bluuebunny/crossref_metadata_2025_split huggingface dataset:
Circular current in a one-dimensional Hubbard quasi-periodic Su–Schrieffer–Heeger ring
Authors: Roy Souvik , Maiti Santanu K 
Published: May 2023

Abstract
In this work, we investigate the behavior of interacting electrons in a Su–Schrieffer–Heeger quantum ring, threaded by an Aharonov–Bohm (AB) flux φ, within a tight-binding framework. The site energies of the ring follow the Aubry-Andre-Harper (AAH) pattern, and, depending on the specific arrangement of neighboring site energies two different configurations, namely, non-staggered and staggered, are taken into account. The electron–electron (e–e) interaction is incorporated through the well-known Hubbard form and the results are computed within the mean-field (MF) approximation. Due to AB flux φ, a non-decaying charge current is established in the ring, and its characteristics are critically studied in terms of the Hubbard interaction, AAH modulation, and hopping dimerization. Several unusual phenomena are observed under different input conditions, that might be useful to analyze the properties of interacting electrons in similar kinds of other fascinating quasi-crystals in the presence of additional correlation in hopping integrals. A comparison between exact and MF results is given, for the sake of completeness of our analysis.
Decay of correlations for critically intermittent systems
Authors: Kalle Charlene , Zeegers Benthen 
Published: May 2023

Abstract
For a family of random intermittent dynamical systems with a superattracting fixed point we prove that a phase transition occurs for the existence of an absolutely continuous invariant probability measure depending on the randomness parameters and the orders of the maps at the superattracting fixed point. In case the systems have an absolutely continuous invariant probability measure, we show that the systems are mixing and that correlations decay polynomially even though some of the deterministic maps present in the system have exponential decay of correlations. This contrasts other known results, where a system maintains exponential decay of correlations under stochastic perturbations of a deterministic map with exponential rate of mixing, see e.g. Baladi and Viana (1996 Ann. Sci. l’Ecole Norm. Sup.
29 483–517).
Statistical description of galaxy clusters in Finzi model of gravity
Authors: Khanday Abdul W , Upadhyay Sudhaker , Iqbal Naseer , Ganai Prince A 
Published: May 2023

Abstract
We exploit a new theory of gravity proposed by Finzi, which gives stronger interaction at large scales, to study the thermodynamic description of galaxy clusters. We employ a statistical model to deduce various thermodynamics equations of state. In addition, we analyze the behavior of clustering parameter in comparison to its standard (Newtonian) counterpart. The general distribution function and its behavior with varying strength of clustering parameter are also studied. The possibility of phase transition is investigated and it is observed that a phase transition is possible though hierarchically. We also analyze the model by comparing the results with data available through SDSS-III, and obtain the parameters involved.
Volatile threshold switching memristor: An emerging enabler in the AIoT era
Authors: Zuo Wenbin , Zhu Qihang , Fu Yuyang , Zhang Yu , Wan Tianqing , Li Yi , Xu Ming , Miao Xiangshui 
Published: May 2023

Abstract
With rapid advancement and deep integration of artificial intelligence and the internet-of-things, artificial intelligence of things has emerged as a promising technology changing people’s daily life. Massive growth of data generated from the devices challenges the AIoT systems from information collection, storage, processing and communication. In the review, we introduce volatile threshold switching memristors, which can be roughly classified into three types: metallic conductive filament-based TS devices, amorphous chalcogenide-based ovonic threshold switching devices, and metal-insulator transition based TS devices. They play important roles in high-density storage, energy efficient computing and hardware security for AIoT systems. Firstly, a brief introduction is exhibited to describe the categories (materials and characteristics) of volatile TS devices. And then, switching mechanisms of the three types of TS devices are discussed and systematically summarized. After that, attention is focused on the applications in 3D cross-point memory technology with high storage-density, efficient neuromorphic computing, hardware security (true random number generators and physical unclonable functions), and others (steep subthreshold slope transistor, logic devices, etc.). Finally, the major challenges and future outlook of volatile threshold switching memristors are presented.
Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images
Authors: Sujatha K , Bhavani N.P.G. , Kirubakaran D. , Janaki N. , George G.Victo Sudha , Cao Su-Qun , Kalaivani A. 
Published: May 2023

Abstract
With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.
Lung Image Classification Based On Long-Short Term Memory recurrent neural network
Authors: Shaker Ahmed Maher , Xiong Shengwu 
Published: May 2023

Abstract
Due to various factors such as complicated lung imaging and rapidly growing amount of data, the task for imaging technicians is arduous. The emergence of artificial intelligence assisted diagnosis technology comes just in time.To effectively classify lung partial images and alleviate the burden of medical application, a deep learning method based on attention mechanism is therefore developed. The proposed model uses deep learning as the basic integrates Long-Shot term memory (LSTM) the recurrent neural network (RNN). Technology for lung imaging diagnosis based on artificial intelligence has evolved through time from combined diagnosis of multiple diseases to the diagnosis of a single specific disease. The suggested network’s overall classification accuracy, according to experiments, is 95.93%, which is 1.019% greater than that of the deep learning basic network. It also outperforms the VGG16 and VGG19 networks in terms of classification performance. Finally, the benefits and drawbacks of the suggested algorithm are explored, as well as the future development path.
Prediction of photovoltaic power generation based on LSTM and transfer learning digital twin
Authors: Yang Heng , Wang Weisong 
Published: May 2023

Abstract
This research offers a digital twin model for solar power production power prediction based on long short term memory network (LSTM), and then applies this model to other models with limited operational time and inadequate data through transfer learning. The prediction for the solar system’s electrical output. Due to the effect of sun irradiation, temperature, and other random elements, photovoltaic power output is very intermittent and fluctuating, making it impossible to anticipate photovoltaic power with precision. Synchronization and real-time updating of physical entities, thereby obtaining more accurate forecasting results than traditional forecasting methods, while utilizing knowledge learned from PV systems with sufficient historical data to assist PV systems with limited historical data in establishing a digital twin of power generation forecasting model, not only can obtain accurate prediction results but also save training time for the model. In this study, the PV historical data of three distinct sites from the open source websites of Queensland University and Shanxi Jinneng Clean Energy Company are used to validate the validity of the suggested technique.
Acceleration Factor Optimization of Gauss-Seidel Numerical Analysis for Power Flow Using FA
Authors: Yahyia Anwar A. , Abbas Adel A. , Jaber Aqeel S. 
Published: May 2023

Abstract
Load flow analysis is one of the essential ways used by electrical power engineers to achieve high system quality owing to the relevance of load flow in economic optimization, planning, and stability. This issue can be formulated as a set of nonlinear algebraic equations, which can be solved by iterative algorithms. Gauss-Seidel is one of the simple ways of determining load flow in a power system, with the acceleration factor being the most considerable modification when aiming at improving the accuracy of this method. This article reports the determination of the optimal acceleration factor using PSO and Firefly (FA) algorithms. The simulation was done in MATLAB using a 4-bus, 6-bus, and 9-bus of IEEE standard system for the determination of the optimal acceleration factor. The proposed methods were evaluated and benchmarked against the conventional value of the factor using the number of iterations as the fitness function. The outcome of the study showed the FA method as the most preferable method of identifying the optimum acceleration factor.
Modelling of an Efficient Electromagnetic Method for Underwater Wireless Communication
Authors: Laghari Waqas Ahmed , Sahar Tayyaba 
Published: May 2023

Abstract
This paper investigates the characteristics of brief-term variety in wireless communications in the underwater environment. Nowadays, they are extensively used in experimental research related to underwater communication networks and channel estimations for an efficient link. From the first perspective, the main research exploration enquiry depends upon increasing the distance and frequency range. In this research work, an electric current method focusing on RF wave propagation is chosen. This technique in the seawater is produced by a couple of electrodes sensed by two receipt electrodes. The method can be an option as the waves show less effective refraction and reflection effecting in deep and shallow water.
Multi-feature Extraction of Metal Cracks using Based on Wavelet Neural network
Authors: Yan Siqian , Yao Hua , Bian Haiyi 
Published: May 2023

Abstract
The traditional ultrasonic testing mostly uses a single feature, which induce the inaccuracy of the crack detection. This paper proposes a metal crack detection method based on multi-feature extraction. First, the characteristics of time-frequency information of the signal obtained by using wavelet packet transform is briefly introduced. Secondly, the designed wavelet neural network model consists of first layer with three neurons (wavelet packet transform of the ultrasonic echo signal, the peak and energy function), and the output layer with one neuron presenting recognition features to identify and analyze metal cracks. The experimental results show ultrasonic echo measurement with the improved method proposed model can effectively reduce the influence of white gaussian noise while significantly improving the recognition rate of metal cracks.
Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics
Authors: Geng Junzuo , Mokhtarzadeh Amir Ali , Fei Hongyan , Gao Han , Ding Nan , Kong Rongshuang , Zhou Jiayu 
Published: May 2023

Abstract
This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited adaptation to scenarios and susceptibility to environmental influences. Real-time kinematics (RTK) is a real-time dynamic observation technique based on carrier phase observations, which can provide real-time 3D positioning results of the measurement site in a specified coordinate system, and can be positioned with centimetre-level accuracy, which is suitable for It is suitable for outdoor positioning of mobile robots. In this paper, a dataset is studied to compare and analyse the advantages and disadvantages of different SLAMs in order to determine the most suitable robot positioning and navigation technique for autonomous indoor and outdoor mobility. These sensors were well calibrated and the composition was applied to the RK3588. The results show good performance in indoor-outdoor switching scenarios, obtaining a mapping technique that cannot be achieved with a single visual SLAM, effectively improving the ability to adapt to multiple scenarios and enhancing the operational range of the transportable robot.
Eddy Current Testing Methods and Design for Pipeline Inspection System: A Review
Authors: Mohamad Ahmad Joraimee , Ali Kharudin , Rifai Damhuji , Salleh Zulfikri , Othman Ahmad Anwar Zikri 
Published: May 2023

Abstract
Non-destructive testing and complex systems integration are commonly used in transportation, aircraft, automotive, manufacturing, petrochemical, and defence. Eddy current testing (ECT) is a common non-destructive evaluation technique, especially in the coating, nuclear, and oil and gas industries. However, the eddy current testing method must continue to focus on the probe’s construction and application. It ascribed noisy data to inadequate detection and a severe false alarm problem. Nonetheless, only few academics have elaborated on the intelligent ECT crack detection method. This study provides an overview of the eddy current testing technique and the probe structure design elements that influence fracture detection accuracy. The first section describes the Eddy current testing by encircling coil system for a differential probe (ECDP) and an encircling coil for an absolute probe design (ECDP). The explanations focus mostly on the probe’s creation and detection in Rotating electromagnetic field eddy current (RoFEC). The advantages and disadvantages of eddy current testing surrounding probes are analysed. In addition, a summary of prior studies investigating testing samples of ferromagnetic pipe, steel rod, and wire rope is presented. The second segment focuses on Eddy current testing employing a Giant Magnetoresistance (GMR) sensor system, with a particular emphasis on sensor arrays, GMR, and conventional eddy current coils for crack identification. Finally, a comprehensive overview of eddy current testing methods and design for non-destructive pipeline inspection systems is presented.
Diverging climate response of corn yield and carbon use efficiency across the U.S.
Authors: Yu Shuo , Falco Nicola , Patel Nivedita , Wu Yuxin , Wainwright Haruko 
Published: May 2023

Abstract
In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.
Efficacy of phytohormones producing isolates on the growth of two stem-cutting robusta coffee (Coffea canephora)
Authors: Firdaus N K , Oktafiyanto M F , Sasmita K D , Pranowo D 
Published: May 2023

Abstract
The propagation techniques of robusta coffee by cutting can be accelerated by the application of plant growth-promoting hormones. This study is to determine the efficacy of phytohormone-producing isolates on the growth of two-segment cutting of Robusta coffee. The study was conducted using a non-factorial completely randomized design with 2 replications. The treatments consist of control, synthetic IBA (Rooton-f) with three dilution concentrations, 4 isolates (Rhodococcus qingshengii, Burkholderia ambifaria, and Bacillus wiedmannii; fungi Aspergillus niger) with three dilution concentrations. Observed parameters included a percentage of live cuttings, percentage of cuttings sprouted, percentage of rooted cuttings, plant height, number of shoots, number of internodes, number of leaves, number of primary roots, primary root length, and primary root diameter. The results showed that the B. wiedmannii treatment had the best performance compared to other treatments because this isolate produced the highest number of primary roots (4.60) and primary root diameters (2.97 mm) compared to other treatments including control and Rooton-f. This is related to its ability to produce phytohormones that can stimulate the root growth of coffee robusta cuttings.
Performance of Five Superior Varieties of Rice in Specific Agroecology of Rice Land in Jombang Regency
Authors: Sudaryono T. , Prahardini P. E. R. , Purnama S. , Fidiyawati E. 
Published: May 2023

Abstract
Rice is a food crop that produces rice as a staple food of the community and plays an important role in the country’s economy. East java as one of the provinces of rice production centers has decreased productivity. In 2020, rice productivity in east java amounted to 5.7 tons.ha−1. In 2021, it fell to 5.6 tons.ha−1. Efforts to increase rice productivity must continue to be carried out. Therefore, research was conducted on 5 VUB (new high-yield varieties) on the agroecology of rice fields specific to the Jombang regency. The reach was conducted participatory involving farmers and field officers. The research was conducted in Ngrandulor village, Peterongan District, Jombang Regency from April to August 2021 using a random group design and five replications. The treatment consists of 5 varieties of rice, namely Inpari 32, Inpari 42, Inpari 45, Inpari Nutrizinc, and Pamelen. Vegetative plant appearance, mainly plants height, Inpari 45 shown highest markedly from other varieties. Other vegetative aspects of the plant, such as the number of samplings, the area of the flag leaves, and the leave under the flag leave between varieties showed no difference. In the generative aspect, namely the length of panicle, amount of grain content and amount of empty grain between varieties are real. Likewise, the weight of grain per panicle and the weight of grain per clump between varieties are markedly different. Based on research results show the productivity of 5 varieties ranged from 9 tons.ha−1, and 5 varieties of rice can be developed in the Jombang Regency.
Application of Oil Palm Empty Bunch Ash Nanoparticle on Epidermal Cell Thickness and Chlorophyll Content of the Soybean Leaf
Authors: Budiyanto G , Mulyono M , Bintania C , Aisyah S N 
Published: May 2023

Abstract
The utilization of oil palm empty bunch (OPEB) could be a powerful solution to manage such large volume of wastes produced from the oil palm plantation. Ash created from this waste could be used as soil ameliorant as well as additional agent for liquid fertilizer. Its form in nano size might increase the leaf absorption. Therefore, this study was aimed to investigate the effect of OPEB ash nanoparticle on epidermal cell thickness and chlorophyll content of soybean leaves. This study was carried out using completely randomized design to examine five levels of OPEB ash nanoparticle concentration, ranging from 0, 1.25, 2.5, 3.75, and 5%. Each concentration was repeated nine times. OPEB ash nanoparticle was applied firstly at 2 weeks-old plants and repeated weekly up to 5 times. Application OPEB ash nanoparticle was performed through foliar spray. The results showed that OPEB ash nanoparticle affect the soybean leaf physiology, particularly the structure and anatomy of its stomata. In addition, application of 0.125% OPEB ash nanoparticle increased the epidermal cell thickness. However, higher concentration of OPEB ash nanoparticle was found to inhibit the chlorophyll formation.
Frequency and Period of Stem Wounding Enhances Quality of Cassava-Mycorrhiza Association and Plant Yield
Authors: Astuti A , Mulyono  , Ma’rifah F 
Published: May 2023

Abstract
The main problem in cassava production is the low productivity, as often manifested in the limited number of tubers produced. It was expected that wounding treatments at different growth periods, in addition to mycorrhiza inoculation, would also have an effect on cassava production. This study was aimed to determine the effect of stem wounding frequency and period on quality of cassava-mycorrhiza symbiosis and cassava yield. This study was conducted using a single factor Randomized Complete Block Design (RCBD) consisting of four application methods: (A) no wounding; (B) 1 time wounding before planting; (C) 2 times wounding before and after planting; (D) 3 times wounding before and after planting. Each treatment was repeated three times (12 experimental units in total). The variables observed include (1) percentages of mycorrhiza infections and number of spores, (2) root growth and (3) cassava yield. The results showed that wounding frequency and period were found to be effective in increasing the cassava root surface, as well as increasing the mycorrhiza infection, which resulted in the increase of cassava yield (54.32 tonnes/ha). The wounding frequency of one to three times on cassava gave significant difference compared to control treatment. Three times wounding treatment prior to planting resulted in the highest cassava yield improvement of 54.05%.