bluuebunny/crossref_metadata_2025_split huggingface dataset:
A large scale study of portable sweat test sensor for accurate, non-invasive and rapid COVID-19 screening based on volatile compound marker detection
Authors: Thaveesangsakulthai Isaya , Chatdarong Kaywalee , Somboonna Naraporn , Pombubpa Nuttapon , Palaga Tanapat , Makmuang Sureerat , Wongravee Kanet , Hoven Voravee , Somboon Pakpum , Torvorapanit Pattama , Nhujak Thumnoon , Kulsing Chadin 
Published: August 2024

AbstractThis study established a novel infield sensing approach based on detection of the volatile compound markers in skin secretions. This was based on analysis of volatile compounds in axillary sweat samples collected from RT-PCR-proven Coronavirus disease 2019 (COVID-19) positive and negative populations using gas chromatography-mass spectrometry (GC–MS). The analysis proposed the possible markers of the monoaromatic compounds and ethyl hexyl acrylate. A portable photo ionization detector (PID) incorporated with the selective material towards the marker compounds was then developed with the pressurized injection approach. This provided the accuracy of 100% in the research phase (n = 125). The developed approach was then applied for screening of 2207 COVID-19 related cases covering the periods of the Alpha, Beta, Delta and Omicron variants of SARS-CoV-2 infection in Bangkok, Thailand. This offered the sensitivity, specificity and accuracy ranges of 92–99, 93–98 and 95–97%, respectively.
Generalising quantum imaginary time evolution to solve linear partial differential equations
Authors: Kumar Swagat , Wilmott Colin Michael 
Published: August 2024

AbstractThe quantum imaginary time evolution (QITE) methodology was developed to overcome a critical issue as regards non-unitarity in the implementation of imaginary time evolution on a quantum computer. QITE has since been used to approximate ground states of various physical systems. In this paper, we demonstrate a practical application of QITE as a quantum numerical solver for linear partial differential equations. Our algorithm takes inspiration from QITE in that the quantum state follows the same normalised trajectory in both algorithms. However, it is our QITE methodology’s ability to track the scale of the state vector over time that allows our algorithm to solve differential equations. We demonstrate our methodology with numerical simulations and use it to solve the heat equation in one and two dimensions using six and ten qubits, respectively.
Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering
Authors: Malik Saleem , Patro S Gopal Krishna , Mahanty Chandrakanta , Lasisi Ayodele , Al-sareji Osamah J. 
Published: August 2024

AbstractThe field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.
Involuntary motor responses are elicited both by rare sounds and rare pitch changes
Authors: Sabu Simily , Parmentier Fabrice B. R. , Horváth János 
Published: August 2024

AbstractUnpredictable deviations from an otherwise regular auditory sequence, as well as rare sounds following a period of silence, are detected automatically. Recent evidence suggests that the latter also elicit quick involuntary modulations of ongoing motor activity emerging as early as 100 ms following sound onset, which was attributed to supramodal processing. We explored such force modulations for both rare and deviant sounds. Participants (N = 29) pinched a force sensitive device and maintained a force of 1–2 N for periods of 1 min. Task-irrelevant tones were presented under two conditions. In the Rare condition, 4000 Hz tones were presented every 8-to-16 s. In the Roving condition, 4000 Hz and 2996 Hz tones were presented at rate of 1 s, with infrequent (p = 1/12) frequency changes. In the Rare condition, transient force modulations were observed with a significant increase at ~ 234 ms, and a decrease at ~ 350 ms. In the Roving condition with low frequency deviant tones, an increase in force was observed at ~ 277 ms followed by a decrease at ~ 413 ms. No significant modulations were observed during perception of high frequency deviants. These results suggest that both rare silence-breaking sounds and low-pitched deviants evoke automatic fluctuations of motor responses, which opens up the possibility that these force modulations are triggered by stimulus-specific change-detection processes.
Optimal sizing and operation of a hybrid energy systems via response surface methodology (RSM)
Authors: Moradzadeh Arash , Pourhossein Kazem , Ghorbanzadeh Amin , Nazari-Heris Morteza , Colak Ilhami , Muyeen S. M. 
Published: August 2024

AbstractHybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues related to energy management. In this paper, the Response Surface Methodology (RSM) is proposed for the optimal sizing of a Photovoltaic (PV) system in a HESs. The suggested procedure solves the optimization problem by considering the factors affecting PV output power about the environmental conditions of the HESs. Providing a mathematical model for each of the input parameters and the ability to assessment the sensitivity of each of the input variables are the most important advantages of the proposed technique. In this paper, the RSM provides the most optimal sizing related to the PV system by considering climatic and geographical factors in the study site, and technical and economic issues related to the HESs. The optimal model obtained is evaluated by the Analysis of Variance (ANOVA) evaluation method, which is one of the important techniques of statistical evaluation. It should be noted that the RSM technique can be utilized to optimize all components of any HES.
Biological comparisons between pre-eclampsia and placenta accreta spectrum
Authors: Bartels Helena C. , Hameed Sodiq , Young Constance , Nabhan Myriam , Downey Paul , Curran Kathleen M. , McCormack Janet , Fabre Aurelie , Kolch Walter , Zhernovkov Vadim , Brennan Donal J. 
Published: August 2024

AbstractPlacenta Accreta Spectrum (PAS) and pre-eclampsia are obstetric disorders associated with significant maternal morbidity and mortality. As pre-eclampsia is characterised by shallow placental invasion and PAS by abnormally deep invasion, we sought to compare the biological processes dysregulated in PAS to those reported in pre-eclampsia. We demonstrate that many of the processes contributing to unregulated placental invasion in PAS are the mirror findings of those previously reported in pre-eclampsia. These comparisons of PAS to a more common obstetric condition allow a greater understanding of the biological processes contributing to PAS, which remains a very rare obstetric complication.
Quantum coherence protection by utilizing hybrid noise
Authors: Feng Jiahui , Guo Tengtao , Zhou Yuxuan , Zhao Xinyu , Xia Yan 
Published: August 2024

Abstract
Noise is often considered as the biggest enemy of maintaining quantum coherence. However, in this paper, we show a scheme to protect quantum coherence by introducing extra noise. To be specific, we study an atom coupled to a single mode cavity (Jaynes–Cummings model) with two noises. One is from the cavity leakage, the other is from the stochastic atom-cavity coupling. Based on the non-Markovian dynamical equation, we show the quantum coherence can be protected by introducing the noise in the atom-cavity coupling. We study four different types of noises and show their performance on the coherence protection. We also analytically reveal the mechanism of the quantum coherence protection, namely the high frequency noise can freeze the dynamics thus protect coherence. Last but not least, a mixture of different types of noises (hybrid noise) is studied. We show the mixture can lower the performance of the coherence protection scheme and provide an explanation. We hope the results presented here may attract more attention on protecting quantum coherence by utilizing noise.
Bidirectional quantum teleportation in continuous variables
Authors: Nesterova E A , Korolev S B 
Published: August 2024

Abstract
We propose a bidirectional quantum teleportation (BQT) protocol in continuous variables. We use a cluster state in continuous variables as the main resource to realize this protocol. In the paper, we obtain a family of configurations of cluster states in continuous variables that can be used to realize the BQT protocol. From the whole family of configurations, we have chosen those that realize the protocol with the smallest possible error.
Reconfigurable Digital Twin from the Perspective of Smart Manufacturing Systems in Industry 4.0/5.0
Authors: Mo Guangyu , Sun Yuan , Huang Sihan , Gao Feng , Huang Jintang , Lai Lijuan , Wang Guoxin , Yan Yan 
Published: August 2024

Abstract
In industry 4.0/5.0, Smart manufacturing systems are typically complex systems that can be adjusted to deal with demand fluctuation through reconfigurable software/hardware. The emergence of digital twin as enabler technology can promote the operation efficiency of smart manufacturing systems. However, the modelling processes of digital twin of smart manufacturing systems is inefficient due to their complicated structures in general. Besides, the frequent reconfiguration of smart manufacturing systems and the differentiation among smart manufacturing systems will increase the burden of digital twin modelling. Therefore, a reconfigurable modelling method for digital twin of smart manufacturing systems is proposed in this paper. First, a reconfigurable modelling framework based on open architecture is proposed to provide basic model scenario for digital twin of smart manufacturing systems. Second, the reconfiguration of digital twin is elaborated from three dimensions, including reconfigurable virtual model, reconfigurable mapping, and reconfigurable scene. Finally, a case study is given to demonstrate the effectiveness of the proposed method.
Research on Underwater Imaging Technology Based on Polarization Enhancement
Authors: Liu Jiahao , Cao Shouhu , Zhang Di , Chen Yujie , Kong Fang 
Published: August 2024

Abstract
In response to the problem of unclear representation of underwater polarization image information, this paper first improves the pulse coupled neural network (PCNN) model. The polarization image is used as model input, and ultimately obtaining the target polarization degree feature representation (e-DOP) image. The polarization angle image and polarization degree feature representation image are used as the decomposition source images for non-subsampled contourlet transform (NSCT) decomposition. The decomposed high-frequency sub bands are used as inputs to the PCNN model to obtain the ignition map, and the high-frequency coefficients are computed by analyzing their regional energy distribution. The low-frequency parts are fused using principal component analysis. Finally, the final resultant image is obtained by NSCT inverse transformation. The outcomes of the experiment demonstrate that the image of fusion obtained through the method in this article has better visual effects, more prominent detail features, and effectively improves the contrast between the target and the background.
Lateral and Longitudinal Shared Control Strategy of Human-Machine Based on Game-Theoretic
Authors: Ding Wenlong , Chen Xiang , Zhao Wanzhong , Wang Chunyan 
Published: August 2024

Abstract
Human-machine shared driving has become a research focus in the field of autonomous driving. In this paper, we propose a lateral and longitudinal shared control strategy of human-machine games. Firstly, a model of the driver and machine in shared driving mode is established. Then, game theory is employed to describe the coupled interaction between human and machine, and an optimal human-machine control strategy based on game theory is designed. Finally, the results of joint simulations and driver-in-the-loop experiments demonstrate that the proposed shared control strategy improves performance in terms of trajectory tracking, driver burden and vehicle stability.
MAIR-Net: a sparse-view CT reconstruction network based on a combination of mixed attention and iterative optimization learning
Authors: Guo Tong , Liu Yi , Zhang Pengcheng , Liu Yu , Gui Zhiguo 
Published: August 2024

Abstract
Sparse-view computed tomography (CT) is one of the main
means to reduce radiation risk. When the projection data is highly
undersampled, the reconstructed CT image may suffer from serious
stripe artifacts and structural information loss. In this paper, we
propose a sparse-view CT reconstruction network architecture
combining mixed attention (MA) and an iterative reconstruction
strategy, called MAIR-Net. Firstly, the approach expands the
proximal gradient descent into the neural network and uses an
initial value enhancement module between the gradient descent module
and the proximal mapping module. The aim is to enhance the flow of
detailed information between different layers, fully retain image
details, and improve the network convergence speed. Secondly, the
mixed attention module (MAM) is introduced into the reconstruction
process as a regularization term. It adaptively fuses local and
non-local features of the image, which are used to reduce the
over-smoothing of the reconstructed image and fully retain the
details of the reconstructed image, respectively. Experimental
results showed that the proposed method can better retain the
details of the reconstructed image and improve the quality of the
reconstructed image while inhibiting the sparse angle artifacts of
the CT reconstructed image.
Study of physical and chemical characteristics of milling rice with several degrees of milling
Authors: Zakky Mochamad , Ahmad Usman , Subrata IDM , Mardison S 
Published: August 2024

Abstract
Rice is the staple food for most of the Indonesian people. Along with the increase in population and living standards in Indonesia, demand for rice with high quality is increasing, especially in big cities. Rice quality is a combination of physical and chemical characteristics required for consumption. This study aimed to study physical and chemical characteristics by its quality and nutritional composition of milled rice with several degrees of milling. The material used was the Ciherang variety, which was milled with varying milling times (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16, 22, and 30 seconds). The collected data in this research were chemical characteristics obtained from proximate tests and physical attributes in the form of rice grain size. The results of the study showed that the physical characteristics were a milling degree of 10.21% with a duration of 30 seconds, an average width of rice grains of 2.33 ± 0.11 mm, a white rice grain length of 7.32 ± 0.34 mm, which was classified as very long, a roundness of 0.32 ± 0.02 which was classified as slender and rice grain shape (L/W) averaged 3.15 ± 0.19, categorized as slender rice grain shape. Chemical characteristics in the form of carbohydrate proportions increased with milling time, namely 74.80% in brown rice to 75.95%, fat proportions decreased with milling time from 2.52% in brown rice to 0.62%, and protein proportions decreased from 11.65% to 10.43% of the brown rice is milled for up to 30 seconds.
Impact of residues of chicken manure and oil palm shell biochar on water holding capacity and yield of peanuts in ultisol
Authors: Junedi H , Ermadani  , Arsyad AR , Zurhalena  
Published: August 2024

Abstract
One of the problems in the development of food crops is the limited availability of fertile land so that the choice is only for less fertile soil, namely Ultisol, which are very widely available in Indonesia. One of the problems is the low organic matter content of the soil so that the ability to hold water in Ultisol is low. The addition of chicken manure and oil palm shell biochar separately or together has been shown to increase the ability of Ultisols to hold water and yield peanuts in the first growing season. The aim of the study was to see the effects of chicken manure and biochar residues in the second growing season. The study was conducted in the Teaching and Research Farm of the Faculty of Agriculture, University of Jambi for 6 months, using a Randomized Block Design (RBD) with 6 treatments, namely: P0 = without manure and biochar; P1 = 10 tons ha−1 manure; P2 = 7.50 tons ha−1 manure and 2.50 tons ha−1 biochar; P3 = 5 tons ha−1 manure and 5 tons ha−1 palm shell biochar, P4 = 2.50 tons ha−1 chicken manure and 7.50 tons ha−1 biochar ; P5 =10 tons ha−1 biochar. The results showed that the application of a combination of chicken manure and oil palm shell biochar was still able to maintain soil water content and peanut yield in the second growing season.
Growth estimation of leaf vegetable plants using camera image analysis
Authors: Suharto YB , Suhardiyanto H , Susila AD , Supriyanto  
Published: August 2024

Abstract
Fresh weight and leaf canopy area of vegetable plants are the main parameters of plant growth in determining productivity and yield quality, affecting consumer acceptance. Visual observations and manual measurements are time-consuming, labor-intensive, and subject to high errors. Plant phenotyping technology can be applied to measure physiological and growth conditions and predict plant productivity non-destructively. The objective of the study was to estimate the canopy area and fresh weight of leaf vegetable plants non-destructively using camera image analysis. Pakcoy mustard and romaine lettuce are grown hydroponically in a greenhouse. Plant images were taken using a digital web camera installed on a photo box set with dimensions of 90cm×60cm×90cm (l×w×h). One hundred images of each pakcoy and romaine lettuce plant were analyzed using image processing to see the profile of the relationship between the canopy area and the fresh weight of vegetable plants. Camera image analysis can describe leaf vegetable plants’ growth profile in greenhouses. Canopy area and plant fresh weight correlated well to form a quadratic equation with R2 0.96 for pakcoy and R2 0.98 for romaine lettuce. The plant growth estimation model built using the results of camera image analysis can estimate the parameters of the canopy area and fresh weight of leaf vegetable plants with high accuracy.
Approach maximum likelihood classification and cellular automata markov chain model for land use/land cover change prediction in Nagan Raya Country, Indonesia
Authors: Yuliani  , Ramli I , Joni  
Published: August 2024

Abstract
Forest is one of the land cover classes found in Nagan Raya District. Deforestation can contribute to a decrease in carbon emission absorption potential. Land cover change can affect a region’s policy in managing a good environment. This study aims to determine land cover change for the period 2007-2023 and predict land cover in 2035. Land cover change analysis uses Maximum Likelihood Classification (MLC) and Cellular Automata Markov Chain to predict land cover in 2035. Land cover was classified into 8 (eight) groups using kappa coefficient estimation. The kappa result for land cover in 2007 was 77%. The kappa result for land cover in 2015 was 84% and the kappa result for land cover in 2023 was 82%. The dominating land cover in 2023 is forest with the area of 169.555,44 Ha. Forest deforestation in 2007-2023 amounted to 19.926 Ha (10,5%) and the addition of farmfield is 9.428 Ha (9,4%). The validation of the 2035 model is 0,79, in which the declining land cover classes are forests 12.848 Ha (7,6%), wetlands 1.518 Ha (10,8%), rice field 131,2 Ha (2%), open land 4.935 Ha (38,1%) and water bodies 164,2 Ha (4,2%). While the increasing land cover classes are settlement 1.502 Ha (20,1%) and farmfield 20.709,9 Ha (18,9%). The results of this study can support policy decision-making and regional planning systems in Nagan Raya District.
Recognition of defect types of Arabica coffee beans using image processing
Authors: Ahmad Usman , Nurrahman Muhammad Ilham 
Published: August 2024

Abstract
Coffee is one of the most popular commodities in the world and Indonesia is a large coffee producer. Every year coffee production in Indonesia reaches more than 600,000 tons and some of them are exported. However, most coffee bean grading is still done manually, and this work requires a lot of time with low consistency. This study aims to study the quality parameters of Arabica coffee beans and the use of image processing to distinguish types of defects in green coffee beans. Samples were grouped into normal beans, shelled beans, deformed, discolored, hollow beans and small beans. Each group consisted of 100 green coffee bean samples. Image acquisition is carried out on white paper from a short distance using USB camera. Image processing program is made to obtain several image parameters from objects such as area, perimeter, height and width of beans and color values. Recognition results using image processing sequentially were 77%, 43%, 41%, 90%, 68%, and 84% for normal beans hollow beans, deformed beans, discolored beans, skinned beans, and small beans.