bluuebunny/crossref_metadata_2025_split huggingface dataset:
Area-Efficient Gray Code Counter Design Using Hybrid Flip-Flop
Authors: Rajani alugonda  , Kalyani koilada  
Published: November 2024

Chip area plays an important part in the advancement of semiconductor technology, enabling integrated circuits to get smaller and smaller over time. Numerous advantages result from this reduction, including higher production yield, better energy efficiency, better system performance, and lower component density in packaging. We are Utilizing [2] 18-transistors hybrid flip-flop structure to design a Gray code counter (GCC) [2] with asynchronous clock input, the design is useful for high-performance implementations, Gray code is a unit-distance coding technique that significantly decreases error propagation in communication networks by altering only one bit every transition. The suggested design outperforms traditional Gray code counters by reducing the area by about 30.49%. The implementation operates in the voltage range of 0.7-1.2V and has a transient response time of 300ns. Cadence Virtuoso in 45nm is used to accomplish it.
Pembuatan Pilar Support Mesin 3D Concrete Printing menggunakan Konstruksi Cremona dengan Material Besi Profil Siku
Authors: Nopriyanti Rani , Fauzi Muhammad , Setiawan Heri , Kurniaji Anhar 
Published: November 2024

Mesin 3D concrete printing memiliki beberapa bagian, diantaranya adalah bagian pilar. Pilar terbagi menjadi pilar utama dan pilar support. Pilar utama terbagi menjadi tiga bagian yaitu pilar sumbu X, pilar sumbu Y, dan pilar sumbu Z. Penelitian ini berfokus kepada proses fabrikasi pilar support dan estimasi estimasi biaya - waktu untuk mesin 3D concrete printing dengan dimensi 7020 x 400 x 354 mm. Pilar support terbuat dari besi profil siku dengan dimensi 30 x 30 x 3 mm, dan berfungsi untuk menopang pilar sumbu Z dan menjaga kesejajarannya. Berdasarkan dokumen operation plan yang telah dibuat dan perhitungan biaya-waktu menunjukkan bahwa proses fabrikasi dapat diselesaikan dalam 7,16 jam dengan biaya Rp. 1.588.140,-. Hasil penelitian ini memberikan gambaran detail mengenai proses pembuatan pilar support yang dapar diaplikasikan pada proyek serupa.
GAMBARAN KUALITAS HIDUP PASIEN DIABETES MELLITUS DI PUSKESMAS PEKAUMAN BANJARMASIN
Authors: Eri Setiani  , Cynthia Eka Fayuning Tjomiadi  , Onieqie Ayu Dhea Manto  
Published: November 2024

Diabetes Mellitus adalah gangguan metabolisme karbohidrat, protein, dan lemak yang ditandai oleh hiperglikemia. Hiperglikemia dapat terjadi karena menurunnya kerja insulin. Penderita Diabetes Mellitus mengalami perubahan dan ketidakseimbangan pada berbagai aspek kehidupannya sehingga mengganggu aktivitas sehari-hari, kondisi ini dapat menurunkan kualitas hidup pada penderitanya. Penelitian ini bertujuan untuk mengetahui gambaran kualitas hidup pasien Diabetes Mellitus di Puskesmas Pekauman Banjarmasin. Penelitian kuantitatif dengan rancangan deskriptif analitik ini dilakukan pada 48 pasien penderita Diabetes Mellitus yang dipilih dengan teknik purposive sampling menggunakan kuesioner EQ5D-5L yang dianalisa secara univariat. Mayoritas responden berusia dewasa (72,9%), perempuan (62,5%), memiliki IMT Obesitas I (37,5%), dan menderita Diabetes Mellitus ?1 tahun (60,4%). Kualitas hidup mayoritas penderita Diabetes Mellitus termasuk kategori buruk (56,3%). Kualitas hidup pasien Diabetes Mellitus termasuk dalam kategori buruk dengan gejala dominan berupa kecemasan. Kualitas hidup yang buruk pada penderita Diabetes Mellitus berkaitan dengan karakteristik mayoritas responden merupakan perempuan berusia >50 tahun yang memiliki IMT kategori Obesitas I dan telah menderita DM ?1 tahun.
Keywords:
Characteristics, Diabetes Mellitus, Quality of Life.
Vastuullisuuden politiikat: valtio, oikeudet ja dialogi kaivoskonflikteissa
Authors: Järvelä Johanna 
Published: November 2024

Väitöskirjassani tutkin deliberatiivisen dialogin rajoja, sekä oikeuksien ja vastuiden jakoa valtion, yritysten ja kansalaisyhteiskunnan välillä luonnonvarojen hallinnan ja kaivoskonfliktien kontekstissa. Globalisaation myötä perinteiset rajat näiden toimijoiden välillä ovat murtuneet ja muuttuneet, ja erilaisista intresseistä neuvotellaan moninaisilla paikallisilla ja globaaleilla foorumeilla. Näin ollen, on kiinnostavaa tutkia miten erilaisia intressikoalitioita ja hegemonioita rakennetaan, ylläpidetään ja kyseenalaistetaan. Teoreettisesti tutkimukseni sijoittuu organisaatiotutkimuksen alalle hyödyntäen politiikan tutkimuksen ja poliittisen ekologian käsitteitä. Tutkimus selvittää miten erilaiset historialliset ja poliittiset tekijät vaikuttavat rakentuviin hallinnan muotoihin, miten yksityinen sääntely korvaa tai lomittuu osaksi julkisen vallan käyttöä, ja millaisia mahdollisuuksia ja haasteita nämä uudet hallinnan muodot tuottavat. Lisäksi tutkimukseni tarkastelee kriittisesti deliberatiivisen dialogin mahdollisuuksia johtamisen ja organisaatiotutkimuksen alalla painottaen tasa-arvoisen osallistumisen tärkeyttä sekä erimielisyyksien, ristiriitojen ja konfliktien mahdollistamista näissä prosesseissa. Väitöskirjan tulokset tuovat uutta tietoa yritysten ja yhteiskunnan välisistä suhteista, ja erityisesti siitä miten julkinen valta tuottaa yritysvastuuta ja vastuullisia yrityksiä.
Morphosyntactic Errors in Students’ Written Narrative Text
Authors: Masriani Mery Rosmida Silalahi  , Rakasiwi Rani , Tampubolon Marlina , Siahaan Ricki Asi Erwindo 
Published: November 2024

The objective of this study are to (1) investigate types of errors in students’ Written Narrative Text made by students grade IX, (2) to find out the causes of errors on the students’ writing. Based on the analysis by Dulay’s theory, there are four kinds of errors: 1. Error of Omission, 2. Error of Addition, 3. Error of Misformation, 4. Error of Misorder. The dominant types of errors found in the students writings is Error of Misformation. The source of errors is analysed based on Brown’s Theory. The source of the errors are: 1) Interlingual Transfer, 2) Intralingual Transfer, 3) Context of Learning, and 4) Communication Strategy. Based on the analysis, the source of errors found are Interlingual Transfer and Intralingual Transfer. This study uses descriptive qualitative research. The subjects of this study are the students of grade IX SMK Satria Binjai. In conclusion, students make some morphosyntactic errors in their writing an d the potential cause of the errors is intralingual Transfer and Interlingual Transfer. In this case, the conclusion and the suggestions are directed to those who would teach and learn english writing.
Keywords : Error Analysis, Narrative Text, Morphosintactic
Analytical Solution of the Klein-Gordon coupled Equations via the Differential Transform Method
Authors: Asal B. Saleh  
Published: November 2024

The basic equations in physical applications are the Klein-Gordon equation, as it gives a description of numerical particles in the fields of quantum and quantum mechanics, through the use of the differential transformation method and combining it with the Idomian method, in order to provide an integrated method for solving this equation, and it has been widely used to solve differential equations. Partial, to get rid of nonlinear terms and complex terms by integrating these two methods, which led to reducing the error and thus increasing the efficiency and accuracy of the solution using these numerical methods and the theoretical analysis of the method.. Researchers can learn more about quantum field dynamics using our method, which makes it easier to study a wide range of physical phenomena including scalar particles. In addition to improving the computational toolset in relativistic quantum mechanics, this work offers a useful foundation for investigating basic interactions in the field of particle physics and other fields.
Conclusions: Adomain decomposition method (ADM) and the Differential Transform Method (DTM) together provide a powerful approach to solving the Klein-Gordon system equation. The effectiveness of this integrated method in efficiently and precisely solving complex differential equations controlling scalar particle dynamics has been proven by our research. The nonlinearities and boundaries of the Klein-Gordon system equation have proven to be formidable obstacles, but we have overcome them by combining the two approaches, and the findings are solid and trustworthy. Scalar particle behaviour in relativity quantum mechanics can be better understood with the help of this comprehensive methodology, which also improves computational efficiency. The DTM technique is a powerful and flexible tool for academics in many fields, including applied mathematics, particle physics, and quantum field theory, as our results show. The future looks bright for tackling ever more complex systems and improving our understanding of basic physical processes as we continue to investigate and enhance this integrated approach.
Sustainable Plastic Waste Management in Thanjavur District: An Evaluation Using Fuzzy AHP and Neutrosophic TOPSIS
Authors: V. Usha  
Published: November 2024

Introduction: The amount of plastic garbage has increased in tandem with the steady growth in the use of plastic products worldwide. Since plastics are lightweight, inexpensive, and durable, they are highly appreciated in a variety of industries, including electronics, building, packaging, healthcare, and agriculture). Plastic's cost, adaptability, resistance to corrosion, durability, and ease of production all contribute to its rising demand.With changes to the Plastic Waste Management Rules, India has made managing single-use plastics a top priority.
Objectives: The research provides an extensive examination of the methods used to manage plastic garbage in Thanjavur's rural villages. Examining household behaviors and waste production trends, it reveals a wide range of plastic kinds, including polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polyethylene terephthalate (PET).
Methods: The research uses a Multi-Criteria Decision Making (MCDM) approach that combines the Neutrosophic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for alternative ranking with the Fuzzy Analytic Hierarchy Process (FAHP) for criteria weighting. Although it uses fuzzy logic to address ambiguity and uncertainty in decision-making processes, the Fuzzy AHP approach is applied in a manner similar to that of AHP.
Results: Egestas diam in arcu cursus euismod quis viverra nibh. Convallis aenean et tortor at risus viverra. Sit amet justo donec enim diam. Sem et tortor consequat id. Purus gravida quis blandit turpis. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Eget duis at tellus at urna condimentum mattis pellentesque. Auctor elit sed vulputate mi sit amet.
Conclusions: This study determines that, among the available options, Polyethylene Terephthalate (PET) is the best option by applying the fuzzy analytic hierarchy process (Fuzzy AHP) to balance these criteria. PET exhibits potential because of its economic and environmental advantages, but its effective application necessitates customized strategies that take into account the infrastructure constraints and socioeconomic characteristics of the local area. Programs for raising community knowledge and participation are crucial in promoting sustainable waste management techniques, as the study emphasizes.
Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images
Authors: Naveenkumar Jayakumar  
Published: November 2024

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.
Subclass of Harmonic Univalent Functions Connected with Hypergeometric Functions
Authors: Katterapalle Sridevi  
Published: November 2024

In this paper, we introduce and define a new subclass of harmonic univalent functions involving hypergeometric functions which are sense-preserving in the open unit disc. Further we obtain the coefficient bounds, extreme points, convex combination, growth bounds, distortion bounds and integral operator for this class.
CNN-Based Voice Recognition by Self-Governing Robots to Improve Computer-Human Communication
Authors: Ben Sujin. B  
Published: November 2024

Robots must have the ability to recognize human emotions to engage with individuals and to plan their movements autonomously. Nonverbal signals encompassing pitch, loudness, spectrum, and speech speed are successful methods for transmitting emotions to most individuals. Provided this situation, a machine could have the capability of qualified figure out emotions by deploying the traits of spoken communication, and these potentially propagate vital information concerning the speaker's emotional state. More precisely, a combination of numerous facial action units can be employed to describe a human's emotion. In this paper, we propose a deep Convolutional Neural Network-based system that can identify sentiments in real-time and with a high accuracy rate. This investigation establishes an entirely novel speech-emotion detection system founded on Convolutional Neural Networks (CNNs). With the support of a top-tier GPU, a model is developed and nourished raw speech from a specific set for training, classification, and testing purposes. We further analyze the speech data and incorporate the information from the visual and audio sources to enhance the recognition system's accuracy. The benefits of the proposed method for emotion identification and the implications of blending visual and aural suggestions are made clear by the experimental results. Convolutional neural networks (CNNs) are taught on grayscale images from the softmax dataset in the present work. To acquire the best accuracy, we experimented with different baselines and max pooling layers, finally acquiring 89.98% accuracy. Dropout is one approach we have used to ward off overfitting.
The Collection of Various βws – Closed Sets in Topological Spaces
Authors: S. Saranya  
Published: November 2024

This research presents an innovative class of Beta weakly semi-closed sets namely, The collection of various βws – closed sets in Topological spaces. Throughout this paper, βws-Semi closure sets, βws-Interior and βws – Neighbourhood sets were examined to get the fundamental facts in the Beta weakly semi-closed sets. In this paper, the notion of countable βws-semi- closed sets and βws-Interior in TS were explored and βws – Neighbourhood (βws-nbd) in TS were also studied to get results. Here, many characterizations were obtained along with some of their feature results.
A Two-Phase Methodology leveraging X-DenseNet for Multi-Organ Segmentation in Abdominal CT Scans
Authors: Harinder Kaur  
Published: November 2024

Introduction: Multi organ segmentation (MOS) is crucial for various medical applications such as disease diagnosis, treatment planning, and surgery. Manual segmentation of CT organs is inefficient, laborious and time-consuming, necessitating the development of automated techniques. To address these challenges, automatic techniques based on deep learning models are explored in this field. These techniques require improvements to deal with patient-to-patient variability in organ size, location, and shape. Despite the transformative potential of deep learning techniques, research studies often inadequately address low contrast and overlapping structures within CT scans. This paper proposes a novel two phase methodology to integrate deep learning models and image enhancement strategies.
Methods: Our framework contributes in two stages named (a) Optimized Contrast Limited Adaptive Histogram Equalization-Weighted Grey Wolf Optimization (optiCLAHE-Weighted GWO), where the organs contrast is enhanced by employing weighted GWO for CLAHE clip limit selection (b) X-DenseNet architecture, where segmented regions of interest are produced with X-DenseNet model from the enhanced CT.
Results: For validation of proposed multi organ segmentation (PMOS) technique, experimentation and comparative analysis with existing models has been conducted on FLARE 22 challenge dataset. The Dice Score (DSC) of liver, aorta and spleen for proposed Multi organ segmentation (PMOS) approach is 90.4%, 99.38% and 98.33% respectively. The mean of Precision (Pre), Accuracy (Acc), F-score and DSC of the proposed approach are 95.05%, 95.55%, 95.29% and 96.06% respectively.
Logic Density in Gabor Frame Structures
Authors: Manal Yagoub Ahmed Juma  
Published: November 2024

The Gabor system G(g,1+ε,ε-1)={e^2iπ(ε-1)nt g(t-(1+ε)m):m,n∈Z}is examined with respect to its frame property, uunder reasonable oversampling conditions, that is, when (ε+1,ε-1∈Q).
An appropriate "rational" counterpart of the Ron-Shen Gramian is developed, establishing that for every peculiar pane function (g), The structure: G(g,ε+1,ε-1) fails to produce a frame if ε^2=(2n-1)/(n-1).
A particular focus is placed on the initial Hermite function, h_1=te^(-πt^2 ).
Machine Learning and Computer Vision Driving Precision in Robotic Surgery
Authors: Ipseeta Nanda  
Published: November 2024

The utilization of Artificial Intelligence (AI) in robotic surgery represents a significant advancement in healthcare, improving precision, effectiveness, and overall patient outcomes. The research studies the recent improvements in AI-powered robotic surgery, fastening on how artificial intelligence algorithms such as machine learning, computer vision, and natural language processing are transforming surgical processes. AI improves the functioning of robotic systems by employing data-driven algorithms, enabling independent or semi-autonomous tasks, and furnishing surgeons with exceptional delicacy through real-time analytics. AI-supported minimally invasive procedures and stoked reality interfaces have helped to drop complications, rehabilitation times, and surgical failures. The exploration explores the impact of AI on surgical training and simulation, in which AI- AI-grounded virtual surroundings give surgeons realistic practice scripts before performing live operations.
"Smart Farming in the Indian Context: Fertilizer Prediction through Ensemble Machine Learning Based Soil Nutrient Analysis"
Authors: Bhagwan Dinkar Thorat  
Published: November 2024

The agricultural landscape in India is undergoing a transformative shift with the integration of advanced technologies to enhance productivity and sustainability. Recognizing the critical role of soil health in agricultural outcomes, this research leverages advanced algorithms to analyze and interpret soil nutrient data, providing farmers with accurate and timely recommendations for optimal fertilizer application. The methodology involves the collection of comprehensive soil nutrient information from various regions, utilizing state-of-the-art sensing technologies. Relationships between soil nutrient levels and crop performance are established through the applying ML models, which include regression & classification methods. The purpose of this study is to create a fertilizer prediction model capable of anticipating fertilizer requirements based on specific soil characteristics, crop types, and regional variations. The anticipated benefits of this research include improved resource utilization, enhanced crop yields, and reduced environmental impact through the targeted application of fertilizers. By providing farmers with precise recommendations tailored to their specific soil conditions, this approach seeks to contribute to sustainable agricultural practices, economic efficiency, and overall food security in the Indian context. This paper underscores the potential of machine learning applications in revolutionizing traditional farming practices by introducing data-driven decision-making processes.
Industrial Product Surface Defect Detection Using CNN: A Deep Learning Approach
Authors: Deepali Ujalambkar  
Published: November 2024

Defect identification on surfaces of industrial products is a thought-provoking issue that has garnered significant attention. Defect identification of industrial products plays a primordial role in ensuring quality and reliability of products at all levels of the manufacturing process. The traditional approach relies fundamentally on the human inspection, which was unreliable and inefficient. Moreover, human inspection cannot meet the high standards for real-time detection required in industrial production situations. While methods used in image processing can solve difficult modules, they are not meant to deal with complicated ambient textures, noise, or lighting fluctuations. Innovative techniques have so been used by researchers and practitioners to improve and expedite the defect diagnosis process. This study classifies the product as "defect or not defect." and provides an intelligent approach for surface defect identification using convolutional neural network (CNN). The model demonstrates strong performance in identifying many types of industrial product defects, such as "crack, patch, inclusion, rolled, pitting, and scratching," by utilizing deep learning approaches. This involved training the model with several datasets featuring surface textures. The results were indicative of better performance, where the proposed method achieved outstanding precision in fault detection.