bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic type Identification of Sub - Axial Vertebral Column Fractures using Deep learning Technique
Authors: Sindhura D N , Pai Radhika M , Bhat Shyamasunder N , Manohara Pai M M 
Published: October 2023

Abstract
Traumas like falls, sports injuries, or vehicle accidents are the main causes of Vertebral Column Fracture(VCFs) or spine fracture. The symptoms of VCF include back pain, swelling, numbness, and height decrease. VCF causes extreme pain, paralysis, difficulty in movement, etc. CT scans are inexpensive and effective in providing precise and quick VCF type detection. The determination of type VCF, however subject to inter-observer variability. To address this limitation this work introduces an automatic system for the detection of VCF type based on an ensemble of deep fine tuned models. This can assist the orthopaedicians in type identification of VCF which decreases the image interpretation time and increases the patient care time. For type identification, a total of two fine-tuned CNN architectures are used. Then, to further enhance the type identification performance, we developed an ensemble model of fine tuned VGG16 and ResNet50 deep learning models, which average the outputs of the models during final prediction. We used a dataset of 2820 CT images from eight different VCF types that were collected from Kasturba Medical college, Manipal Academy of Higher education, Manipal. The results shows that the ensemble model performs well compare to individual fine-tuned models with accuracy of 82%, precision of 0.84, recall of 0.82 and F1-score of 0.81 for VCF type identification from CT scans. This study shows that type identification of VCF in CT scans may be successfully accomplished using an ensemble deep transfer learning system with various fine tuned CNN architectures.
Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool
Authors: Pratima A , GopalaKrishna K , Prasad S N 
Published: October 2023

Abstract
Cardiac Arrhythmia (CA) is a disorder of heartbeat or rhythm, that happens when the electrical signals that synchronize the heartbeats do not function properly. The Electro Cardio Gram (ECG) is the electrical realization of the expanding and contracting action of the heart and can be registered easily with the electrodes placed near the chest. Hence, due to the complexity of analyzing the huge number of signals in ECG records, it has become one of the major challenges to cardiologists to make early and accurate diagnoses and prognoses. Therefore, there is an essential need for accurate automatic arrhythmia classification. According to the records of the World Health Organization (WHO), 4.5 million CA patients are reporting alone in the United States. Therefore, it is stated as one of the most common reasons for death worldwide and it is very essential to the early diagnosis and prevention of CA. Hence, this research article mainly focuses to analyse the various methods used for the classification, early diagnosis, and prevention of CA. This research presents the overview of a few research articles suggesting different methods based on various fields like IOT, Machine Learning (ML) approaches, Deep Learning (DL) approaches, and so on for the automatic detection of Cardiac Arrhythmia. The literature work mainly focuses on various early Detection, prediction, and classification techniques for CA. The research gaps were also analyzed from these papers and elaborated for further research work which can be helpful for society.
Attention based Peephole LSTM model for Soybean crop yield prediction
Authors: Shanmuga Priya S , Adwait Dathan R 
Published: October 2023

Abstract
In the agricultural domain, crop yield prediction is one of the most challenging tasks as the predicted yield depends on the input features such as the genomic type of crop, soil, and weather conditions that are taken into consideration. With the advent of precision agriculture, a lot of sensors have been used to measure different parameters related to the crop, which has brought in more challenges for feature selection. In this research work, an attention-based peephole LSTM model was designed to predict the yield of the Soybean crop in the US Corn Belt. The Attention block is designed to automatically learn the important features from the training data, while the Peephole LSTMs predict the yield of the crop using the weighted temporal features coming from the attention block. While comparing the generalized and site-specific modeling approaches in the nine states of corn belt with the same architecture, it was observed that on average the RMSE score of site-specific models were less than the generalized model by 1.31 bushels per hectare, making the site-specific approach a better option for yield prediction. Unmasking the black box property of the proposed architecture revealed that the model gave more weightage to precipitation and less weightage to vapor pressure.
Design of Efficient Multiply-Accumulate Unit for Convolutional Neural Networks
Authors: Sajjan Akash C , Gadhave Suyash , Ratnakumar Rahul 
Published: October 2023

Abstract
Convolutional Neural Networks (CNN) are used in a range of machine learning tasks, such as voice, image, and video processing. As the demand for faster response times in real-time applications grows, the need for high-speed implementation of CNNs is becoming more significant. However, the convolutional layer of CNNs is computationally demanding, leading to higher delays. Therefore, this study seeks to design an efficient and fast convolution block for the hardware implementation of the CNN algorithm. The proposed solution uses a Bit-Level Multiplier and Accumulator (BLMAC) unit that incorporates a modified Booth Encoder and Wallace Reduction Tree to achieve time optimization. The BLMAC, a key component of the convolution process, is optimized for speed. The area occupied by this architecture is 2761.517μm2, the power consumed is 121.4μW, and the delay is 9.11ns. Hence the proposed architecture is extremely power efficient and other parameters like area and delay have not been sacrificed to achieve this result. The proposed BLMAC architecture is designed using Verilog. The testbench for the code was verified and simulated in Cadence NCSIM. The synthesis is done in the Genus tool offered by Cadence.
Study of Nonlinearities in FSO Communication System: A Detailed Review
Authors: Kumar Suresh , Sharma Pulkit 
Published: October 2023

Abstract
In the world of today where growing demand for a fast, reliable and secured communication system for the growing population of the world is in dire need, the Free Space Optics (FSO) provides a very good alternative to the conventional RF communication. When compared in terms of free space communication it is better than RF communications in some aspects. Also in regard to optical mode of communication, it is superior to fiber optics when it comes to surpassing the geographical installation related issues and being the “last mile” solution to the impeding problem of providing access of communication link to remote areas across the world. FSO also offers a good access for local operation usage in civil and military areas. Considering such wide usage and promising future applications of FSO technology, this paper brings out various challenges presently faced in the usage of FSO communication system. In the present paper study of non linearities faced and a brief discussion regarding possible mitigation techniques has been carried out.
Electron structure and phonon dynamics of LiNbRhAl quaternary Heusler compound
Authors: Suktel Lokanksha , Saini Sapan Mohan 
Published: October 2023

Abstract
Li based quaternary Heusler compounds, having 18 valence electrons show significant applications in different fields i.e., spintronics, thermoelectric and photo voltaic etc. We report the structural stability, electronic property and dynamical stability of LiNbRhAl compound, (Li based QH alloy) via volume optimization and phonon dynamics. Calculations have been performed using Generalized Gradient Approximation based on Density Functional Theory. LiNbRhAl compound crystallizes in LiMgPdSn – type crystal structure having three types of atomic arrangements. We found atomic arrangement of type-3 is energetically most stable. Self-consistence band structure calculations show that valence band (VB) lie from – 4 eV to the Fermi level (EF), whereas conduction band (CB) span up to 2 eV above the EF. Our DOS plots show that Rh-4d states mainly contribute in valence band (VB), while Nb-4d states dominate in both VB and CB bands near EF and open an indirect band gap of 0.177 eV. Presence of positive frequencies in phonon dispersion curve shows that the above-mentioned compound is dynamically stable.
Long Lasting Persistent Photo-Luminescence Properties of EuxM1-xMgAl10O17(M = Ba, Ca, Sr, Zn) Phosphors
Authors: Verma Akshkumar , Bisen DP , Brahme N , Sahu Ishwar Prasad , Barik Priya 
Published: October 2023

Abstract
In this work, the EuxM1-xMgAl10O17[M = Ba, Ca. Sr, Zn, and x = 0.05)] phosphors were prepared through a combustion route technique. The complexing reagents namely urea (fuels) were used to synthesis for EuxBa1-xMgAl10O17 (BAM: Eu), EuxCa1-xMgAl10O17 (CAM: Eu), EuxSr1-xMgAl10O17 (SAM:Eu), and EuxZn1-xMgAl10O17 (ZAM:Eu) phosphors. The crystallinity of the synthesized compound, elemental analysis and surface morphology were measured using the PXRD FE-SEM, and FTIR spectroscopy respectability. The average crystallite size (<Ds>) of BAM: Eu, CAM:Eu, SAM: Eu, and ZAM: Eu were found 80, 30, 29, and 26 nm respectively. The photoluminescence emission spectra of phosphor were found broadband by the monitoring using common excitation (350 nm) and emission (450 nm). The CIE-1931 color- coordinates were calculated for each sample in the blue region. The overall PL emission was found near the blue [BAM: Eu, SAM: Eu, and ZAM: Eu ] and blue-red [CAM:Eu] regions with high quantum efficiency and high color purity. Long-lasting PL decay was also recorded of samples with respect to all samples BAM:Eu was calculated from 0.46 to more than 40 min. Therefore, the BAM: Eu and other synthesized phosphors would be used as a blue-emitting phosphor in widely varying fields of lightings.
Background Constraints on Dark Energy Models
Authors: Mishra Shruti , Singh Dr. Avinash 
Published: October 2023

Abstract
Due to the gravitational attraction of all the matter in the universe, the expansion rate of the cosmos has changed over time, decreasing (decelerating) in past, and more recently speeding up (accelerating). The hypothesis that the Universe is expanding quickly and is spatially nearing its limit now has a lot of cosmological evidence to back it up (assuming the density is at least somewhat time-independent). The majority of cosmologists believe that “dark energy” is to blame for the accelerated cosmological expansion that has been witnessed. The cosmological constant, an additional constant to the Einstein field equation, can be used to explain why the universe is expanding faster than before. The ΛCDM model is the simplest and most common model in use. Besides this, there are also dark energy models like the Barotropic fluid model, canonical scalar field model, and non-canonical scalar field model. We analyse the background constraints on dark energy models using current cosmological data available. We also present the comparison between dark energy models using Bayesian statistics.
Design and development of an autonomous unmanned aerial vehicle for surface coalmines surveillance
Authors: Kumar Aayush , Abhigyan  , Khanduri Ansh , Jain P K 
Published: October 2023

Abstract
This paper presents a detailed investigation into the development of a specialized unmanned aerial vehicle (UAV) for practical applications in coal mining. The main focus is on enhancing surveillance capabilities, identifying potential hazards, and supporting various coal mine activities. This study initiates with the careful design of a quadcopter, taking into account essential parameters such as VTOL, maneuverability, and hover efficiency. The quadcopter model is meticulously developed to ensure smooth operation under ideal conditions. The design process involves utilizing SolidWorks software to create a robust and optimized quadcopter model. Additionally, the static deformation simulation of the quadcopter is conducted using Ansys software, providing valuable insights into its structural integrity and performance. Dynamic modelling using the Bond Graph Method enables a thorough understanding of the quadcopter’s behavior and informs propulsion system selection. The chosen propulsion system prioritizes maximizing thrust-to-weight ratio to about 2:1, achieving the factor of safety of approximately 4, and ensuring maneuverability. The use of eCalc, an online open-source software, assists in identifying the ideal motor-propeller combination for optimal thrust characteristics. Surveillance and mapping techniques are explored in detail. A step-by-step methodology is proposed for image identification, while mapping is achieved through image stitching and path planning using waypoints. Additionally, volume estimation and vulnerability assessment methods specific to coal mining are discussed.
Augmented Reality Based Training Module for IC Engine Assembly and Disassembly
Authors: Sundaram Sankara Narayanan Shunmuga , Gurusamy Muralimohan 
Published: October 2023

Abstract
Augmented reality (AR) technology has revolutionized various industries, including education and training. This paper proposes an AR-based training module for IC engine assembly and disassembly, which is aimed at providing an interactive and immersive learning experience to trainees. The module was developed using Solidworks for modeling, Blender for animation, and Unity for game development. Its primary objective is to help trainees improve their understanding and retention of complex procedures, thereby enhancing their technical skills, multi-step tasks, and product knowledge. Our approach intends to eliminate traditional paper-based instructions and replace them with AR-based ones, increasing maintenance efficiency and bridging the knowledge gap between OEMs and internal maintenance operators. The training module was designed for technicians and students who are learning how to assemble and disassemble engines. To make it more accessible and versatile, it was integrated with various devices, such as smartphones or tablets. This allows trainees to access the module from anywhere, at any time, and on any device, thus enabling remote learning. The module can be implemented in various settings, such as training centres, manufacturing facilities, or even through online platforms. Overall, this project represents a significant step forward in AR-based training modules for technical education. The module’s accessibility and versatility make it suitable for various settings, and its structured development process ensures that it is effective and reliable. The use of AR technology in education and training is an exciting field with tremendous potential, and this project demonstrates one of the many ways it can be applied to improve learning outcomes.
Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm
Authors: Kulkarni Rohan , Kakade Suhas , Mawande Shamli , Sadakale Ranjit 
Published: October 2023

Abstract
The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.
Determination of optimum process parameters for minimum punch force in deep drawing using numerical simulation
Authors: Sisil Amose A , Elsie Femina K , Sivarajan S 
Published: October 2023

Abstract
This article represents the most common sheet formation method, deep drawing. This technology is employed in the production of the automotive and aerospace industries. There are various aspects that have an impact on the deep drawing process; these factors are known as the deep drawing process parameters. Finite element analysis and Taguchi Techniques are coupled to find out the optimum deep drawing parameters. Six process parameters such as die corner radius, punch corner radius, punch speed, friction between blank and die, friction between blank and blank holder and friction between blank and punch are used for analysis. Punch force is considered as response variable. The Taguchi method is employed to determine the optimal process parameters for the deep drawing process. Based on SN ratio analysis, optimum deep drawing parameters for punch force were found to be die corner radius of 7mm, punch corner radius of 7mm, punch speed of 0.2mm/s, friction between blank and die of 0.08, friction between blank and blank holder of 0.08 and friction between blank and punch of 0.10. From ANOVO, it was found that die corner radius was the most significant process parameter influencing minimum punch force.
Enhanced antimicrobial properties and bioactivity of 3D-printed titanium scaffolds by multilayer bioceramic coating for large bone defects
Authors: Milivojevic Marija , Chen Ke , Radovanovic Zeljko , Petrovic Rada , Dimitrijevic-Brankovic Suzana , Kojic Vesna , Markovic Danica , Janackovic Djordje 
Published: October 2023

Abstract
The restoration of large bone defects caused by trauma, tumor resection, or infection is a major clinical problem in orthopedics and dentistry because postoperative infections, corrosion, and limited osteointegration of metal implants can lead to loosening of the implant. The aim of this study was to improve the surface properties of a 3D-printed (electron beam melting) Ti6Al4V-based macroporous scaffold by multilayer coating with bioactive silicate glasses (BAGs) and hydroxyapatite doped with a silver (AgHAP) or AgHAP additionally sonochemically modified with ZnO (ZnO-AgHAP). The coated scaffolds AgHAP_BAGs_Ti and ZnO-AgHAP_BAGs_Ti enhanced cytocompatibility in L929 and MRC5 cell lines and expressed bioactivity in simulated body fluid. A lower release of vanadium ions in coated samples compared to bare Ti scaffold indicates decreased dissolution of Ti alloy in coated samples. The coated samples reduced growth of Escherichia coli and Staphylococcus aureus for 4–6 orders of magnitude. Therefore, the 3D-printed Ti-based scaffolds coated with BAGs and (ZnO-)AgHAP have great potential for application as a multifunctional implant with antibacterial properties for the restoration of defects in load-bearing bones.
Advantages of agarose on alginate for the preparation of polysaccharide/hydroxyapatite porous bone scaffolds compatible with a proline-rich antimicrobial peptide
Authors: Mardirossian Mario , Gruppuso Martina , Guagnini Benedetta , Mihalić Franka , Turco Gianluca , Porrelli Davide 
Published: October 2023

Abstract
The optimized proline-rich antimicrobial peptide B7-005 was loaded on bone scaffolds based on polysaccharides and hydroxyapatite. Alginate was firstly chosen in order to exploit its negative charges, which allowed an efficient B7-005 loading but hindered its release, due to the strong interactions with the positive charged peptide. Hence, alginate was substituted with agarose which allowed to prepare scaffolds with similar structure, porosity, and mechanical performance than the ones prepared with alginate and hydroxyapatite. Moreover, agarose scaffolds could release B7-005 within the first 24 h of immersion in aqueous environment. The peptide did not impaired MG-63 cell adhesion and proliferation in the scaffold, and a positive cell proliferation trend was observed up to two weeks. The released B7-005 was effective against the pathogens E. coli, K. pneumoniae, and A. baumannii, but not against S. aureus and P. aeruginosa, thus requiring further tuning of the system to improve its antimicrobial activity.
Understanding uncertainties in projections of western North Pacific tropical cyclogenesis
Authors: Huang Xin , Zhou Tianjun , Chan Johnny C L , Zhan Ruifen , Chen Ziming , Zhao Jiuwei 
Published: October 2023

Abstract
Reliable projections of tropical cyclone (TC) activities in the western North Pacific (WNP) are crucial for climate policy-making in densely-populated coastal Asia. Existing projections, however, exhibit considerable uncertainties with unclear sources. Here, based on future projections by the latest Coupled Model Intercomparison Project Phase 6 climate models, we identify a new and prevailing source of uncertainty arising from different TC identification schemes. Notable differences in projections of detected TCs and empirical genesis potential indices are found to be caused by inconsistent changes in dynamic and thermodynamic environmental factors affecting TC formations. While model uncertainty holds the secondary importance, we show large potential in reducing it through improved model simulations of present-day TC characteristics. Internal variability noticeably impacts near-term projections of the WNP tropical cyclogenesis, while the relative contribution of scenario uncertainty remains small. Our findings provide valuable insights into model development and TC projections, thereby aiding in adaptation decisions.
Record-breaking persistence of the 2022/23 marine heatwave in the Mediterranean Sea
Authors: Marullo Salvatore , Serva Federico , Iacono Roberto , Napolitano Ernesto , di Sarra Alcide , Meloni Daniela , Monteleone Francesco , Sferlazzo Damiano , De Silvestri Lorenzo , de Toma Vincenzo , Pisano Andrea , Bellacicco Marco , Landolfi Angela , Organelli Emanuele , Yang Chunxue , Santoleri Rosalia 
Published: October 2023

Abstract
Since May 2022, the Mediterranean Sea has been experiencing an exceptionally long marine heatwave event. Warm anomalies, mainly occurring in the Western basin, have persisted until boreal spring 2023, making this event the longest Mediterranean marine heat wave of the last four decades. In this work, the 2022/2023 anomaly is characterized, using in-situ and satellite measurements, together with state of the art reanalysis products. The role of atmospheric forcing is also investigated; the onset and growth of sea surface temperature anomalies is found to be related to the prevalence of anticyclonic conditions in the atmosphere, which have also caused severe droughts in the Mediterranean region over the same period. Analysis of in-situ observations from the Lampedusa station and of ocean reanalyzes reveals that wind-driven vertical mixing led to the penetration of the warm anomalies below the sea surface, where they have persisted for several months, particularly in the central part of the basin. The evolution of the 2022/23 event is compared with the severe 2003 event, to put recent conditions in the context of climate change.