bluuebunny/crossref_metadata_2025_split huggingface dataset:
Isospin-conserving hadronic decay of the Ds1(2460) into Dsπ+π−
Authors: Tang Meng-Na , Lin Yong-Hui , Guo Feng-Kun , Hanhart Christoph , Meißner Ulf-G. 
Published: April 2023

AbstractThe internal structure of the charm-strange mesonsDs0*(2317)andDs1(2460) are the subject of intensive studies. Their widths are small because they decay dominantly through isospin-breaking hadronic channelsDs0*(2317)+→Ds+π0andDs1(2460)+→Ds*+π0. TheDs1(2460) can also decay into the hadronic final statesDs+ππ, conserving isospin. In that case there is, however, a strong suppression from phase space. We study the transitionDs1(2460)+→Ds+π+π−in the scenario that theDs1(2460) is aD*Khadronic molecule. Theππfinal state interaction is taken into account through dispersion relations. We find that the ratio of the partial widths of theΓ(Ds1(2460)+→Ds+π+π−)/Γ(Ds1(2460)+→Ds*+π0)obtained in the molecular picture is consistent with the existing experimental measurement. More interestingly, we demonstrate that theπ+π−invariant mass distribution shows a double bump structure, which can be used to disentangle the hadronic molecular picture from the compact state picture for theDs1(2460)+. Predictions on theBs10→Bs0π+π−are also made.
Fluctuation dominated phase ordering in coarse-grained depth models: domain wall structures, extreme values and coarsening
Authors: Das Arghya , Barma Mustansir 
Published: April 2023

Abstract
Models of particles driven by a one-dimensional fluctuating surface are known to exhibit fluctuation dominated phase ordering (FDPO), in which both the order and fluctuations appear on macroscopic scales. Highly dynamic and macroscopically broad interfacial regions, each composed of many domain walls, appear between macroscopically ordered regions and consequently the scaled correlation function violates the Porod law. We focus on two essential quantities which together quantify the unique characteristics of FDPO, namely the total number of domain walls and the length of the largest ordered domain. We present results in the context of coarse-grained depth (CD) models, both in steady state and while coarsening. Analytic arguments supported by numerical simulations show that even though domain wall number fluctuations are very strong, the associated variance remains constant in time during coarsening. Further, the length of the largest cluster grows as a power law with significant multiplicative logarithms which involve both the time and system size. In addition, we identify corrections to the leading power law scaling in several quantities in the coarsening regime. We also study a generalisation of the CD model in which the domain wall density is controlled by a fugacity and show that it maps on to the truncated inverse distance squared Ising model. The generalised model shows a mixed order phase transition, with the regular CD model (which exhibits FDPO) corresponding to the critical point.
Multicomponent gauge-Higgs models with discrete Abelian gauge groups
Authors: Bracci Testasecca Giacomo , Pelissetto Andrea 
Published: April 2023

Abstract
We consider a variant of the charge-Q compact Abelian-Higgs model, in which an N

f
-dimensional complex vector is coupled with an Abelian

Z

q

gauge field. For

N
f

=
2

and Q = 1 we observe several transition lines that belong to the O(4), O(3), and O(2) vector universality classes, depending on the symmetry breaking pattern at the transition. The universality class is independent of q as long as

q
⩾
3

. The universality class of the transition is uniquely determined by the behavior of the scalar fields; gauge fields do not play any role. We also investigate the system for

N
f

=
15

and Q = 2. In the presence of U(1) gauge fields, the system undergoes transitions associated with charged fixed points of the Abelian-Higgs field theory. These continuous transitions turn into first-order ones when the U(1) gauge fields are replaced by the discrete

Z

q

fields: in the present compact model charged transitions appear to be very sensitive to the nature of the gauge fields.
Performance analysis of Polar Codes in a Visible Light Communication System
Authors: Vaz Aldrin Claytus , Hegde Navya Thirumaleshwar , Shetty Akhil Appu 
Published: April 2023

Abstract
One of the vital entity of any communication systems is Channel coding, which leads to the design of high performance codes for future wireless systems having low complexity encoder and decoder design. These systems will have a requirement of operating in highly reliable conditions, maximum throughput, and low and high code rates and to work with short and long information messages. Polar codes are one of the promising error correcting channel codes that can be used in these situations to obtain maximum throughput and coding gain in a communication systems because of their capacity approaching performance and finds interests in Satellite communication and 4G/5G services. These codes uses the concept of channel polarization to be constructed. The work proposed in this paper focusses on the Bit Error Rate evaluation and analysis of the Polar codes using traditional approach and Deep learning approach. The feedforward deep learning networks using different activation functions were used for the Deep learning approach. The Successive Cancellation algorithm and its variant using List decoding were used as traditional decoding methodology. The results obtained using Deep learning approach were satisfactory and was matching as per the traditional decoding.
Performance Comparison of Various Neural Networks for Speech Recognition
Authors: Sridhar C. , Kanhe Aniruddha 
Published: April 2023

Abstract
Speech recognition is a method where an audio signal is translated into text, words, or commands and also tells how the speech is recognized. Recently, many deep learning models have been adopted for automatic speech recognition and proved more effective than traditional machine learning methods like Artificial Neural Networks(ANN). This work examines the efficient learning architectures of features by different deep neural networks. In this paper, five neural network models, namely, CNN, LSTM, Bi-LSTM, GRU, and CONV-LSTM, for the comparative study. We trained the networks using Audio MNIST dataset for three different iterations and evaluated them based on performance metrics. Experimentally, CNN and Conv-LSTM network model consistently offers the best performance based on MFCC Features.
An approach for improving Optical Character Recognition using Contrast enhancement technique
Authors: Shine Nithin K , Bhutani Gariman , Keerthana Tamatapu Sai , Rohith G 
Published: April 2023

Abstract
Digital cameras and mobile image capture of documents are two examples of new developments in the fields of optical character recognition and text recognition. Scans of text or text photographic images and even natural photography results can be distorted to the point where OCR digitization is inaccurate. It offers a unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.
A review on techniques for improving mimo antenna performance in smart 5g devices
Authors: Kanisha R K Rabin , Rimmya C , Madhan M Ganesh 
Published: April 2023

Abstract
This paper presents the state-of-the-art literature on Multiple Input Multiple Output (MIMO) antennas for 5G systems. The degree of freedom offered by MIMO as a result of increasing transmitter and receiver elements has raised serious concern towards the impact due to mutual coupling between the adjacent elements in the MIMO antenna. Hence, this paper summarizes the various measures for suppressing mutual coupling between MIMO antenna elements. It also reports the methods for enhancing the bandwidth at the sub 6 GHz band by the deployment of different radiating structures and their influence over the crucial MIMO parameters. A study on the ways and means of handling the propagation losses at mm-wave frequencies by proper choice of PCB for fabrication is also discussed.
A Compact MIMO T Slot Microstrip Antenna Design for K-band Applications
Authors: Nishandhi S , Kavitha K , Gayathri M 
Published: April 2023

Abstract
In this paper, two compact T-Slot Microstrip antennas have been proposed, designed, and presented using design equations. Each antenna design has the same dimensions and characteristics. High-frequency structure simulator has been used for the proposed design and output has been verified. FR-4 Substrate which is having 1.6mm thickness is used to mount the prototype. The patch operates for 25.75 GHz resonant frequencies and T-Shaped slot has been designed for K-Band applications. In this work, 50 x 40 mm2 measurements is used. The antenna designed is having perfect radiation patterns with good VSWR, Return loss, and Radiation efficient achieved.
Design and Performance prediction of Liquid based Tunable Cylindrical Dielectric Resonator Antenna
Authors: Dhanush R , Kumar Kunde Santhosh , Madhan M Ganesh 
Published: April 2023

Abstract
In this paper, we report a liquid-based tunable Cylindrical Dielectric Resonator Antenna (CDRA), whose resonance is varied by loading the cavity installed in the CDRA with different volumes of liquid. The tunability of the CDRA is investigated by loading the cavity with two different types of liquids, one with lower permittivity (i.e.) oil and the other with higher permittivity (i.e.) water. CDRA loaded with oil is found to be tunable over a small frequency range of 32MHz (5.76GHz to 5.728GHz), whereas the change in water volume in the cavity provides a wide tuning range of 452MHz (5.76GHz to 5.308GHz). Further, the antenna gain is found to be almost constant, around 5.1 dB, for oil-based CDRA. In a water-filled cavity, the gain is observed to reduce by 1dB as the volume is increased from 0 to 100%.
Reduction of Near-End and Far - End Crosstalk in Microwave and Millimetre Wave Band of Parallel Transmission Lines using Meander Shaped DMS
Authors: Gobinath A , Kumar N Suresh , Rajeswari P 
Published: April 2023

Abstract
This research proposes a unique design that makes use of a defective microstrip topology to decrease electromagnetic coupling between parallel high speed interconnects. The performance of this new microstrip construction with defects is evaluated using near-end crosstalk and far-end crosstalk. Serpentine shaped defected microstrip is introduced in one parallel of high speed interconnects and its performance is also compared the existing structure. In this research, the proposed defected microstrip structure (DMS) is simulated and compared with a conventional microstrip structure using Ansoft HFSS software which employs the Finite Element Method (FEM). Simulation results indicate that the DMS design effectively reduces crosstalk in comparison to the previous structure. It reduces more than 5dB near end crosstalk and more than 3dB far end crosstalk compared to conventional model.
Analysis of Three-Stage LNA Architecture for CMOS RF Front End
Authors: Sathya R , Ambulker Sunanda 
Published: April 2023

Abstract
Wireless communication continues to lead the world. The term “wireless” significantly influences everything from telephone to satellite communication. Recent years have seen the need for appropriately built Low Noise Amplifiers (LNAs) in receiver front end because of the modem wireless communication in broadcast and microwave radio. In this paper a high performance LNA for the radio frequency range is presented. To achieve high gain a three stage LNA with each stage as Cascode stage is considered here. The simulation shows that the presented LNA has a gain of 26.12 dB and a Noise Figure (NF) of 2.6 dB. The achieved output and input returns losses are -8.4 dB and -11.6 dB respectively.
A Deep Learning approach to 3D Viewing of MRI/CT images in Augmented Reality
Authors: Paul Dewanshi , Patel Ankit Kumar , Rohith G 
Published: April 2023

Abstract
CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) are both used to diagnose internal damage and deformities of the organs. These are 3D data so doctors diagnose these scans by splitting them into many 2D slices and analyse each slices for detecting disease. This is a hard and time-consuming task to accomplish. Experts are enough trained to analyse this data for interpreting insights. But when they try to explain vulnerable situations to their patients, these scans are not very readable and understandable for them. Thus, it scares them a lot when they are asked to take these scans for diagnosis. Thus, an approach is given to viewing these scans in AR (augmented reality), making them available to view via their smartphones. The idea is to build an application that can take CT and MRI scans as input from “.stl” files and help to view them in AR with plane detection for mesh to be rendered and placed properly. This also reduces the task of storing these scans in bulk. Instead, one can just send data to their patients and store it as records on their phones. Whenever necessary, one can visualise it on the spot.
LSTM and GRU based Accurate Water Quality Prediction for Smart Aquaculture
Authors: Rahul Gandh D , Rasheed Abdul Haq K P , Harigovindan V P , Bhide Amrtha 
Published: April 2023

AbstractThe fast and precise water quality prediction in smart aquaculture can assist the farmers in taking corrective actions before the ecological environment deteriorates. However, the empirical methods take high computation time and provide low accuracy in water quality prediction due to the non-linear and dynamic nature of water quality parameters. This research work proposes long short-term memory (LSTM) and gated recurrent unit (GRU) deep learning recurrent neural network (DL-RNN) models for aquaculture water quality prediction (A-WQP). This work also presents an extensive study about the impact of hyper-parameters (hp) on the performance of the proposed DL-RNN models using two different water quality datasets. The experimental evaluations show that for the optimal sets of hyper-parameters, the proposed DL models offer superior prediction accuracy and computation efficiency.
Machine learning and IoT-based smart farming for enhancing the crop yield
Authors: Sundaresan S. , Daniel Johnson S. , Mani Bharathy V. , Mohan Pavan Kumar P. , Surendar M. 
Published: April 2023

Abstract
The lack of favourable atmospheric conditions leads to the loss of many crops each year. In India alone, over 11 billion dollars are lost. By combining IoT and machine learning technologies, this team has created a system that integrates agriculture’s three primary operations: crop selection, autonomous watering, and fertiliser suggestion. The following crops—Apple, Rice, Maize, Grape, Banana, Orange, Cotton, and Coffee—were considered in the study. Three systems are covered in the paper: The crop recommendation system employs machine learning to examine factors including nitrogen (N), phosphorous (P), potassium (K), pH, and weather before recommending a crop. The crop type and the current levels of soil nutrients are the two main determinants on which the fertiliser recommendation method bases its recommendation. When employing an automatic irrigation system, the crop is irrigated automatically while taking current soil moisture levels and weather forecasts into consideration. This paper attempted to implement the mentioned systems. The paper discusses the successes of the crop recommendation system, the automatic watering system, and the fertiliser recommendation system. In this paper, we report the results of simulations of the mentioned systems.
A study on flow field characteristics of a self-propelled robot fish approaching static obstacles based on artificial lateral line
Authors: Xie Ou , Sun Zhaoguang , Shen Can 
Published: April 2023

Abstract
To perceive the static obstacles in still water, the flow field characteristics of a self-propelled robot fish approaching static obstacles were studied based on artificial lateral line (ALL). The pressure distribution on the fish body surface was calculated with different separation between the robot fish and the obstacle boundary, obstacle size and undulating frequency. Subsequently, an ALL system was established and five obstacle perception models were studied to analyze the perceptual characteristics of the ALL. Finally, the experiments were conducted to further reveal the effects of obstacles and motion parameters on the body surface pressure of robot fish. The results indicate that the obstacles have a significant effect on the pressure distribution of the surface of the fish body. Namely the parameters of separation, obstacle size and undulating frequency will affect the peak value of the amplitude envelope of the pressure signals. The obstacle size and distance between the obstacles can be predicted using the time parameters of the amplitude envelope of the pressure signals. Moreover, the self-propelled robot fish with a medium undulating frequency approach to the large obstacles with small separation has better perceptual performance. The findings could offer some insight into understanding the perception of complex underwater environment based on ALL.
Winter cold extremes over the eastern North America: Pacific origins of interannual-to-decadal variability
Authors: Ge Yao , Luo Dehai 
Published: April 2023

Abstract
Winter cold extreme events have been observed to frequently take place over North America mainly over its east side, which show significant interannual and decadal variability and cause huge economic losses in the United States. However, it is unclear what leads to the interannual-decadal variability of winter cold extremes over the eastern North America. In this study, we indicate that the decadal variability of winter cold extremes over the eastern North America, whose period is shortened in the recent decades, is mainly tied to Pacific decadal oscillation (PDO), whereas their interannual variability is mainly regulated by Victoria mode (VM). A positive PDO promotes cold extremes in the lower latitudes of the eastern North America mainly owing to the presence of positive Pacific North American (PNA+) patterns, whereas a positive VM is favorable for intense cold extremes in the higher latitudes of the eastern North America mainly due to the occurrence of negative North Pacific oscillation (NPO−) patterns. Thus, the positive VM and PDO combine to significantly contribute to the interannual-to-decadal variability of winter cold extremes over the eastern North America through changes in the winter NPO− and PNA+ patterns due to the variations of meridional background potential vorticity gradient and basic zonal winds. These new findings can help us understand what are the origins of the interannual-decadal variability of winter cold extremes over the eastern North America.
Ploidy variation in Rhododendron subsection Maddenia and its implications for conservation
Authors: Hu Ling , Tate Jennifer A , Gardiner Susan E , MacKay Marion 
Published: April 2023

Abstract
Polyploidy, which is common in plants, can confound taxon recognition and hence conservation assessments. In the taxonomically complex genus Rhododendron, 25 % of the over 1,300 taxa are considered under threat and 27 % Near Threatened or Data Deficient, with their taxonomy needing to be resolved urgently. Although ploidy levels of Rhododendron taxa range from diploid (2x) to dodecaploid (12x) according to previous reports, the extent of polyploidy across the genus has not been examined. We first summarized the taxonomic distribution of polyploids in the genus based on the literature. Then as a case study, we estimated ploidy levels of 47 taxa in subsection Maddenia (subgenus Rhododendron, section Rhododendron) using flow cytometry, together with verification of meiotic chromosome counts for representative taxa. The summary of reported ploidy in Rhododendron indicates that polyploidy is most common in subgenera Pentanthera and Rhododendron. In subsection Maddenia, all examined taxa are diploids except for the R. maddenii complex that shows a high ploidy variation (2–8x, 12x). We investigated ploidy level of 12 taxa in subsection Maddenia for the first time, and estimated genome sizes of two Rhododendron species. Knowledge of ploidy levels will inform phylogenetic analysis of unresolved species complexes. Overall, our study of subsection Maddenia provides a model for examining multiple issues including taxonomic complexity, ploidy variation and geographic distribution in relation to biodiversity conservation.
Accuracy and reliability of automated three-dimensional facial landmarking in medical and biological studies. A systematic review
Authors: Al-baker Bodore , Alkalaly Abdullah , Ayoub Ashraf , Ju Xiangyang , Mossey Peter 
Published: April 2023

Summary

Background
3D facial landmarking is becoming a fundamental part of clinical and biological applications. Manual landmarking is time consuming and prone to cumulative errors, so attempts have been made to automate 3D facial landmarking. However, data in the literature are sparse.

Objectives
The objectives of this study are to investigate current evidence for the accuracy and reliability of various 3D facial automated landmarking methods used in medical and biological studies and evaluate their performance against the manual annotation method.

Search methods
Electronic and manual searches of the literature were performed in April 2021.

Selection criteria
Only studies that were published in English and evaluated the accuracy of automated landmarking algorithms in 3D facial images for medical or biological settings were included.

Data collection and analysis
Two authors independently screened the articles for eligibility. The QUADAS-2 tool was used for the quality analysis of the included studies. Due to the heterogeneity of the selected studies, a meta-analysis was not possible, so a narrative synthesis of the findings was performed.

Results
From 1002 identified records, after applying the inclusion and exclusion criteria, 14 articles were ultimately selected, read, and critically analysed. Different algorithms were used for the automated 3D landmarking of various numbers of facial landmarks ranging from 10 to 29 landmarks. The average difference between the manual and automated methods ranged from 0.67 to 4.73 mm, and the best performance was achieved using deep learning models. Poor study design and inadequate reporting were found in the implementation of the reference standards and population selection for the intended studies, which could have led to overfitting of the tested algorithm.

Limitations
This systematic review was limited by the quality of the included studies and uncovered several methodological limitations evident in the corresponding literature

Conclusion and implications
Compared to manual landmarking, automated Landmark localization of individual facial landmarks reported in the literature is not accurate enough to allow their use for clinical purposes. This result indicates that automatic facial landmarking is still developing, and further studies are required to develop a system that could match or exceed the performance of the current gold standard.

Registration
PROSPERO: CRD42021241531.