bluuebunny/crossref_metadata_2025 huggingface dataset:
PHYTOANALYTICAL AND PHYTOCHEMICAL INVESTIGATION OF ESSENTIAL OILS OF LEAVES OF TWO MAJOR COMMERCIAL VARIETIES Citrus aurantium L. AND Citrus sinensis L.
Authors: T. Prabahar  , Yoganandam G. Prakash , Rekha J. Banu , M. Kumar  , Venkateswarlu B. S.
Published: December 2022

Plants are the fashionable foundation of drugs for the mainstream of the world people. The present study was carried out to investigate the constituents of leaves of two major commercial verities of sweet and bitter orange Citrus aurantium L and Citrus sinensis L. Alkaloids, flavonoids, steroidal glycosides, volatile oils, terpenoids, and sterols were confirmed by preliminary phytochemical studies. The physicochemical parameters like Loss on drying, Extractive values, and Ash values were also evaluated. GC-MS analysis of essential oils of Citrus aurantium L. contains 25 compounds and Citrus sinensis L. showed the presence of 30 bio-active compounds. The major components of Citrus aurantium are 1,3-Cyclohexadiene, trans-3-Caren-2-ol, Eucalyptol, γ-Terpinene, Linalool, 2- Cyclohexen-1-ol, 6-Octenal, Terpinen-4-ol, Benzene, α-Terpinyl acetate, 2,6-Octadien-1-ol, Cyclohexane, Caryophyllene, Tetra methyl bicyclo undeca-2,6-diene, 4-Isopropyl-3,7-dimethyloctahydro-1H-cyclopentane, Naphthalene, Caryophyllene oxide, 1-Naphthalenol, Cardinol, and Phytol. Whereas Citrus sinensis L essential oil shows the presence of Eucalyptol, α- Phellandrene, D-Limonene, 3-Carene, Eucalyptol, β-Ocimene, γ-Terpinene, Linalool, Isoneral, Terpinen-4-ol, 6-Octenal, 3,7-dimethyl, α-Terpineol, Citronellol, 2,6-Octadien-1-ol, Neral, Geraniol, trans-Geranic acid methyl ester, 6-Octen-1-ol, 2,6-Octadien-1-ol, 3,7-dimethyl acetate, 3,7-dimethylacetate, Geranyl acetate, Caryophyllene, Humulene, Caryophyllene oxide, and Phytol as the notable compounds. However, both species show similar compounds in their GC-MS analysis with little difference. This study will help us to know the similarities and differences between the closely related medicinally important species of C.sinensis and C.aurantium on their phytochemical nature.
MICROWAVE-ASSISTED In-situ GENERATED COTTON/SILVER NANOPARTICLES COMPOSITE BY USING AN AQUEOUS EXTRACT OF Acacia Lignin BARK
Authors: Pamula Uday Prakash , Y. Prashanthi  , T.B. Patrudu  , Nageswara Rao Tentu
Published: December 2022

In general, silver has been recognized as an antimicrobial agent and helpful for the treatment of burns, wounds, and various infections. Now a day silver nanoparticles production in eco friend method is required. Here in, we report a novel method of silver nanoparticles in situ to generate a cotton matrix using Acacia lignin bark aqueous extract as a reducing agent and stabilizer. The silver nanoparticles were in situ generated by combining an Acacia lignin bark extract, cotton fabric, and aq.AgNO3 in one pot, irradiated under microwave for five minutes. The silver nanoparticles (AgNP’s) formed as film inside and outside of the cotton as a matrix was characterized by TEM, FTIR, UV, and EDX spectroscopic techniques. These bio-renewable films show good antibacterial activity and can be used for the bandage, and packing material for medical purposes.
THE ANTIBACTERIAL PEPTIDES (AMPs) ORIGINATED FROM TRYPTIC HYDROLYSIS OF Naja sumatrana VENOM FRACTIONATED USING CATION EXCHANGE CHROMATOGRAPHY
Authors: Ahmed Naseer , Raharjo Slamet , Tri Swasono Respati , Raharjo Tri Joko
Published: December 2022

Bioactive peptides bring revolutions in the medical field and become an attractive target for new antibiotics. The bioactive peptides can be derived from the hydrolysis of protein. This study aims to explore the antibacterial peptide from venom hydrolysate of the Spitting cobra (Naja sumatrana). The venom protein was isolated from venom using a gel filtration technique. The digestion of venom protein was performed using trypsin with and without alkylation and reduction. After that, fractionation was carried out using a strong cation exchange solid-phase extraction, followed by antibacterial activity testing. The active peptides present in the active fractions were identified using HRMS. The protein hydrolysis with pretreatment using alkylation and reduction gave almost two-fold higher than that of untreated hydrolysis. Both the venom protein and the hydrolysate showed antibacterial activity against Escherichia coli but there was no activity against Staphylococcus aureus. Antibacterial activity has been detected in numerous cation exchange fractions against Escherichia coli and Staphylococcus aureus. The four best bioactive peptides were identified with amino sequences of VYGGDSR, YTPTNK, TQFSDR, and TFQDSR. In addition, molecular docking analysis revealed that the peptides showed high-affinity scores, and the model best matched the DNA gyrase subunit B by salt bridge formation, whereas hydrogen bond interactions proved inhibition of bacterial replication as an antibacterial mechanism.
NbCl5+AgClO4 AS A VERSATILE COMBINED CATALYST SYSTEM FOR AN ACCELERATED SYNTHESIS OF 1, 4- DIHYDROPYRIDINE SCAFFOLDS
Authors: Bora Manisha , Kadam Kailas , Wani Punam , Kamble Narendra
Published: December 2022

Herein presented work describes the catalytic potential of NbCl5+AgClO4 as a combined and convenient catalyst system for a straightforward, safe, quick, and single-pot synthesis of 1, 4-dihydropyridine (DHP) scaffolds. It was investigated that a small amount of NbCl5+AgClO4 is sufficient and potential enough to bring out the solvent-free synthesis of 1, 4-dihydropyridine scaffolds from a three-components, the cyclic reaction between substituted aldehyde(s), ethyl-3-oxobutanoate, and ammonium ethanoate. It is noteworthy to mention that good to excellent yields of DHPs were achieved by developing an environmentally compatible, sustainable, mild, and simple synthetic protocol. A range of differently substituted aldehydes was observed to undergo herein developed catalytic protocol smoothly to offer good yields of DHPs in 3 to 5 h at room temperature. Representative DHPs are characterized by adequate analytical techniques such as FTIR, 1HNMR, 13CNMR, and mass spectrometric techniques
Режим "чорних дошок" у Дніпропетровській області
Authors: Романець Наталя Радомирівна
Published: December 2022

Мета дослідження полягає у висвітленні «чорнодощечного» режиму, запровадженого у Дніпропетровській області під час хлібозаготівельної кампанії 1932/1933 р.: масштабів, застосованих репресивних заходів, пропагандистських акцій, їхнього впливу на виконання хлібозаготівлі та наслідків.
Digitalization of Latin America’s transport and logistics industry within the «Industry 4.0» concept (4IR)
Authors: Kuchinov Pavel
Published: December 2022

The issue of innovative development of Latin American countries in the paradigm of «Industry 4.0» is currently most acute. The article analyzes the main stages of development and modernization of digital solutions in the transport and logistics services. Due to the significant technological backwardness of the Latin America from other macroe-conomic regions and pronounced cross-country differentiation within the Latin American region itself, the need to introduce digitalization systems has become a priority at the government level. The significant fragmentation of digitalization within the transport and logistics industry of the Latin American region itself is also an important subject of present research. The article describes the main strategic advantages that are possible due to the digitalization: reduction of transport costs, formation of additional budget revenues, ensuring full control of the production and sales cycle, optimization of human resources, reduction of anthropogenic factors in the organization of transport and ware-house operations. Integration of digital solutions is a factor of economic well-being and a condition for technological and innovative survival. Cooperation between Latin American countries and the Russian side through the implementation of digital technologies, also in the segment of road cargo transportation, as well as public transport and improving road safety and speed control, seems very promising. This will strengthen Russia's bilateral cooperation with Latin American countries in the field of technological research and innovation, and indirectly strengthen their digital independence.
The modern US policy towards Latin America and the Caribbean
Authors: Хлопов Олег
Published: December 2022

The article analyzes the problems facing the US foreign policy under Joseph Biden administration in relation to the countries of Latin America and the Caribbean, reveals the nature of the strategy of the D. Trump administration, that was aimed at curbing the illegal immigration to the United States from Latin American countries. The author considers the results of the 9th Summit of the Americas in 2022, the attempts by the United States to maintain their influence, expand interaction throughout the Western Hemisphere against the background of China's foreign economic strategy, which largely coincides with the interests of the LAS countries. The author argues that the excessive geo-political ambitions of the United States and their course for global leadership, do not taking into account the interests and characteristics of the states of Latin America, contradict with the possibilities of the United States in solving the variety of problems in the region. To maintain US positive image, american policymakers must recognize that their international initiatives to implement their foreign policy initiatives in the Wertern Hemisphere are severely limited.
Peron: a look into a multipolar world
Authors: Rossi Sergio A.
Published: December 2022

The review provides a brief presentation of the works of Argentine President Juan Domingo Peron, published in Russian. The author points to the importance of the Peronist concept for modern society.
Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting
Authors: Mozdbar Sima , Petersen Melissa , Zhang Fan , Johnson Leigh , Tolman Alex , Nyalakonda Ramyashree , Gutierrez Alejandra , O’Bryant Sid
Published: December 2022

Background:

Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer’s disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD.

Objective:

This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting.

Methods:

Participants were recruited from Alzheimer’s Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed.

Results:

Data were examined on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer.

Conclusion:

The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.
Research on the Influencing Factors of Quality and Safety Control of the Whole Industrial Chain of Agricultural Products Based on LDA Model – From the Perspective of Agricultural Leading Enterprises
Authors: Li Xiaofei , Li Wen , Jin Yuantian , Liu Runjie
Published: December 2022

Food safety has always been an important concern of people. Under the rural revitalization strategy, as the main force in the process of agricultural and rural modernization agricultural product quality and safety control of agricultural leading enterprises is the focus of work. With the gradual formation of well-off and affluent consumption structure, consumers have produced “from farmland to table” agricultural product quality and safety requirements. However, in recent years, there are still omissions and deficiencies in the quality and safety control of agricultural products. By studying a large number of literature, this paper uses big data processing technology to crawl web data, this paper conducts research and analysis on the quality safety and control status of the whole industrial chain of agricultural products. Based on the whole industrial chain of agricultural products, from the perspective of farmers, leading agricultural enterprises, governments, dealers, consumers and other subjects, LDA theme analysis model is used to identify and collect the quality and safety control factors of the whole industrial chain of agricultural products. This paper puts forward the improvement countermeasures and suggestions of agricultural product quality and safety control under the traceability system, which provides a theoretical basis and guidance for agricultural leading enterprises to ensure the quality and safety of agricultural products.
Modeling of Autonomous Grasping Method for UAV Guided by Vision
Authors: Zhang Yunze , Yue Gaofeng
Published: December 2022

For the current problems of separation of flight control and gripping control of the gripping UAV and redundancy of UAV degrees of freedom and robotic arm degrees of freedom. In this paper, the characteristics of multi-rotor UAV with high mobility and multiple degrees of freedom are used to replace the traditional mechanical gripper structure. At the same time, binocular vision technology is used for target information acquisition, the target position is converted into UAV flight attitude control commands, and conduct mathematical modeling and analysis of a multi-rotor grasping UAV.
Measurement and Modeling of Long Multi-Conductor Shielded Cable Based on Fast Vector Fitting Algorithm
Authors: Zhang Jinsheng , Chen Wei
Published: December 2022

The transmission lines need to be modeled accurately to predict the voltage reflections and EMI levels of the converter system. In this paper, a transmission line model based on fast vector fitting algorithm is proposed. At high frequencies, the parasitic capacitance of the cable conductor causes the cable impedance to resonate. Due to skin effect and proximity effect, the cable parameters change with frequency. The frequency equivalent model is used to characterize the effect of cable parameters varying with frequency. When cable is open-circuited and short-circuited, and the high-frequency impedance characteristics of the cable are measured using an impedance analyzer. Through the relationship between measured impedance and model impedance, the impedance of the model is obtained. The impedance is fitted by fast vector fitting algorithm. Finally, the feasibility and validity of the proposed model are verified by comparing the simulation and measurement results in the frequency and time domains.
Health Assessment of Higher Education System Based on 3D Baseline Cube Model
Authors: Zheng Juan , Zhang Hangtao , Xiao Jinrong , Lu Jingyuan , Li Yalin
Published: December 2022

Educational ecology is a branch of pedagogy that uses ecosystem theory to study the law between education and the external ecological environment. In this paper, We use ecology research methods to evaluate the health and sustainability of the education system. Based on Educational stability, carrying capacity and teaching quality, we build the three-dimensional baseline cube model (called the Tri-E model) for comprehensive measurement. In terms of educational stability, we build educational pyramids according to different academic qualifications and calculate Gini coefficient according to geographical distribution and the Educational Stability Index (ESI) is summarized. For the carrying capacity and quality of education, the weight of each factor is determined by the AHP. The Education Carrying Capacity Index (ECCI) and Education Quality Index (EQI) are calculated according to the relevant indicators of the countries studied. These indicators will determine a vector in the 3D Baseline cube to obtain a measure of the health status of higher education Tri-E Index (TEI). We analyzed some representative countries (UK, India, and China) with TEI of 0.741, 0.521, and 0.665, respectively. Referencing China’s education reform, we put forward the current problems facing the Indian education system and how to reform it. After a quantitative analysis of the actual resistance index, the conclusion is that it is difficult for the reform to be completely successful.
Simulation Analysis of Slope Stability Strengthened by Frame Anchor Considering Anchor Failure
Authors: Chen Jiexi , Ye Shuaihua , Zhang Jun
Published: December 2022

In engineering, due to the direct or indirect influence of a variety of other factors, lead to local anchor failure, thus forming a hidden danger of slope safety. Through the specific slope engineering case, the corresponding simulation model is established by using PLAXIS 3D to calculate the slope displacement and safety factor under different anchor failure conditions. At the same time of calculating the safety factor of slope stability, the displacement variation law of slope is analyzed, and the relationship curve between slope stability and displacement has been obtained. The results show that there is an obvious split zone in the unsupported slope, and the displacement of the slope is greatly improved after support. The closer the failed anchor is to the middle of the slope and the top of the slope, the less influence it has on the slope safety.
Research on the Evaluation Index System of Autonomous Ship Berthing and Unberthing Function
Authors: Zhang Qian , Bai Xu , Li Yongzheng , Yang Li , Zhang Haihua
Published: December 2022

At present, there are no standards and specifications of evaluation for autonomous ships. In response to this problem, an evaluation index system for autonomous berthing and unberthing is established. By analyzing the factors affecting autonomous berthing and unberthing, combining the key points of operations and relevant conventions and norms, the evaluation indicators are initially screened. Summarize and classify the indicators, and the final evaluation index system is constructed according to the four stages of berthing area identification, path planning, inshore manipulation and unberthing manipulation. Based on this system, the fuzzy Analytic Hierarchy Process (F-AHP) is adopted to calculate the weight of indicators. The triangular fuzzy number is used to fuzzy the parameters to resolve the uncertainty among the data. Ultimately, by sorting all indicators, it is concluded that the number of rudder reversals accounts in path planning for the largest proportion of all indicators and requires particular attention to this aspect.
An Improved Method of Coal Gangue Deep Learning Recognition
Authors: Zheng Libo , Feng Yuan
Published: December 2022

Aiming at the problems of the existing coal gangue recognition methods based on image processing, such as the inaccurate positioning of coal gangue targets on the conveyor belt, and the influence of belt background and local surface of coal gangue on the recognition process, a coal gangue recognition method based on deep learning is proposed. This method uses the CornerNet Lite model to locate the coal gangue in the image. According to the CornerNet series key point pooling method, four vertices of the rectangle of the coal gangue position in the frame and a key point with significant gray change of the coal gangue itself are found. The image is divided into four blocks by using these points, and then the targets are classified respectively. Finally, the coal gangue recognition results of the whole image are obtained. This method improves the positioning of coal gangue on the belt, reduces the interference of conveyor belt background and the influence of local surface of coal gangue during target positioning, and then improves the accuracy of coal gangue identification. The experimental results show that this method can effectively improve the recognition accuracy of coal gangue, reaching 91.3%.
Analysis of the Impact of Inlet and Outlet Installation Angle on CFD Performance of Centrifugal Fan
Authors: Wang Di , Zhang Feng , Xu Li , Qiu Wei , Zhu Weiguo
Published: December 2022

In the centrifugal fan, the impeller part is a very important part, its structure and installation largely determine the performance of the centrifugal fan.In this paper, the hydrodynamic properties of centrifugal fan are analyzed by computational fluid dynamics (CFD) method, and the cloud diagrams of static pressure, dynamic pressure, full pressure and velocity vector diagram at the outlet are obtained, and their influence on hydrodynamic properties of centrifugal fan is analyzed by changing the installation Angle of inlet and outlet.It provides some reference for performance optimization of centrifugal fan.
Carbon Price Prediction Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis and Convolutional Neural Network
Authors: Wang YuanLing , Yan Zhengjie , Bai Yun
Published: December 2022

China has launched national carbon trading marked in 2021, and up to now, Hubei has the largest proportion of carbon trading volume, it is totally important to research the carbon trading price in Hubei. In this paper, we propose a new model for carbon price in Hubei, which is combine complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) with convolutional neural network (CNN). Firstly, carbon price is decomposed by CEEMDAN into various intrinsic mode function (IMF) which are combined using sample entropy approach. Then, CNN is used to establish a point prediction model. Finally, we calculate the mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of the model are 0.4893, 0.6809, and 0.9754, respectively. Compare with other two models, the hybrid model proposed in this paper exhibits the best performance.
Research on Optimal Trading Strategy Model for Wind Farms
Authors: Wu Lijun , Ma Kexin , Lu Xu , Wu Yue , Tan Tao
Published: December 2022

This paper investigates the optimal model for power trading in the context of wind farm trading. The concept of penalty intensity is introduced into the model for the first time, and a trading strategy of more power when the penalty intensity is low and less power when the penalty intensity is high is obtained. Sensitivity analysis is also used to investigate and compare the payoffs generated by the model parameters for different trading strategies. The results show that the model has good robustness. The model results are extremely important references for firms to apply to trading decisions.
Prediction of Agricultural Products Logistics Demand in Five Provinces of North China Based on BP Neural Network
Authors: Dong Wei , Zhang Kexin , Zuo Min , Yan Wenjing , Zhang Qingchuan
Published: December 2022

Agricultural product logistics is the key to ensuring people’s livelihood. The prediction of agricultural products’ logistics demand is an important guarantee for the rational planning of agricultural products logistics. However, the demand for agricultural product logistics is affected by many factors, which increases the complexity of its prediction. Therefore, taking the logistics demand of agricultural products as the research object, this paper constructs an index system from five aspects: the level of economic development, the level of industrial structure, the level of logistics development, the supply factors of agricultural products and human factors. Using the nonlinear mapping ability of the BP neural network, this paper constructs a BP neural network model to predict the logistics demand of agricultural products and takes the five provinces in North China as an example to predict the logistics demand of agricultural products. The results show that the established model has a strong ability to describe the nonlinear relationship between agricultural products logistics demand and its influencing factors, and can provide a basis for rational planning and policy-making of agricultural products logistics.
Research on IGBT Fault Diagnosis Method Based on Neural Network Optimization Algorithm
Authors: Ma Dezhong , Ding Wenfei
Published: December 2022

Aiming at the actual situation that the current detection methods are difficult to achieve rapid diagnosis of different IGBT fault states and real-time synchronous detection, an IGBT fault detection method based on genetic algorithm optimized neural network is proposed. Methods Using wavelet packet analysis to collect fault samples, construct feature vectors, use feature vectors as the input of the neural network, and use the fault results as the output of the neural network, construct a BP neural network model, and further use genetic algorithms to optimize the fault diagnosis model. The relationship between the fault characteristics and the fault results is established through the neural network to realize the fault location, solve the problem that the IGBT is difficult to accurately and quickly diagnose when the fault occurs, effectively improve the safety of the IGBT, and reduce the maintenance cost of the IGBT. Time guarantees the stability of production operations. The feasibility of the proposed method is verified by modelling and simulation of the inverter, which has practical reference value.
Study on Safety Evaluation Index System and Classification Method of Water-Rich Tunnel Structure
Authors: Hu Xuebing , Guo Hongyan , Wang Huixia , Huang ShiYu , Li Zheng
Published: December 2022

In order to effectively evaluate the structural safety of water-rich tunnel in the construction process, two indexes of groundwater and lining safety reserve were introduced, and the seven factor evaluation index system of water-rich tunnel structural safety was constructed. The fuzzy evaluation method based on comprehensive weighted algorithm was put forward and applied to a water-rich tunnel project in Southwest China. The results showed that: (1) the constructed index system is more systematic and comprehensive. (2) The proposed evaluation method can effectively reduce the subjectivity of expert scoring and improve the accuracy and rationality of results. (3) The safety evaluation grade of tunnel calculated by this method is consistent with the current safety state of the project, indicating that this study has certain practicability.
Substrate Effects on the Plasmonic Resonance of Graphene Nano-Antenna
Authors: Kavitha S  , Sairam K.V.S.S.S.S. , Singh Ashish
Published: December 2022

In this manuscript, a pentagon shaped plasmonic graphene nano patch antenna is designed and investigated on silicon dioxide, silicon nitride and zinc oxide substrates. The optical characteristics of the graphene such as conductivity and permittivity are analyzed at various values of chemical potential using Kubo conductivity model. The plasmonic resonance of the proposed nano-antenna is analyzed through the graphene chemical potential varied in the span from 0.0 eV to 2.0 eV. It is observed that the proposed pentagon shaped graphene nano patch antenna shows good plasmonic resonance characteristics at 29.8750 THz with the reflection coefficient of -27.4153 dB, 91.5650 THz with -27.5179 dB and 853.7350 THz with -40.3267 dB on silicon dioxide, zinc oxide and silicon nitride substrates respectively. These optimum characteristics are observed at the chemical potential values 2.00 eV and 0.50 eV for the proposed graphene nano patch antenna.
Risk Classification Study of Carbofuran in Vegetables Based on K-Means++ Algorithm
Authors: Jiang Tongqiang , Yang Qi , Liu Tianqi , Dong Wei , Jiao Yongchun , Zhang Qingchuan
Published: December 2022

This study used dietary exposure assessment method and K-means++ clustering algorithm to construct a carbofuran risk grading model to assess the risk of carbofuran in six vegetable categories in 20 provinces in China using carbofuran sampling and testing data and consumption data in 2020. The clustering algorithm classified the risk level of carbofuran in vegetables into 3 levels: low, medium, and high. The number of low risk combinations accounted for 92.5%, and the high-risk combinations were bulb vegetables in Hebei and leafy vegetables in Shaanxi. This study uses objective data to build a risk classification model and a data-driven approach to risk classification, enhancing the objectivity and validity of the experimental results.
Convolutional Neural Network Analysis for Modulation Classification of Wireless Communication Signal
Authors: Li Huiqiong , Wei Ying , Zhu Changjian
Published: December 2022

Modulation classification detects the modulation type of received signals to guarantee that the signals can be correctly demodulated and that the transmitted message can be accurately recovered. For the modulation classification of M-PSK, M-QAM and M-APSK modulated signals with similar constellation maps, we analyze waveform characteristics of the signal in the time domain. Based on the waveform characteristics of the signal, we explore the feasibility of using convolutional neural network (CNN) to identify the modulation classification of the signal. We analyze the data input structure and network model required for modulation classification using CNN. Considering the impairment of the signal by additive white Gaussian noise (AWGN), clock offset and Rician multipath fading, a combined channel is simulated in this paper to obtain the impaired data as dataset. The simulation results show that CNN has great potential for modulation classification.
Lane Change Decision and Trajectory Planning for Intelligent Cars in Curved Road Scenarios
Authors: Wang Shufeng , Wang Xinkai , Wang Shihao
Published: December 2022

In order to improve the efficiency of curved road passage and the safety of lane changing, the curved road section is used as the research object, and the curved road lane changing model is established by analyzing the lane changing decision and trajectory planning of intelligent vehicles. In the lane change decision, the influence of different driving styles, curved road radius, car type and other factors are considered, and the improved car ellipse model is proposed and applied to the minimum safety distance for free lane change of intelligent vehicles. In terms of trajectory planning, different lane change executable regions are proposed for drivers with different driving styles, and the optimal lane change trajectory within the lane change executable region is obtained by filtering the trajectory clusters under the Frenet coordinate system through the evaluation function. MPC control is used for trajectory tracking. Finally, the validity of the intelligent vehicle curved road lane change model is verified through Carsim and Simulink simulation.
Potential Field Based Intelligent Vehicle Formation Control for Unstructured Roads
Authors: Wang Shufeng , Wang Shihao , Wang Xinkai
Published: December 2022

In order to obtain environmental information safely and efficiently, a four-vehicle formation model is used as an example to study intelligent vehicle formation control under unstructured roads. Firstly, based on the formation distance and environmental constraints, a formation planning framework is proposed; secondly, a formation control model combining artificial potential field and virtual navigator is established by introducing a fixed-point potential field; Based on the elasticity theory and boundary potential field model, the virtual navigator R-value is improved, and then, the formation model stability is verified. Finally, simulation analysis is performed, and the results show that the established formation model can be formed stably and autonomously, and at the same time, formation maintenance and transformation can be realized.
Research on Community Resilience Theory Based on Entropy and Self-Organization and Its Application in Decision
Authors: Zhao Pengxia , Zhu Wei , Wang Yafei , Ma Yingnan , Shi Yanqin , Zhang Qiujie , Zhou Peiling , Li Tie
Published: December 2022

In order to improve the ability of community governance and reduce the occurrence of accidents, based on the entropy and self-organization theory, this paper first analyzes the influencing factors of community resilience, analyzes the process of emergency and the key factors of response from the change of entropy value of community security system, constructs the mathematical expression of community resilience and entropy value, extracts the evolution law of community resilience, and then constructs the decision theory of building entropy effect and improving community resilience from the perspective of input-output benefit. The results show that the community resilience is positively correlated with the absolute value of negative entropy; the community resilience is negatively correlated with the absolute value of positive entropy; the system resilience can be improved by allocating the limited degree and quantity value according to the importance of the impact on the correlation of each subsystem in the community system.
The Evaluation Method of Running State of the 10kV Cable Head Based on Flow Temperature Complex Monitoring Model
Authors: Huang Qingjiang , Mei Linchang , Qin Fuxiang , Wang Hejing
Published: December 2022

This paper describes evaluation methods of running state of the 10kV cable head based on flow temperature complex monitoring model. The one is the physical model of 10kV cable joint flow temperature curve; the other one is an evaluation method of running state of the 10kV cable head based on flow temperature and other complex parameters monitoring. It is to compare the traditional single parameter evaluation and the evaluation difference and leadership under different clock states, and through the method of this paper, there is a comprehensive and objective evaluation of the cable state.
On the Study of the Optimization and Coordination Countermeasures of Container Port in the Greater Bay Area Based on Systematic Cluster Analysis
Authors: Yang Jingshan , Xu Mindan , Yang Jingxin , Lin Jiaying , Yang Xiuyan , Shi Fangzhe , Li Qiuyi , He Junjie , Ao Yulin , Zhang Likai , Deng Xuhong , Chen Boning
Published: December 2022

At present, there are a large of container ports in the Greater Bay Area,which is existing some problems,such as the overlapping functionality of the port,lack of scientific positioning,fierce competition,lack of coordination mechanism and so on.In order to accelerate the integration between the ports,form completely advantages,win-win cooperation,production factors configuration of optimal new development mode,to make the balance between the port. This paper will based on the current situation of the Greater Bay Area to pursue the research about the theory of systematic cluster analysis and the advantages and disadvantages in its application area. The data sources of the container ports in the Greater Bay Area will be used for the experiment and eleven ports’ classification and stratification with SPSS software to systematic analysis. In the end,this paper will combine with the experience of coordinated development of key port groups at home and abroad to provide feasible,reliable and credible solutions and suggestions for the construction of the port group in the Guangdong-Hong Kong-Macao Greater Bay Area.This study has theoretical guidance and practical significance for the coordinated development of container ports in the Guangdong-Hong Kong-Macao Greater Bay Area.
A Hybrid Model of Argument Concerning Preferences Between Statutory Interpretation Canons
Authors: Araszkiewicz Michał
Published: December 2022

This paper extends the existing account of statutory interpretation based on argument schemes theory. It points out that the preference relations among statutory canons are not always determined by some predefined rules, but in certain systems of law or legal domains, it is necessary to argue these preference relations on the basis of case law. A set of factors favouring linguistic arguments and teleological arguments is presented, and a case-based argument scheme for the assignment of preference relations is reconstructed.
A Multi-Step Approach in Translating Natural Language into Logical Formula
Authors: Nguyen Ha-Thanh , Wachara Fungwacharakorn , Nishino Fumihito , Satoh Ken
Published: December 2022

Translating often has the meaning of converting from one human language to another. However, in a broader sense, it means transforming a message from one form of communication to another form. Logic is an important form of communication and the ability to translate natural language into logic is important in many different fields, in which logical reasoning and logical arguments are used. In the legal field, for example, judges must often reason from facts and arguments presented in natural language to logical conclusions. In this paper, toward the goal of support for this kind of reasoning with machines, we propose a method for translating natural language into logical representations using a combination of deep learning methods. Our approach contributes methodologies and insights to the development of computational methods for converting natural language into logical representations.
Conditional Abstractive Summarization of Court Decisions for Laymen and Insights from Human Evaluation
Authors: Salaün Olivier , Troussel Aurore , Longhais Sylvain , Westermann Hannes , Langlais Philippe , Benyekhlef Karim
Published: December 2022

Legal text summarization is generally formalized as an extractive text summarization task applied to court decisions from which the most relevant sentences are identified and returned as a gist meant to be read by legal experts. However, such summaries are not suitable for laymen seeking intelligible legal information. In the scope of the JusticeBot, a question-answering system in French that provides information about housing law, we intend to generate summaries of court decisions that are, on the one hand, conditioned by a question-answer-decision triplet, and on the other hand, intelligible for ordinary citizens not familiar with legal documents. So far, our best model, a further pre-trained BARThez, achieves an average ROUGE-1 score of 37.7 and a deepened manual evaluation of summaries reveals that there is still room for improvement.
Unpacking Arguments
Authors: Bench-Capon Trevor , Verheij Bart
Published: December 2022

Although argumentation is often studied in AI using abstract frameworks, actual debate often shows a dynamic interaction between argument structure and attack. Often intermediates steps in the reasoning are omitted, but it may be these intermediate steps which are the vulnerable parts of the argument. Inspired by Loui and Norman’s work on the rationale of arguments, we study the relation between argument structure and attack in terms of the unpacking of arguments. The paper provides an analysis of two kinds of rationales discussed by Loui and Norman. Example dialogues inspired by Dutch tort law are used for illustration.
The Illinois Intentional Tort Qualitative Dataset
Authors: Blass Joseph , Forbus Kenneth
Published: December 2022

We introduce the Illinois Intentional Tort Qualitative Dataset, a set of Illinois Common Law cases in Assault, Battery, Trespass, and Self-Defense, machine-translated into qualitative predicate representations. We discuss the cases involved, the natural language understanding system used to translate the cases into predicate logic, and validation measures that serve as performance baselines for future AI research using the dataset.
Fundamental Revisions on Constraint Hierarchies for Ethical Norms
Authors: Fungwacharakorn Wachara , Tsushima Kanae , Satoh Ken
Published: December 2022

This paper studies constraint hierarchies for ethical norms, which are unwritten and may be relaxed if they conflict with stronger norms. Since such ethical norms are unwritten, initial representations of ethical norms may contain errors. For correcting those errors, this paper examines fundamental revisions on constraint hierarchies for ethical norms. Although some revisions on representations for ethical norms have been suggested, revisions on constraint hierarchies for ethical norms have not been completely investigated. In this paper, we categorize two fundamental types of revisions on such constraint hierarchies, namely preference revision and content revision. We also compare effects of those revisions in the criteria of syntactic and semantic changes, which are common criteria of revisions on legal theories. From the comparison, we found that preference revision tentatively makes lower syntactic changes. However, its computation is intractable, incomplete, and potentially makes a large number of semantic changes. On the other hand, we show that content revision on constraint hierarchies can make a small number of semantic changes. However, the content revision tentatively produce a large number of syntactic changes. This comparison leads to the possibility of optimization between preference revision and content revision, which we think is an interesting future work.
WhenTheFact: Extracting Events from European Legal Decisions
Authors: Navas-Loro María , Rodríguez-Doncel Víctor
Published: December 2022

This paper presents WhenTheFact, a tool that identifies relevant events from European judgments. It is able to extract the structure of the document, as well as when the event happened and who carried it out. WhenTheFact builds then a timeline that allows the user to navigate through the annotations in the document.
Cognition Before and After COVID-19 Disease in Older Adults: An Exploratory Study
Authors: Caselli Richard J. , Chen Yinghua , Chen Kewei , Bauer Robert J. , Locke Dona E.C. , Woodruff Bryan K.
Published: December 2022

Background: Older age is a major risk factor for severe COVID-19 disease which has been associated with a variety of neurologic complications, both acutely and chronically. Objective: We sought to determine whether milder COVID-19 disease in older vulnerable individuals is also associated with cognitive and behavioral sequelae. Methods: Neuropsychological, behavioral, and clinical outcomes before and after contracting COVID-19 disease, were compared in members of two ongoing longitudinal studies, the Arizona APOE Cohort and the national Alzheimer’s Disease Research Center (ADRC). Results: 152 APOE and 852 ADRC cohort members, mean age overall roughly 70 years, responded to a survey that indicated 21 APOE and 57 ADRC members had contracted COVID-19 before their ensuing (post-COVID) study visit. The mean interval between test sessions that preceded and followed COVID was 2.2 years and 1.2 years respectively for the APOE and ADRC cohorts. The magnitude of change between the pre and post COVID test sessions did not differ on any neuropsychological measure in either cohort. There was, however, a greater increase in informant (but not self) reported cognitive change in the APOE cohort (p = 0.018), but this became nonsignificant after correcting for multiple comparisons. Conclusion: Overall members of both cohorts recovered well despite their greater age-related vulnerability to more severe disease.
Brain Activation During Working Memory Task in Amnestic Mild Cognitive Impairment Patients and Its Association with Memory and Attention
Authors: Liu Yajing , Zeng Zhenhua , Huang Shuyun , Shang Pan , Lv Zeping , Wang Yukai , Luo Jiali , Chen Jinjuan , Shi Jian , Huang Qiaobing , Xie Haiqun , Chen Zhongqing
Published: December 2022

Background: Amnestic mild cognitive impairment (aMCI) is regarded as a transitional state of Alzheimer’s disease, with working memory (WM) impairment. Objective: To investigate the brain activity in aMCI patients during WM tasks with the functional near-infrared spectroscopy (fNIRS) technique, as well as explore the association between brain activity and cognitive function in multiple domains. Methods: This study is a case-control study of 54 aMCI patients and 33 cognitively healthy elderly (NC). All participants underwent neuropsychological assessments. fNIRS was applied to examine the brain activation during the WM task. Multivariable linear regression analysis was applied to evaluate associations between brain activation and cognitive function in multiple domains. Results: Compared to NC subjects, aMCI patients had lower activation in the bilateral prefrontal, parietal, and occipital cortex during the WM task. Additionally, activation in the left prefrontal, bilateral parietal, and occipital cortex during the encoding and maintenance phase was positively associated with memory function. During memory retrieval, higher activity in the left prefrontal, parietal, and occipital cortex were correlated with higher memory scores. Besides, a positive association also formed between attention function and the activation in the left prefrontal, parietal, and occipital cortex during the WM task. Conclusion: These findings demonstrated that reduced activation in the prefrontal, parietal and occipital cortex during WM might reflect the risk of cognitive impairment, especially memory and attention function in aMCI patients. Given the brain activation visualization, fNIRS may be a convenient and alternative tool for screening the risk of Alzheimer’s disease.
RETRACTED: Hybrid deep learning model for efficient prediction of telecom data using EMF radiation
Authors: Karthiga S. , Abirami A.M.
Published: December 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
T-S fuzzy model identification based on an improved interval type-2 fuzzy c-regression model
Authors: Shi Jianzhong
Published: December 2022

Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can’t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy.
Machine learning approach for corona virus disease extrapolation: A case study
Authors: Kadali Dileep Kumar , Mohan R.N.V. Jagan , Padhy Neelamadhab , Satapathy Suresh , Salimath Nagesh , Sah Rahul Deo
Published: December 2022

Supervised/unsupervised machine learning processes are a prevalent method in the field of Data Mining and Big Data. Corona Virus disease assessment using COVID-19 health data has recently exposed the potential application area for these methods. This study classifies significant propensities in a variety of monitored unsupervised machine learning of K-Means Cluster procedures and their function and use for disease performance assessment. In this, we proposed structural risk minimization means that a number of issues affect the classification efficiency that including changing training data as the characteristics of the input space, the natural environment, and the structure of the classification and the learning process. The three problems mentioned above improve the broad perspective of the trajectory cluster data prediction experimental coronavirus to control linear classification capability and to issue clues to each individual. K-Means Clustering is an effective way to calculate the built-in of coronavirus data. It is to separate unknown variables in the database for the disease detection process using a hyperplane. This virus can reduce the proposed programming model for K-means, map data with the help of hyperplane using a distance-based nearest neighbor classification by classifying subgroups of patient records into inputs. The linear regression and logistic regression for coronavirus data can provide valuation, and tracing the disease credentials is trial.