bluuebunny/crossref_metadata_2025_split huggingface dataset:
Jarimatika Digital: Media Berhitung Materi Perkalian Siswa Sekolah Dasar Berbantuan Articulate Storyline
Authors: Purwaningsih Ni Ketut Maysi , Wiyasa I Komang Ngurah 
Published: January 2025

Pembelajaran di kelas cenderung belum menarik bagi siswa karena kurangnya variasi media dalam pembelajaran tersebut. Penelitian ini bertujuan untuk mengembangkan media berhitung metode jarimatika pada muatan pelajaran matematika untuk siswa sekolah dasar yang valid dan praktis. Penelitian ini merupakan penelitian pengembangan (research and development) dengan mengadaptasi model pengembangan ADDIE. Pengumpulan data dilakukan dengan menggunakan instrumen berupa kuesioner dan tes hasil belajar. Data validitas media bersumber dari 2 ahli rancang bangun, 2 ahli isi mata pelajaran matematika, 2 ahli desain instruksional, dan 2 ahli media pembelajaran. Data kepraktisan media bersumber dari 1 orang praktisi pembelajaran di sekolah dasar, 3 siswa untuk uji coba perorangan, dan 9 siswa untuk uji coba kelompok kecil. Proses analisis data dilakukan dengan menggunakan pendekatan kualitatif dan kuantitatif. Hasil pengembangan menunjukkan bahwa media berhitung metode jarimatika pada muatan pelajaran matematika untuk kelas II sekolah dasar dinyatakan valid dengan rata-rata hasil penilaian ahli dengan kualifikasi baik; praktis dengan rata-rata hasil uji respon dengan kualifikasi sangat baik, serta efektif berdasarkan data perolehan nilai rata-rata hasil belajar siswa sesudah menggunakan media berbasis metode jarimatika lebih tinggi dibandingkan dengan nilai rata-rata siswa sebelum menggunakan media berbasis metode jarimatika. Dapat disimpulkan bahwa media berbasis metode jarimatika valid, praktis, dan efektif digunakan dalam proses pembelajaran sebagai salah satu upaya meningkatkan hasil belajar siswa ke tingkat yang lebih baik.
E-Modul Pembelajaran IPA Berbasis Isu-Isu Sosial Sains untuk Meningkatkan Literasi Sains dan Literasi Sosial Peserta Didik Kelas VIII
Authors: Rosmayuni Anak Agung Ayu Putri , Suma Ketut , Suja I Wayan 
Published: January 2025

Implementasi Kurikulum Merdeka yang telah berlangsung selama dua tahun menghadapi berbagai masalah. Salah satu masalah utama adalah kesiapan sumber belajar yang dapat mendukung tujuan kurikulum. Penelitian ini bertujuan untuk menganalisis karakteristik e-modul, validitas, kepraktisan, dan keefektifan e-modul IPA berbasis isu sosial sains dalam meningkatkan literasi sains dan literasi sosial. Penelitian pengembangan ini menggunakan model pengembangan ADDIE. Uji efektivitas menggunakan desain pretest -posttest non-equivalent control group design. Subjek yang terlibat pada penelitian ini adalah ahli materi, ahli bahasa, dan ahli media. Pengumpulan data dilakukan menggunakan instrumen yakni angket validitas ahli, angket kepraktisan, tes literasi sains, dan pedoman observasi literasi sosial. Data dianalisis menggunakan teknik analisis data deskriptif kualitatif dan deskriptif kuantitatif. Hasil penelitian menunjukkan bahwa, e-modul memiliki tingkat validitas isi, bahasa, dan media yang tergolong sangat tinggi. Selain itu, e-modul juga tergolong pada kategori sangat praktis. Dapat disimpulkan bahwa, kebaruan fitur seperti, video interaktif, fill the blank, drag and drop, image hotspot, image pairing, dan rekaman audio pada e-modul IPA berbasis isu sosial sains terbukti efektif meningkatkan literasi sains dan literasi sosial peserta didik. Penelitian ini berimplikasi pada peningkatan kuantitas dan kualitas sumber belajar yang dapat mendukung penerapan Kurikulum Merdeka.
Investigation of Deep Learning Models for Analysis of Heart Disorders in Smart Health Care based IoT Environment
Authors: Sengupta Jewel 
Published: January 2025

Abstract
Heart disorders are a crucial global health issue, requiring effective and precise diagnostic mechanisms for early identification and timely intervention. Traditional healthcare systems face challenges such as delayed diagnosis, insufficient real-time monitoring, and difficulty in processing large volumes of sequential cardiovascular data. Existing machine learning models often struggle with capturing temporal dependencies in data and addressing issues like data noise and computational efficiency on resource-constrained IoT devices. To overcome these limitations, this research investigates the use of Gated Recurrent Units (GRU), a deep learning model known for its ability to handle sequential data effectively, for heart disorder analysis in a smart healthcare environment powered by the Internet of Things (IoT). IoT-enabled devices, such as wearable sensors, facilitate real-time data collection, then it is processed by the GRU model for accurate prediction of heart disorders. Experimental evaluations on datasets such as UCI, Framingham, Public Health, and real-time IoT data demonstrate that the proposed framework achieves superior performance with 99% prediction accuracy. By addressing challenges like data noise, energy efficiency, and privacy concerns, the framework offers a resilient, scalable, and real-time solution for heart disorder diagnosis, advancing personalized and proactive healthcare solutions.
Predictive IoT-AI Model for Cyber Threat Detection in Smart Healthcare Environments
Authors: Hemalatha K.L , Mohan Pushpa 
Published: January 2025

Abstract
The rise of IoT-based smart healthcare environments has escalated the demand for robust and efficient cyber threat detection mechanisms, given the critical nature of these systems and their susceptibility to evolving cyber-attacks. This research presents the design of a predictive IoT-AI approach for cyber threat detection, leveraging the NSL-KDD dataset for comprehensive training and evaluation. Moreover, the existing methods demonstrates excessive computational time due to its high complexity in feature extraction and sequence modeling. In this study, the proposed model combines Convolutional Neural Network (CNN) layers for spatial feature extraction with a Gated Recurrent Unit (GRU) in the intermediate layers to capture temporal patterns and evolving threat behaviours. The combination of CNN and GRU utilizes the benefits of both models: CNNs for precise feature representation and GRUs for sequence modeling, thereby enabling the identification of sophisticated and emerging cyber threats. This hybrid architecture is optimized to attain high accuracy while retaining computational efficiency, ensuring real-time applicability in IoT-enabled healthcare systems. Through meticulous design and rigorous testing, the proposed algorithm achieved an impressive accuracy of 98%, underlining its capability to effectively and reliably detect a broad spectrum of cyber threats. The experimental results not only validate the efficacy of the CNN-GRU hybrid model but also highlight its scalability and robustness in real-world healthcare IoT applications. This exceptional accuracy underscores the model’s ability as a practical and dependable solution for safeguarding sensitive patient data and critical medical infrastructures against evolving cybersecurity challenges.
Wearable IoT and Artificial Intelligence Techniques for Leveraging the Human Activity Analysis
Authors: Sheker Lina , Petli Vishwanath , Satish Reddy K. 
Published: January 2025

Abstract
The integration of wearable Internet of Things (IoT) devices with advanced Artificial Intelligence (AI) techniques has significantly transformed human activity analysis (HAA), It enables precise, real-time monitoring of diverse physical behaviors. This research delves into the design of an efficient and accurate human activity recognition (HAR) system utilizing the WISDM dataset. The dataset comprises accelerometer and gyroscope data collected from wearable devices during various activities, offering a robust foundation for HAR. Despite advancements, existing systems face challenges in accurately capturing the spatial and temporal dependencies in time-series data, which often leads to suboptimal classification performance. To address this, a hybrid deep learning framework is proposed, combining Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This combination ensures better learning by using CNNs for spatial features and LSTMs for time-based patterns, making activity analysis more accurate. The proposed model attains a remarkable classification accuracy of 99.4%, underscoring its efficacy in distinguishing between various activities in real-time scenarios. Key performance metrics, including Precision (99%), Recall (98.6%), Specificity (99.3%), and F1-Score (99.2%), are systematically evaluated, validating the model’s robustness, reliability, and practical applicability. Experimental results reveal the model’s consistent and exceptional performance, highlighting its superiority over existing state-of-the-art approaches. The findings underscore the practical viability of deploying such intelligent systems in real-world environments, representing a notable progression in the evolution of human activity analysis.
Development of Hybrid Intrusion Detection Systems for IoT Enabled Devices Utilizing Resource Constraint Learning Frameworks
Authors: Rachana P. , Talakoti Mamatha , Surya Narayana Reddy V. 
Published: January 2025

Abstract
The increasing prevalence of IoT-enabled devices in resource-constrained environments has made them vulnerable to various network intrusions and security threats. Traditional intrusion detection systems often struggle with capturing complex temporal dependencies in network traffic and balancing computational efficiency with accuracy, limiting their effectiveness in IoT networks. To address this challenge, we recommend a novel hybrid intrusion detection framework based on a hyperparameter-tuned LSTM-GRU model. The proposed system leverages the strengths of both architectures to efficiently analyse sequential patterns and temporal dependencies in network traffic while maintaining computational feasibility for IoT devices. A grid search method is employed to elevate hyperparameters like learning rate, batch size, and the number of layers, ensuring the model’s performance is fine-tuned for the intrusion detection task. Experimentation outcomes on the CICIDS 2017 dataset depict the model’s robustness, achieving 95% accuracy, 96% precision, 95% recall, 96% F1-score and 95% specificity, significantly outperforming existing approaches. This hybrid architecture offers a practical solution for real-time intrusion detection in IoT systems, balancing computational efficiency with high detection accuracy.
Deep Learning for Sign Language Recognition: A Comparative Review
Authors: Abd Al-Latief Shahad Thamear , Yussof Salman , Ahmad Azhana , Khadim Saif 
Published: January 2025

Abstract
Sign language can be regarded as a unique form of communication method between human beings, which relies basically on visualized gestures of the individual body parts to transfer messages and obtains a substantial role in the life of impaired people having hearing and speaking disabilities deaf. There are various different signs in every sign language with differences in representation using hand shape, motion type, and location of the hand, face, and body portions participate in every sign. Understanding sign language by individuals without disabilities is a challenging operation. Therefore, automated sign language recognition has become a significant need to bridge the communication gap and facilitate the interaction between the deaf society, and the normal hearing majority. In this work, an extensive review of automated sign language recognition and translation of different languages around the world has been conducted. More than 140 research articles have been reviewed, and all of them are relying on deep learning techniques, which were published between 2018 and 2022, to recognize, and translate sign language. A brief review of concepts related to sign language is also presented including its types, and acquiring methods, as well as an introduction to deep learning, and the main challenges facing the recognition process. A description of the various types of public datasets of sign language in different languages is also presented and discussed.
Pengaruh Kemampuan Membaca Pemahaman dan Pemahaman Kalimat Terhadap Kemampuan Memecahkan Soal Cerita Matematika
Authors: Ardianik Ardianik , Utami Sri , Kusmiyati  
Published: January 2025

Sejak dahulu, membaca dan memahami suatu bacaan terbilang cukup sulit. Hal ini bisa terjadi karena seseorang malas untuk sekedar membaca kemudian memahami sebuah bacaan. Namun jika tidak dapat melakukan kedua hal tersebut, kita tidak akan dapat mengerti dan memahami isi dari suatu bacaan yang diberikan. Terlebih jika hal ini terjadi di dalam soal cerita matematika. Semua orang tetap harus mempelajari matematika karena matematika merupakan sarana untuk memecahkan masalah sehari-hari, sama halnya dengan bahasa, membaca, dan menulis. Matematika tidak hanya seputar soal menulis, melainkan memahami konsep dengan membaca dan memahaminya terlebih dahulu. Penelitian ini bertujuan menguji dan membuktikan: (1) Pengaruh kemampuan membaca pemahaman terhadap kemampuan menyelesaikan soal cerita matematika; (2) Pengaruh kemampuan pemahaman kalimat terhadap kemampuan menyelesaikan soal cerita matematika; (3) Pengaruh kemampuan membaca pemahaman dan pemahaman kalimat terhadap kemampuan menyelesaikan soal cerita matematika. Pendekatan penelitian kuantitatif dengan jenis penelitian ex-post facto ini, metode pengumpulan data dengan metode tes dan instrumen penelitian meliputi soal tes membaca pemahaman, soal tes pemecahan kalimat, dan soal tes menyelesaikan soal cerita matematika. Metode analisis data menggunakan regresi linear berganda yang sudah dipenuhi uji asumsi. Hasil penelitian menunjukkan: (1) Terdapat pengaruh kemampuan membaca pemahaman yang sangat signifikan terhadap kemampuan menyelesaikan soal cerita matematika; 2) Terdapat pengaruh kemampuan pemahaman kalimat yang sangat signifikan terhadap kemampuan menyelesaikan soal cerita matematika; 3) Terdapat pengaruh kemampuan membaca pemahaman dan pemahaman kalimat yang sangat signifikan terhadap kemampuan menyelesaikan soal cerita matematika siswa kelas XI Jurusan TKJ dan Jurusan Multimedia SMK UNITOMO Surabaya.
Surgical treatment tactics for osteomas of frontal sinuses in the combined approach of maxillofacial surgeon and otorhinolaryngologist
Authors: Daikhes N.A. , Karayan A.S. , Subbotina N.A. , Shumov K.M. 
Published: January 2025

Due to the widespread use of multispiral computed tomography (MSCT) in clinical practice, the incidence of sinus osteoma has increased. Osteoma is the most common benign tumor of the nose and paranasal sinuses; its most frequent localization is the frontal sinus with transition to the ethmoidal labyrinth. According to some authors, the prevalence of the disease in the population amounts to 1%, according to other authors – 3%. Surgical treatment of osteoma often requires a simultaneous approach involving maxillofacial surgeons and otorhinolaryngologists since the tumor affects adjacent anatomical regions. This article contains criteria for the choice of surgical tactics in different types of osteomas. The article presents clinical cases of osteoma requiring transcoronary access. The pathologic diagnosis of osteoma was confirmed in all mentioned cases.
В связи с широким применением в клинической практике мультиспиральной компьютерной томографии (МСКТ) возросло число пациентов с диагностированной остеомой придаточных пазух носа. Остеома – наиболее распространенная доброкачественная опухоль носа и околоносовых пазух, наиболее частая ее локализация – лобная пазуха с переходом в решетчатый лабиринт. Согласно данным некоторых авторов, распространенность заболевания среди населения составляет 1%, согласно другим авторам – 3%. Хирургическое лечение остеом нередко требует симультанного подхода со стороны челюстно-лицевых хирургов и оториноларингологов, поскольку поражает смежные анатомические области. Данная статья содержит критерии выбора хирургической тактики при различных видах остеом. В статье представлены клинические случаи остеом, требующих выполнения транскоронарного доступа. Патоморфологический диагноз «остеома» подтвержден во всех указанных случаях. Ключевые слова: лобная пазуха, гигантская остеома, фронтотомия, коронарный доступ, фронтит, околоносовые пазухи, дефект орбиты, костные аутотрансплантаты
Experience of clinical application of customized titanium implants created with additive manufacturing methods in maxillofacial surgery
Authors: Ponomarev I.V. , Zakirova A.A. , Svyatoslavov D.S. , Karalkin P.A. , Cherevko E.V. , Alekseeva S.K. , Nebezhev A.A. , Makoveev K.A. , Chuvilin D.Yu. , Samsonov V.V. , Reshetov I.V. , Romanko Yu.S. 
Published: January 2025

Background. Mine blast, shrapnel, and gunshot wounds of the craniofacial region are the most difficult to reconstruct, which is due to both the complex anatomical structure and the need to restore functional abilities. The multifocal nature of the injury requires careful alignment and fixation of the fragments or replacement of whole bone fragments bearing functional and aesthetic significance. The optimal solution to this problem is the use of customized titanium implants created with additive manufacturing methods based on CT and MRI data. The aim of the study was to use customized titanium implants in patients with combat injuries of the maxillofacial region. Clinical observations are presented by the cases of male patients E., 39 years old, and G., 40 years old, who were injured as a result of a combined traumatic factor. Conclusion. Active introduction of 3D modeling and additive technologies into routine medical practice can reduce the duration of surgical intervention, minimize possible complications, increase the effectiveness of treatment and reduce the rehabilitation time for patients at all stages.
Введение. Минно-взрывные, осколочные и пулевые ранения черепно-лицевой области являются наи- более сложными для реконструкции, что обусловлено как сложным анатомическим строением, так и необходимостью восстановления функциональных нарушений. Многооскольчатый характер травмы требует тщательного сопоставления и фиксации отломков, либо замещения целых костных фрагментов, несущих функциональную и эстетическую нагрузку. Наиболее оптимальным решением данной проблемы является использование индивидуальных титановых имплантатов, созданных методами аддитивного производства на основе данных компьютерной и магнитно-резонансной томографии. Целью работы явилось использование индивидуальных титановых имплантатов для пациентов с высокоэнергетическими травмами челюстно-лицевой области. Клинические наблюдения представлены случаями из практики лечения пациентов Е. 39 лет и Г. 40 лет, получивших ранение в результате комбинированного воздействия травмирующего фактора. Заключение. Активное внедрение в рутинную медицинскую практику 3D-моделирования и аддитивных технологий позволит сократить длительность оперативного вмешательства, минимизировать возможные осложнения, повысить эффективность лечения и сократить сроки реабилитации пациентов на всех этапах.
Parallel biobanking of post-COVID-19 cancer patient samples as an innovative route to multiomics screening
Authors: Petersen E.V. , Zorigt D. , Lifanov D.A. , Shabalina E.Y. , Khalil A. , Shiryaev A.A. , Pisareva T.N. , Zhemerikin G.A. , Sukortseva N.S. , Karalkin P.A. , Reshetov I.V. 
Published: January 2025

Compared to the concept of biobanks, which include one type of biomaterials from patients or a library of DNA samples, new types of biobanks shouldn’t be only repositories, but also infrastructure, which allows for innovative and translational research using biomaterials from patients. Also, parallel biobanking, including the simultaneous collection of various types of biomaterials (whole blood, plasma, exosomes, DNA, microRNA, leukocytes, tumor cells and their microenvironment - frozen with preservation of their viability, etc.) will allow them to be used in in-vitro models to test the effects of drugs, as well as to predict the development of treatment-resistant populations of cancer cells and cellular transformation. This biobanking approach opens new opportunities to study precancerous niches, rare forms of cancer and to develop personalized therapeutic strategies, giving researchers new opportunities for in vitro recapitulation of tissue mechanical and molecular changes, changes of signaling molecules profile and secretome.
По сравнению с традиционным представлением о биобанках, включающих в себя один тип заготовляемых биоматериалов пациентов или коллекцию образцов ДНК, биобанки нового типа должны представлять собой не только репозитории, но и инфраструктуру, которая позволит проводить трансляционные и поисковые исследования с использованием биоматериалов от пациентов. Также, паралельное биобанкирование, включающее одновременный забор различных типов биоматериалов (цельная кровь, плазма, экзосомы, ДНК, микроРНК, лейкоциты, замороженные с сохранением своей жизнеспособности клетки опухолей и их микроокружения и т.д.) позволит использовать их в in vitro моделях для проверки действия лекарственных препаратов, а также для прогнозирования развития резистентных к лечению популяций раковых клеток и клеточной трансформации. Такой подход к биобанкированию открывает новые возможности для изучения предраковых ниш, редких форм рака и разработки персонализированных стратегий лечения, также обеспечивая возможность воспроизведения in vitro механической и молекулярной перестройки тканей, изменение профиля сигнальных молекул и секретома клеток.
Combined treatment of recurrent laryngeal papillomatosis in adults using a CO2 laser and photodynamic therapy
Authors: Nazhmudinov I.I. , Guseynov I.G. 
Published: January 2025

The aim of the study was to evaluate the results of a combined treatment for recurrent laryngeal papillomatosis in adults using two stages: initial removal of papillomas with a CO2-laser followed by photodynamic therapy. Material and мethods. Between 2019 and 2021, 75 adult patients with recurrent laryngeal papillomatosis were treated using this new treatment approach. The treatment outcome was assessed using a scale for evaluating the severity of recurrent laryngeal papilloma proposed by the authors. Results. 72 (96%) patients showed a decrease in the prevalence score, indicating a reduction in the total number of papillomas. 3 (4%) patients had no change in the prevalence of papillomas in their larynx. Conclusion. The use of a combined treatment method for recurrent laryngeal papillomatosis is an effective and promising approach, with the potential to significantly improve patient quality of life and reduce the severity of laryngeal lesions.
Цель исследования: изучение результатов комбинированного лечения рецидивирующего папилломатоза гортани (ПГ) у взрослых путем двухэтапного лечения с удалением папиллом с помощью СО2-лазера как первый этап лечения и последующей фотодинамической терапией. Материал и методы. С применением нового метода с 2022 г. пролечены 75 больных, результат лечения которых оценивался по шкале оценки распространенности рецидивирующего ПГ. Результаты. У 72 (96%) пациентов достигнуто снижение балльной оценки по шкале распространенности, что говорит об уменьшении общего числа папиллом, у 3 (4%) пациентов число папиллом в гортани не изменилось. Заключение. Применение комбинированного метода лечения рецидивирующего ПГ является эффективным и перспективным методом лечения, при правильном применении которого можно добиться значительного улучшения качества жизни пациентов и снижения степени папилломатоз-ного поражения гортани.