Large Arrays of Cylinders via a Hybrid of the Method of Auxiliary Sources (MAS) with the Fast Multipole Method (FMM)
Authors: Mastorakis Eleftherios , Papakanellos Panagiotis J. , Anastassiu Hristos T. , Tsitsas Nikolaos L.
Published: September 2022

The Method of Auxiliary Sources (MAS) is an established technique for the numerical solution of electromagnetic (EM) scattering and radiation problems. This paper presents a hybrid of MAS with the Fast Multipole Method (FMM), which provides a strategy for reducing the computational cost and for solving large-scale problems without notable accuracy loss (and in a reasonable time). The hybrid MAS-FMM scheme is applied to the problem of EM scattering from an arbitrarily large array of lossless/lossy dielectric cylinders. Numerical results are presented to verify the MAS and MAS-FMM schemes, as well as to illuminate the improvements stemming from the proposed hybridization (especially the ones regarding the associated complexity and computational cost). A few concluding remarks offer a summary of this work, along with a list of possible future extensions.
An Efficient Zero-Knowledge Dual Membership Proof Supporting Pos-and-Neg Membership Decision
Authors: Yin Hongjian , Chen E , Zhu Yan , Feng Rongquan , Yau Stephen S.
Published: September 2022

In this paper, we address the problem of secure decision of membership. We present a Zero-Knowledge Dual Membership Proof (ZKDMP) protocol, which can support positive and negative (Pos-and-Neg) membership decisions simultaneously. To do it, two secure aggregation functions are used to compact an arbitrarily-sized subset into an element in a cryptographic space. By using these aggregation functions, a subset can achieve a secure representation, and the representation size of the subsets is reduced to the theoretical lower limit. Moreover, the zeros-based and poles-based secure representation of the subset are used to decide Pos-and-Neg membership, respectively. We further verify the feasibility of combining these two secure representations of the subset, so this result is used to construct our dual membership decision cryptosystem. Specifically, our ZKDMP protocol is proposed for dual membership decisions, which can realize a cryptographic proof of strict Pos-and-Neg membership simultaneously. Furthermore, the zero-knowledge property of our construction ensures that the information of the tested element will not be leaked during the implementation of the protocol. In addition, we provide detailed security proof of our ZKDMP protocol, including positive completeness, negative completeness, soundness and zero-knowledge.
NFTs and Cryptocurrencies—The Metamorphosis of the Economy under the Sign of Blockchain: A Time Series Approach
Authors: Apostu Simona Andreea , Panait Mirela , Vasa Làszló , Mihaescu Constanta , Dobrowolski Zbyslaw
Published: September 2022

Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto Coin, and Bitcoin), using data for the period between September 2020 until February 2022. The conclusions of the study are useful for cryptocurrency and NFT issuers, but also for investors on the financial market who are reconfiguring their portfolios with increasing frequency, and use these new assets for speculative or hedging purposes based on blockchain technology. The results highlighted relationships between NFTs and Ethereum, between Ethereum and Crypto Coin, and between Bitcoin and Ethereum, Ethereum being a bridge between all four. Therefore, NFTs present a relationship with Ethereum, the NFTs price had a causal effect on the price of Ethereum.
Sharpe-Ratio Portfolio in Controllable Markov Chains: Analytic and Algorithmic Approach for Second Order Cone Programming
Authors: Ortiz-Cerezo Lesly Lisset , Carsteanu Alin Andrei , Clempner Julio Bernardo
Published: September 2022

The Sharpe ratio is a measure based on the theory of mean variance, it is the measure of the performance of a portfolio when the risk can be measured through the standard deviation. This paper suggests a Sharpe-ratio portfolio solution using a second order cone programming (SOCP). We use the penalty-regularized method to represent the nonlinear portfolio problem. We present a computationally tractable way to determining the Sharpe-ratio portfolio. A Markov chain structure is employed to represent the underlying asset price process. In order to determine the optimal portfolio in Markov chains, a new hybrid optimization programming method for SOCP is proposed. The suggested method’s efficiency and efficacy are demonstrated using a numerical example.
Rota-Baxter Systems for BiHom-Type Algebras
Authors: Guo Qiaoling , Liu Ling , Lü Jiafeng , Shen Bingliang , Wang Xingting
Published: September 2022

The purpose of this paper is to study Rota–Baxter systems for BiHom-type algebras such as BiHom analogues of associative, dendriform, quadri algebras. It is shown that BiHom-dendriform structures of a particular kind are equivalent to Rota–Baxter systems. It is shown further that a Rota–Baxter system induces a weak BiHom-pseudotwistor which can be held responsible for the existence of a new product on the underlying BiHom-associative algebra. Moreover, we study the relationship between BiHom-quadri-algebras and Rota–Baxter systems for BiHom-dendriform algebras.
Research on Adversarial Domain Adaptation Method and Its Application in Power Load Forecasting
Authors: Huang Min , Yin Jinghan
Published: September 2022

Domain adaptation has been used to transfer the knowledge from the source domain to the target domain where training data is insufficient in the target domain; thus, it can overcome the data shortage problem of power load forecasting effectively. Inspired by Generative Adversarial Networks (GANs), adversarial domain adaptation transfers knowledge in adversarial learning. Existing adversarial domain adaptation faces the problems of adversarial disequilibrium and a lack of transferability quantification, which will eventually decrease the prediction accuracy. To address this issue, a novel adversarial domain adaptation method is proposed. Firstly, by analyzing the causes of the adversarial disequilibrium, an initial state fusion strategy is proposed to improve the reliability of the domain discriminator, thus maintaining the adversarial equilibrium. Secondly, domain similarity is calculated to quantify the transferability of source domain samples based on information entropy; through weighting in the process of domain alignment, the knowledge is transferred selectively and the negative transfer is suppressed. Finally, the Building Data Genome Project 2 (BDGP2) dataset is used to validate the proposed method. The experimental results demonstrate that the proposed method can alleviate the problem of adversarial disequilibrium and reasonably quantify the transferability to improve the accuracy of power load forecasting.
Adaptive Control for Narrow Bandwidth Input and Output Disturbance Rejection for a Non-Isothermal CSTR System
Authors: Sainz-García Susana Haydee , López López Guadalupe , Alvarado Víctor M. , Rumbo Morales Jesse Y. , Sarmiento-Bustos Estela , Zatarain Durán Omar Alí
Published: September 2022

This paper presents an adaptive control scheme to face the challenge of rejecting input and output disturbances. The research is put on a layer of the design and start-up of chemical plants. The emphasis is on handling disturbances appearing in a narrow band of frequencies, which illustrates standard forms of disturbances in the alluded kind of systems. The controller is made up of a central RS structure that stabilizes the closed-loop plant. A second layer boosts the control law performance by adding the Youla–Kucera (YK) filter or Q parametrization and taking advantage of the internal model principle (IMP). This practice aids in modeling unknown disturbances with online control adjustment. We probe the resultant compensator for three non-isothermal continuous stirred tank reactors connected in series. The plant should conduct a first-order exothermic reaction consuming reactant A, while an isothermal operation stays and the outlet concentration is close to its nominal value. The primary concerns are open-loop instability and steady-state multiplicity in the plant’s first unit. The control objective is to reject input and output disturbances in a band of frequencies of 0.0002Hz to 0.007Hz, whether there are variants or not in time. We test the controller with input signals depicting both variations in the auxiliary services and abrupt changes. We then compare the executions of the resultant control law with a model-based predictive control (MPC). We find comparable responses to multiple disturbances. However, the adaptive control offers an effortless control input. We also conclude that the adaptive controller responds well to reference changes, while the MPC fails due to input constraints.
On Energy Release Rate for Propagation of a Straight Crack in a Cosserat Elastic Body
Authors: Marin Marin , Vlase Sorin , Tuns Ioan
Published: September 2022

In this paper, we extend some results involving the energy release rate in the case of the propagation of a straight crack in an elastic solid. These results, approached by Gurtin and Yatomi in classical elasticity, are generalized in order to cover a Cosserat-type elastic body. We also investigate the effects of the microinertia and the couple stresses on the energy release rate.
An Optimal Random Hybrid Maintenance Policy of Systems under a Warranty with Rebate and Charge
Authors: Shang Lijun , Yu Xiguang , Du Yongjun , Zou Anquan , Qiu Qingan
Published: September 2022

Facilitated by advanced digital technologies, reliability managers can monitor system working cycles during the whole life cycle. Such a technological realization can help reliability managers ensure system reliability in real time by monitoring working cycles. In this paper, by incorporating a limited random working cycle, rebate and charge into warranty theory, a random free repair warranty with rebate and charge (RFRW-RC) is devised to ensure system reliability during the warranty stage. Under RFRW-RC, the rebate removes manufacturers’ responsibility for continuing to ensure system reliability, while the charge is a support where manufacturers continue to ensure system reliability. The warranty cost of RFRW-RC is derived, and a random discrete free repair warranty (RDFRW) is presented by simplifying RFRW-RC. By mixing random age replacement last (RARL) and classic age replacement (CAR), a random hybrid age replacement (RHAR) is designed in order to ensure system reliability during the post-warranty stage. In such an RHAR, RARL is applied to extend the replacement time during the post-warranty stage in order to maximize the remaining life of the system through warranty, and CAR is used to lower the maintenance cost of the system through warranty. The cost rate of RHAR is modeled, and the cost rate of RDFRW is offered as well by discussing parameter values. The decision variable is optimized by minimizing the cost rate model. The properties of the presented models are explored from numerical perspectives.
Riemannian Calculus of Variations Using Strongly Typed Tensor Calculus
Authors: Dods Victor
Published: September 2022

In this paper, the notion of strongly typed language will be borrowed from the field of computer programming to introduce a calculational framework for linear algebra and tensor calculus for the purpose of detecting errors resulting from inherent misuse of objects and for finding natural formulations of various objects. A tensor bundle formalism, crucially relying on the notion of pullback bundle, will be used to create a rich type system with which to distinguish objects. The type system and relevant notation is designed to “telescope” to accommodate a level of detail appropriate to a set of calculations. Various techniques using this formalism will be developed and demonstrated with the goal of providing a relatively complete and uniform method of coordinate-free computation. The calculus of variations pertaining to maps between Riemannian manifolds will be formulated using the strongly typed tensor formalism and associated techniques. Energy functionals defined in terms of first-order Lagrangians are the focus of the second half of this paper, in which the first variation, the Euler–Lagrange equations, and the second variation of such functionals will be derived.
Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis
Authors: Brazdil Pavel , Muhammad Shamsuddeen H. , Oliveira Fátima , Cordeiro João , Silva Fátima , Silvano Purificação , Leal António
Published: September 2022

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.
Assessment of CNN-Based Models for Odometry Estimation Methods with LiDAR
Authors: Clavijo Miguel , Jiménez Felipe , Serradilla Francisco , Díaz-Álvarez Alberto
Published: September 2022

The problem of simultaneous localization and mapping (SLAM) in mobile robotics currently remains a crucial issue to ensure the safety of autonomous vehicles’ navigation. One approach addressing the SLAM problem and odometry estimation has been through perception sensors, leading to V-SLAM and visual odometry solutions. Furthermore, for these purposes, computer vision approaches are quite widespread, but LiDAR is a more reliable technology for obstacles detection and its application could be broadened. However, in most cases, definitive results are not achieved, or they suffer from a high computational load that limits their operation in real time. Deep Learning techniques have proven their validity in many different fields, one of them being the perception of the environment of autonomous vehicles. This paper proposes an approach to address the estimation of the ego-vehicle positioning from 3D LiDAR data, taking advantage of the capabilities of a system based on Machine Learning models, analyzing possible limitations. Models have been used with two real datasets. Results provide the conclusion that CNN-based odometry could guarantee local consistency, whereas it loses accuracy due to cumulative errors in the evaluation of the global trajectory, so global consistency is not guaranteed.
Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language
Authors: Draskovic Drazen , Zecevic Darinka , Nikolic Bosko
Published: September 2022

In this research, a method of developing a machine model for sentiment processing in the Serbian language is presented. The Serbian language, unlike English and other popular languages, belongs to the group of languages with limited resources. Three different data sets were used as a data source: a balanced set of music album reviews, a balanced set of movie reviews, and a balanced set of music album reviews in English—MARD—which was translated into Serbian. The evaluation included applying developed models with three standard algorithms for classification problems (naive Bayes, logistic regression, and support vector machine) and applying a hybrid model, which produced the best results. The models were trained on each of the three data sets, while a set of music reviews originally written in Serbian was used for testing the model. By comparing the results of the developed model, the possibility of expanding the data set for the development of the machine model was also evaluated.
Meaningful Secret Image Sharing with Uniform Image Quality
Authors: Cheng Jingwen , Liu Lintao , Chen Feng , Jiang Yue
Published: September 2022

In meaningful secret image sharing (MSIS), a secret image is divided into n shadows. Each shadow is meaningful and similar to the corresponding cover image. Meaningful shadows can reduce the suspicion of attackers in transmission and facilitate shadow management. Previous MSIS schemes always include pixel expansion, and cross-interference from different shadows may exist when cover images are extremely unnatural images with large black and white blocks. In this article, we propose an MSIS with uniform image quality. A threshold t is set to determine the absolute salient regions. More identical bits are allocated according to saliency values in the absolute saliency region, which can improve image quality. In addition, the new identical bits allocation strategy also adjusts the randomness of the shadow images, generating shadows with uniform image quality and avoiding the cross-interference between different shadows. Experimental results show the effectiveness of our proposed scheme.
Potential Food and Nutraceutical Applications of Alginate: A Review
Authors: Bi Decheng , Yang Xu , Yao Lijun , Hu Zhangli , Li Hui , Xu Xu , Lu Jun
Published: September 2022

Alginate is an acidic polysaccharide mainly extracted from kelp or sargassum, which comprises 40% of the dry weight of algae. It is a linear polymer consisting of β-D-mannuronic acid (M) and α-L-guluronic acid (G) with 1,4-glycosidic linkages, possessing various applications in the food and nutraceutical industries due to its unique physicochemical properties and health benefits. Additionally, alginate is able to form a gel matrix in the presence of Ca2+ ions. Alginate properties also affect its gelation, including its structure and experimental conditions such as pH, temperature, crosslinker concentration, residence time and ionic strength. These features of this polysaccharide have been widely used in the food industry, including in food gels, controlled-release systems and film packaging. This review comprehensively covers the analysis of alginate and discussed the potential applications of alginate in the food industry and nutraceuticals.