bluuebunny/crossref_metadata_2025 huggingface dataset:
Export Trends of Jute and Its Derivatives
Authors: Naveen Jose  , Vidya Bhushan Shambhu  , Sanjoy Debnath  , Prateek Shrivastava  , Dinesh Babu Shakyawar
Published: January 2025

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Farm Machinery-based Entrepreneurship Development
Authors: P K Sahoo  , Satish Devram Lande
Published: January 2025

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THE INTEREST RATE IN KEYNES, OR HOW TO FALL INTO A FATAL ERROR. PART I
Authors: Gallego Morales Daniel José , Alonso Neira Miguel Ángel , Romero Ania Alberto
Published: January 2025

La ideología se ha convertido en un concepto capital en la Filosofía política, la Sociología, la Economía y el Derecho. Desde los años treinta del siglo pasado, las humanidades prácticas han dejado de entenderse como disciplinas estrictamente científicas, pasando la ideología a ocupar en ellas un papel fundamental. De hecho, cada vez se es más consciente de que la palabra “ciencia” y los métodos de las ciencias positivas se usan en las disciplinas prácticas para revestir de credibilidad un fundamento ideológico. Los mismos
MODEL PENDIDIKAN AGAMA ISLAM MELALUI INTEGRASI DAN INTERKONEKSI
Authors: Faridi Kaharuddin, Tobroni, , Tobroni Tobroni , Faridi Faridi
Published: January 2025

Artikel ini membahas tentang model pendidikan agama Islam (PAI) melalui integrasi dan interkoneksi. Pendidikan Agama Islam (PAI) memiliki peran strategis dalam membentuk karakter dan kepribadian peserta didik yang sejalan dengan nilai-nilai Islam. Dalam konteks globalisasi dan kompleksitas tantangan zaman, model pendidikan PAI memerlukan pendekatan yang lebih holistik. Artikel ini membahas konsep integrasi dan interkoneksi dalam pendidikan PAI sebagai upaya inovatif untuk mengatasi fragmentasi ilmu dan memperkaya wawasan peserta didik. Pendekatan integrasi menghubungkan nilai-nilai agama dengan ilmu pengetahuan kontemporer, sedangkan interkoneksi menjembatani antara tradisi keislaman dengan kebutuhan praktis kehidupan modern. Model ini bertujuan untuk menciptakan peserta didik yang tidak hanya memahami ajaran agama secara mendalam, tetapi juga mampu menerapkannya dalam konteks sosial, budaya, dan teknologi. Artikel ini menyajikan analisis konseptual tentang potensi, tantangan, dan strategi implementasi model integrasi dan interkoneksi dalam pendidikan PAI. Dengan demikian, tulisan ini diharapkan menjadi kontribusi teoretis yang bermanfaat bagi pengembangan kurikulum dan metode pembelajaran PAI yang relevan dengan kebutuhan zaman
Hemşirelik Öğrencilerinin Toplumsal Cinsiyet Algısı ile Evlilik Dışı Cinsel Yakınlığa Yönelik Tutumlarının Belirlenmesi: Kesitsel Çalışma
Authors: Cirban Ekrem Ebru , Küçükkaya Burcu
Published: January 2025

Amaç: Bu çalışmada, hemşirelik bölümü öğrencilerinin toplumsal cinsiyet algısı ile evlilik dışı cinsel yatkınlığa yönelik tutumunun incelenmesi amaçlanmıştır.
Metot: Kesitsel tipte olan araştırmanın evrenini Ekim 2023-Mart 2024 tarihleri arasında …… Üniversitesinin Sağlık Bilimleri Fakültesi Hemşirelik Bölümü’nde eğitim gören 431 öğrenci oluşturmuştur. Araştırma 353 öğrenci ile tamamlanmıştır. Araştırmanın verileri Tanıtıcı Bilgi Formu, Toplumsal Cinsiyet Algısı Ölçeği (TCAÖ) ve Evlilik Dışı Cinsel Yatkınlığa Yönelik Tutum Ölçeği’ni (EDCYYTÖ) içeren bir veri toplama formu ile toplanmıştır.
Bulgular: Öğrencilerinin yaş ortalaması 20,65±1,69, %68,0’ının kadın ve %29,7’si birinci sınıfta eğitim görmektedir. Öğrencilerin, TCAÖ toplam puan ortalaması 100,43±18,36 ve EDCYYTÖ toplam puan ortalaması 31,29±6,89 olarak bulunmuştur. Öğrencilerin yaşı ile eğitim gördükleri sınıf (r=,726, p=,000) ve EDCYYTÖ (r=,213, p=,000), sınıf ile TCAÖ (r=,116, p=,029) ve EDCYYTÖ (r=,199, p=,000) arasında anlamlı ilişki saptanmıştır. Cinsiyet, sınıf, anne eğitim durumu, cinsel yakınlık durum, toplumsal cinsiyet bilgisi olma durumu gibi değişkenlerle TCAÖ ve EDCYYTÖ toplam puan ortalamaları farklılık göstermektedir.
Sonuç: Bu çalışmada öğrencilerin toplumsal cinsiyet algılarının ve evlilik dışı cinselliğe yönelik tutumlarının olumlu olduğu belirlenirken, toplumsal cinsiyet algıları ile evlilik dışı cinselliğe yönelik tutumları arasında ilişki saptanmamıştır. Bu sonuçlar doğrultusunda, kadın sağlığı ve hastalıkları hemşireliği alanında çalışan uzman profesyonellerin farklı örneklem gruplarında, bölgelerde ve araştırma tiplerinde çalışmalar yapması önerilmektedir.
Gebelerin Non-Stress Test İşlemine Yönelik Deneyimleri: Nitel Bir Çalışma
Authors: Şen Aytekin Melek , Kahraman Ayşenur , Alparslan Özgür
Published: January 2025

Amaç: Araştırmada mevcut gebeliğinde ilk kez NST çektiren gebelerin NST’ye yönelik deneyim ve beklentilerinin belirlenmesi, fiziksel koşulların NST deneyimine etkisini belirlemek amaçlanmıştır.
Yöntem: Araştırma nitel desende ve 01.02.2024-01.04.2024 tarihleri arasında yürütülmüştür. Amaçlı örnekleme yöntemi ile toplam 15 gebe ile derinlemesine görüşmeler yapılarak araştırma tamamlanmıştır. Araştırma verileri Gebe Bilgi Formu, Yarı Yapılandırılmış Görüşme Formu ve Gözlem Formu ile toplanmıştır. Verilerin içerik analizine başvurulmuştur. Araştırma için etik kurul izni ve kurum izni alınmıştır.
Bulgular: Araştırmada gebelerin ifadesi doğrultusunda NST’ye yönelik 3 tema belirlenmiştir.
Bu temaları “NST bilgisi Teması”, “NST’ye yönelik deneyim teması” ve “NST’ye yönelik beklenti teması” oluşturmaktadır. Her ana tema alt tema elde edilen kodlara göre alt temalar altında sınıflandırılmıştır. NST’ye yönelik deneyim teması ve NST’ye yönelik beklenti temasında literatürle benzer doğrultuda gebelerin muayene ortamına yönelik, sağlık personeline yönelik beklentilerinin olduğuna ilişkin veriler elde edilmiştir.
Sonuç: Gebelerin NST işlemi ile ilgili eksik olsa da doğru yönde bilgilerinin olduğu görülmüştür. Ayrıca gebelerin işlem ile ilgili temizlik beklentisi, işlemlerin yapıldığı odalarda aktif kullanılan perde/paravan olması ya da mümkünse tek kişilik olması, eş desteğinin de sağlanması beklentisi bulunmaktadır.
UJI IRITASI DAN UJI EFEKTIVITAS SEDIAAN WHITE ACTIVATED CARBON SERBUK KAYU ULIN (Eusideroxylon zwageri) SEBAGAI AGEN ADSORBEN KERINGAT PADA KULIT KETIAK
Authors: Rahmah Indah Noor , Nuraji Tara Pramesti , Rizkiyah Noor Annisa , Salsabilla Risma Zahra , Salwa Helmina , Triyasmono Liling
Published: January 2025

White Activated Carbon serbuk kayu ulin (Eusideroxylon zwageri) merupakan terobosan yang digunakan dalam penyerapan keringat pada kulit ketiak penyebab bau badan dengan menunjang kemudahan dalam pemakaian. Penelitian bertujuan untuk mengetahui aktivitas iritasi primer, uji hedonik, dan efektivitas White Activated Carbon serbuk kayu ulin sebagai agen penjerap keringat berlebih pada kulit ketiak. Metode penelitian yang digunakan adalah metode uji tempel untuk uji iritasi, uji hedonik, dan uji efektivitas. Uji iritasi dengan hewan uji kelinci albino, sedangkan uji hedonik dan uji efektivitas menggunakan alat skin analyzer pada responden manusia. Hasil penelitian menunjukkan bahwa nilai indeks iritasi primer sebesar 0,22, uji hedonik pada responden menunjukkan kategori “suka” untuk bau, warna, bentuk, dan kesukaan setelah diaplikasikan, dan hasil uji efektivitas pada uji Paired T-Test diketahui bahwa nilai sig≤0.05, artinya terdapat penurunan jumlah keringat setelah penggunaan White Activated Carbon pada kulit ketiak.
EXPLORATION OF CHITINOLYTIC MICROORGANISMS FROM SEAWATER AS ANTI-CANDIDIASIS AGENTS USING FUNCTIONALIZED CHITIN SUBSTRATE DERIVED FROM GREEN MUSSEL WASTE
Authors: Adnyanaschah Rahadhyan , Ismail Dzava Prawinsyah Fairus , Mahardika Bintang Satrio , Putri Yolla Adellia , Shafa Nafis , Rostinawati Tina
Published: January 2025

Candidiasis is a Candida sp. infection (Candida albicans in common) that happens in nails, hairs, skin, and oesophagus, with around 20-25% prevalence in Indonesia. Chitin is one of the main components of its cell wall and serves as a target for candidiasis therapy using natural degradant compounds, such as chitinase enzyme. Chitinase enzyme is collected from chitinolytic bacteria found massively in chitin-rich environments like seawater. The screening is needed to identify the varieties of bacteria with chitinolytic activity. The screening was done using green mussel waste (Perna viridis), which contains 14-35% chitin and reached 10.776,75 tons of trash in 2020. This research aims to get a chitinolytic bacteria using green mussels as a chitin substrate source and a chitinolytic bacteria with anti-candidiasis activity. This research was conducted by isolating chitin from green mussel shells, chitinolytic bacteria screening, and anti-candidiasis activity test. Results collected from 5.00, 5.08, and 5.18 km isolates were the Vibrio alginolyticus, Vibrio neocaledonicus, and Marinobacter persicus species, which had a chitinolytic index of 1.246, 1.560, 1.492 also 64.29; 61.30; 69.01 U/ml chitinase enzymatic activity. The highest anti-candidiasis activity was found in a sample 5,18 km from the coast of Kejawanan Beach based on the number of chitinase enzyme activity and the chitinolytic index.
THE EFFECT OF SUGAR CONCENTRATION IN KOMBUCHA, KEJEK TEA AND ASAM KANDIS (Garciniaxanthochymus) ON ANTIOXIDANT ACTIVITY, TOTAL ALCOHOL AND TOTAL ACID
Authors: Soni, M.Farm. Dang , Abdillah Muhammad Nur , Junaedi Effan Cahyati , Panjaitan Destrien Resmita
Published: January 2025

This study aims to determine the effect of sugar concentration and the addition of asam kandis on antioxidant activity, total alcohol and total acid in kombucha drinks. The test began with a test of the characteristics of the simplicia phytochemical screening. The antioxidant activity was then examined using the DPPH method, the total alcohol content was determined using gas chromatography, and the total acid test was performed using titration. The results of the simplicia characteristic test showed that the simplicia used had met the standards of the Indonesian herbal pharmacopoeia, and the results of the phytochemical screening test showed that only saponins did not show their presence, both kejek tea, asam kandis and a combination of both. The results of the DPPH test showed that the sample with optimal antioxidant activity was in Formula 4 with a percentage inhibition value of 82% ± 0.08, total alcohol of 0.09% ± 0.12 and total acid of 1.57% ± 0.17. This study also proves that the combination of kejek tea, kandis acid and higher sugar content can increase antioxidant activity, total alcohol and total acid, but the total alcohol and total acid are still below the safe limit.
Effectiveness of Localized Radio-Based Instruction as an Aid to Modular Distance Learning
Authors: C. F. Algar, , A. T. Binongo, , M. A. B. Villarubia, , D. M. Ortega,
Published: January 2025

The study aims to assess the effectiveness of localised radio-based instruction as an aid to modular distance learning based on the academic performance of grade 9 students at Tambulig National High School, Zamboanga del Sur Division. The study was conducted during the Academic Year 2021-2022 amidst the pandemic. First Quarter grades of the Grade 9 students were gathered online. The study was quantitative, using a descriptive research design. Sampling methods targeted two groups: students who used RBI and non-RBI students. In determining the 84 students who used RBI, purposive sampling was used. Systematic random sampling was also used to get the 84 samples from the 239 non-RBI students. The majority of the RBI students (91.67%) have a very satisfactory to outstanding performance compared to the 75.19% of the non-RBI. Based on the result of a weighted average of both Non-RBI and RBI groups (85 and 87) implies that the students who availed of the Radio-Based Instruction have better performance in Araling Panlipunan compared to those who relied only on the learning modules. Based. On the mean, LRBI students have higher performance than the non-RBI. In the new normal education, the students were able to learn and perform better in school when they availed of the Localized Radio-Based Instruction. Students prefer to listen to radio-based instruction. It supplements the information provided by the modules, hence improving students’ academic performance. Using local radio-based lessons is an effective way to bridge the digital divide in education., foster learner-centred approaches, and reach out to the most marginalised students.
Analysis of traces of the transferred paints by X-ray Fluorescence and UV-Vis spectroscopy to identify the suspect's vehicle for the Forensic science purpose: A Case Study
Authors: Bhoir Bhaskar T. , Jirimali Harishchandra D. , Navale Vijaykumar M. , Deonikar Shubhangi N. , More Bhausaheb P.
Published: January 2025

The analysis of medico legal cases for forensic science aspect, paint from the suspect's and the victims vehicle can be matched, identified and convictions can be made. In the present case the X-ray fluorescence (XRF) and UV-Vis spectroscopic analysis was carried out for the identification of the suspect's vehicle. The XRF analysis shows the different elemental composition of the paint from the victims vehicle and suspects vehicle. The composition of the paint transferred on the victims vehicle is identified by matching the XRF spectrum. Further confirmation of transfer of paint was carried out by UV-Vis spectroscopy. Both the methods of analysis confirms the paint transfer on the victims vehicle is from the suspect's vehicle. In this way the conclsion can be taken that the involvement of the suspects vehicle in hit and run case and sspect can be identified.
Importance of GC-MS for Analyzing Sars-Cov-2 Drugs in Biological Sample
Authors: Fatima Mutawaqila Juveria , C Parthiban
Published: January 2025

The advent way of the SARS-CoV-2 virus has put a lot of pressure on the actual way of developing drugs and diagnostics. Gas Chromatography-Mass Spectrometry (GC-MS) is rapidly becoming one of the most valuable techniques in detecting and identifying drugs related to COVID-19 in biological samples. This method integrates the separation capabilities of gas chromatography with the identification power of mass spectrometry, enabling the analysis of volatile and semi-volatile compounds. Through GC-MS, researchers can identify trace elements in complex biological matrices, such as serum, urine, and exhaled breath, which is crucial for monitoring drug concentrations and understanding pharmacokinetics. Notably, GC-MS has been instrumental in detecting antiviral drugs like chloroquine and hydroxychloroquine, as well as identifying volatile biomarkers associated with COVID-19 infection. The application of GC-MS extends beyond drug analysis; it also plays a significant role in early diagnosis by analyzing breath samples for specific metabolites linked to SARS-CoV-2. This paper discusses the importance of GC-MS in SARS-CoV-2 research, highlighting its applications in drug detection, early diagnosis, and public health management.
СВОЄРІДНІСТЬ ДІАЛОГУ З БІБЛІЄЮ В АПОКРИФІ ЛЕСІ УКРАЇНКИ «ПРОКЛЯТТЯ РАХІЛІ»
Authors: ЖВАНІЯ Л. В.
Published: January 2025

Розвідка є спробою інтерпретації авторського апокрифу Лесі Українки «Прокляття Рахілі» в біблійному контексті, що дозволило відчитати нові сенси, які відкриваються у кореляції твору з текстом Святого Письма. Зазначено, що у творі Лесі Українки спостерігається зміщення акцентів з однієї з центральних містичних подій Євангелія – боговтілення до природніх переживань матері Марії за свою дитину, прагнення захистити її від переслідувань. Ще однією специфічною рисою апокрифу стає переміщення авторського фокусу уваги зі стосунку Небесний Отець – Син, постульованого в Євангелії, на зв’язок матері й сина. В апокрифі Лесі Українки використовуються такі, підкреслено зневажливі, найменування Ісуса як «син вбогої Марії», «сей, Марії син», що призводить до профанації образу Ісуса. Образ Богоматері у тексті апокрифу також десакралізовано. Авторка окреслює емоційний стан Марії у суто людських, егоїстичних вимірах, підкреслюючи, що її почуття нічим не відрізняються від емоцій інших матерів, які також пишаються своїми дітьми. Підкреслюється, що в інтерпретації Лесі Українки образ Рахілі постає втіленням темного, деструктивного аспекту материнського архетипу, що проявляється у жорстокості, мстивості, агресії. Стверджуючи, що смерть Ісуса даремна, героїня апокрифу демонструє повну нівеляцію основних засад християнства. Праматір Ізраїлю апелює до принципу кровної помсти, згідно якого вбивство вимагає відплати. Прокляття, виголошене в апокрифі Рахіллю, з християнської точки зору постає страшним гріхом, адже це злісне побажання смерті спрямоване проти Божого Сина. Виснувано, що апокрифічна модель у творі «Прокляття Рахілі» базується на онтологічних засадах відмінних від Євангельських. Сюжет твору розвивається в межах Старозавітної етичної парадигми, що передбачає верховенство Закону – зовнішнього імперативного припису, порушення, невиконання якого веде до покарання.
ВПЛИВ СОЦІАЛЬНИХ МЕРЕЖ НА СУЧАСНИЙ ЛЕКСИКОН АНГЛІЙСЬКОЇ МОВИ
Authors: ЗУБЕНКО О. В.
Published: January 2025

Сучасний світ переживає стрімкий розвиток цифрових технологій та комунікацій, які стали невід’ємною частиною повсякденного життя. Одним із найвпливовіших аспектів цифрової революції є соціальні мережі, які об’єднують мільйони користувачів по всьому світу. Зростання їхньої популярності суттєво вплинуло не лише на соціальну взаємодію, а й на мовні особливості багатьох культур, включаючи англомовну. Англійська мова, як глобальна мова міжнародного спілкування, найбільше піддається впливу змін, що йдуть від соціальних мереж. Мета статті полягає в аналізі впливу соціальних мереж на формування сучасного лексикону англійської мови та визначенні основних тенденцій у появі нових слів і виразів під впливом цифрових комунікацій. Предмет дослідження – лексичні зміни в англійській мові під впливом соціальних мереж. Об’єкт дослідження – сучасна англійська мова в умовах цифрової комунікації, зокрема в межах таких соціальних платформ, як Twitter, Instagram, TikTok та Facebook, які впливають на формування та поширення нових лексичних одиниць. Матеріалом дослідження слугують лексичні одиниці сучасної англійської мови, які з’явилися та поширилися завдяки соціальним мережам. Аналіз таких популярних платформ дозволив нам лексичні зміни умовно поділити на кілька груп: неологізми, скорочення та акроніми, фразеологічні одиниці та меми, хештеги. Зміни в сучасному лексиконі англійської мови виконують кілька важливих ролей: адаптація до нових реалій і технологій, швидка і ефективна комунікація, поширення культурних та соціальних трендів, взаємодія з глобальним контекстом, креативність і розвиток мови, а також вплив на традиційну мову. Проте нові лексичні одиниці можуть як швидко втрачати актуальність або значення, так і залишатися в лексиконі і продовжувати використовуватися, навіть коли їхнє первинне значення може трохи змінитися. Мовні новотвори є свідченням того, як цифрова культура не лише змінює комунікаційні практики, але й стає невід’ємною частиною мовної еволюції.
Fuzzy Baer Subrings: A Fuzzified Extension of Baer Rings
Authors: Dinesh Kute
Published: January 2025

In classical ring theory, a ring  is classified as a Baer ring if, for any subset  the left (or right) annihilator  is generated by an idempotent element in  This paper introduces the concept of fuzzy Baer subrings, extending the principles of Baer rings to the fuzzy setting by defining a fuzzy subset generated by an element and utilizing fuzzy left and right annihilators. Additionally, we develop the theory of fuzzy Rickart subrings by introducing the concept of fuzzy points, establishing that each fuzzy Baer subring inherently qualifies as a fuzzy Rickart subring. Further contributing to fuzzy algebra, we define fuzzy idempotent subrings, capturing broader generalizations in the fuzzy context. This research bridges classical and fuzzy set theories by adapting Baer rings, Rickart rings, and idempotent rings within the framework of fuzzy algebra.
Deep CNN-Based Numerical Modeling for Optimizing Mathematical Models in Brain Image Segmentation and Classification
Authors: Shruti Thapar
Published: January 2025

This research investigates the application of convolutional neural networks (CNNs) for the segmentation and classification of brain tumors using the Brain Tumor Segmentation (BRATS) dataset, a comprehensive collection of annotated MRI scans. The objective was to develop and optimize a CNN model capable of accurately identifying and delineating various tumor regions within the brain to support clinical decision-making and treatment planning. The methodology involved preprocessing the MRI data for standardization and enhancement through normalization and augmentation techniques such as rotation, scaling, and elastic deformation. This was followed by designing a CNN architecture that includes convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. The model's training was guided by a combination of dice coefficient and cross-entropy loss functions, with hyperparameters finely tuned through methods such as grid and random search to achieve optimal performance. The resulting model demonstrated high accuracy, achieving an overall accuracy of 93.5% with a Dice coefficient of 89.7%. Class-specific performance metrics indicated particularly effective segmentation of the whole tumor region. Data augmentation significantly improved the model's robustness and ability to generalize across unseen data, a crucial aspect for clinical applications. The findings underscore the potential of advanced deep learning techniques in medical imaging, particularly in enhancing the precision and efficiency of brain tumor diagnostics. This research paves the way for further exploration into real-time applications and integration into clinical workflows, highlighting the importance of aligning AI developments with clinical needs. Future work will focus on refining the model's architecture and exploring its applicability to other imaging modalities and tumor types.
Fuzzy Double Gradation of Openness
Authors: Sowmya.R
Published: January 2025

This paper explores the concept of fuzzy double gradation of openness, a framework that extends traditional binary classifications to accommodate the complexity of real-world scenarios. By introducing a two-dimensional approach to openness, this framework allows for a more nuanced analysis of systems and processes, especially in environments characterized by uncertainty and ambiguity. The paper discusses the theoretical foundations of fuzzy logic and its application to the double gradation of openness, highlighting its advantages over conventional models. Case studies from various disciplines are presented to demonstrate the practical utility of this approach. The findings suggest that fuzzy double gradation provides a more accurate and flexible tool for decision-making, making it a valuable addition to the analytical toolkit for researchers and practitioners alike. A theory, Fuzzy Double Gradation of Openness, Closedness are initiated and analyzed.
Optimized Privacy Preserved Itemset Mining using Federated Learning from Transactional Data in Data Mining
Authors: Raja Rao Budaraju
Published: January 2025

Federated learning allows you to train a global machine-learning model without requiring you to move data from one location to another. This is especially important for applications in the healthcare industry, where data is full of sensitive, personally identifiable data, and data analysis techniques need to demonstrate that they adhere to legal requirements. The created machine learning model or the model variables that are made public during training can still be the target of privacy attacks, even when federated learning forbids the sharing of raw data. In this research, we first present an embedding model for the transaction classification job based on federated learning. Transaction data is viewed by the model as a collection of frequent item sets. After that, by maintaining the contextual relationship between frequent item sets, the algorithm can learn low-dimensionality continuous matrices. We conduct a thorough experimental investigation on a large volume of high-dimensional transactional data to validate the created models that incorporate federated learning and attention-based techniques. Our investigations demonstrate how the categorization might aid in the design of federated learning systems. We provide the design considerations, case cases, and prospects for future research by methodically summarising the current federated learning systems.
Enhancing Sustainability in Agriculture using Hybrid Grey Wolf and Cuckoo Search Algorithm
Authors: V. Vasudevan
Published: January 2025

To increase production and efficiency, modern agriculture makes use of the latest innovations including artificial intelligence (AI), cloud/fog/edge (CFE) computing, and agricultural robots. However, the data transfer and center operations of traditional cloud-based systems result in considerable CO2 emissions. To combat this, reducing CO2 emissions from microgrids in agriculture is vital for sustainable farming practices. To address this, an Integrated Grey Wolf and Cuckoo Search (IGWCS) architectural paradigm is proposed for energy-efficient smart agriculture. This paradigm employs a hierarchical approach, utilizing edge, fog, and cloud layers for real-time data processing. By distributing processing tasks, energy consumption is reduced. However, the challenge of energy-efficient offloading of IoT applications remains NP-hard. Hence, a heuristic algorithm, IGWCS-CFE, is developed. This heuristic leverages the combined optimization power of Grey Wolf and Cuckoo Search algorithms to efficiently allocate tasks in the CFE algorithm. It provides fast, practical solutions and validates results obtained from Mixed-Integer Linear Programming (MILP) approaches, particularly vital for large-scale problems where MILP is impractical. By minimizing energy consumption, IGWCS-CFE indirectly reduces CO2 emissions associated with non-clean energy sources used in cloud data centers. Thus, the proposed approach not only enhances energy efficiency but also contributes to mitigating the environmental impact of agricultural operations. Through this integration of heuristic algorithms and advanced architectural design, IGWCS-CFE strives to optimize smart agriculture systems for sustainability and productivity in the face of evolving technological demands and environmental challenges. Based on data from agriculture sensors, proposed algorithm surpasses traditional cloud-based systems by lowering energy consumption and carbon emissions. Due to high CO2 emissions from microgrids, this study advocates for utilizing the IGWCS paradigm in agriculture. This approach employs IGWCS-CFE heuristic algorithm to lower energy consumption and environmental impact. By optimizing productivity through efficient task allocation, it aims to mitigate the environmental footprint associated with traditional cloud-based systems. This integrated strategy promises to enhance sustainability while maintaining high levels of agricultural productivity.
Advanced Network Threat Detection Using Deep Learning Models
Authors: Y. N. Thakare
Published: January 2025

Object detection and recognition are important tasks in computer vision and are widely used in many fields. This article provides an overview and comparison of the main technologies used in the mission to detect, recognize and follow the changes transferred from modern methods to modern deep learning, starting from the historical background. This study compares and contrasts popular techniques such as field detectors, single-shot devices, and two-phase detectors, highlighting their advantages and disadvantages. In addition, recent developments are discussed, including one-off studies of monitoring systems and reform strategies, combining existing research and analysis control examples. This article is a useful resource for researchers to research and search for information.
Optimizing Asset Turnaround in Indian Railways: A Machine Learning and Big Data Analytics Approach
Authors: Sanjay Priyadarshnam
Published: January 2025

This research investigates the integration of machine learning and big data analytics to enhance asset turnaround processes in the Delhi Division of Indian Railways. Addressing key operational inefficiencies, the study presents a data-driven model aimed at optimizing crucial performance indicators such as turnaround time, asset utilization, maintenance expenses, and overall operational efficiency. Through the analysis of both historical and real-time data, the model demonstrates significant improvements, achieving a 30.56% reduction in average turnaround time, a 25% increase in asset utilization, and a 21.43% enhancement in operational efficiency. Furthermore, the study reports a 30.77% improvement in predictive maintenance accuracy, resulting in a 40% reduction in unscheduled downtime and a 25% decrease in maintenance costs. These results highlight the potential of modern technologies to transform railway operations by delivering a more efficient, cost-effective, and reliable system.
The study emphasizes the scalability and adaptability of this approach, suggesting its application across other transportation sectors where similar operational challenges exist. In addition to its immediate contributions to railway operations, the research points to future directions involving the integration of the Internet of Things (IoT) and advanced machine learning techniques. These innovations could further enhance predictive maintenance capabilities, contributing to greater sustainability and long-term efficiency in railway systems. By demonstrating the practical benefits of these technologies, the study underscores their role in revolutionizing transportation infrastructure, particularly in developing nations, where optimizing existing assets is critical for growth and modernization.
Using Genetic Methods to Solve Nonlinear Retrogression Analysis
Authors: Iraq T. Abbas
Published: January 2025

Whilst mathematical approaches in order managing Non-linear The reduction Using the Further Effective Genetic Algorithm (EPNGA) to Estimation of the following factors are always available, the application of algorithmic evolution (EAs) In order these kinds of issue offer an outline In order dealing with a broad range of Multi-Objective Conflicts (MOPs). Non-linear Retrogression A factor estimation using generalized estimating equations It is possible to synthesize a Genetic Algorithm (GA) by using this method. Since the Tchebycheff Strategy is used in the leader recruitment procedure, addressing the multi-objective problem (MOP) in the context of the Genetic Algorithm (GA) at the identical time may result in quick results.
When building the leader's library, predominance is crucial since it allows the selected leaders to include less dense areas, which in turn reduces international optimization challenges and produces a more varied Pareto front approximation. Six non-linear common functions were used to arrive at this conclusion. GA seems to be effective than combined BAT and PSO. All of the results were produced using MATLAB (R2020b).
Optimized Web Server Attack Detection: A Super Learner Ensemble Model Approach
Authors: Sainath Patil
Published: January 2025

Web applications are essential for many organizations, yet they are vulnerable to various security threats, such as injection attacks and inadequate authentication mechanisms. To address these risks, this study proposes a super learner ensemble learning model that combines multiple machine learning (ML) algorithms to improve web server attack detection. Leveraging the unique strengths of each base ML model, the super learner approach enhances predictive accuracy by using a meta-model trained on out-of-fold predictions from base learners, achieving superior performance in identifying attacks. The proposed model was evaluated on the UNSW-NB 15 and KDD CUP 99 datasets, achieving impressive detection accuracies of 99.69% and 99.90%, respectively. This ensemble model effectively addresses challenges in cybersecurity, such as high false-positive rates and imbalanced data, by employing adaptive synthetic sampling and feature selection. Comparative analysis reveals that the super learner model outperforms existing detection methods, improving detection accuracy by up to 9.54%. These findings suggest that the super learner ensemble approach is a promising method for enhancing the security of web applications. Future work could expand on these results by exploring different base models, datasets, and real-time anomaly detection mechanisms to further improve web server protection.
Reversible Geodesics of a Finsler Space with (α,β)-Metric
Authors: Rinkal
Published: January 2025

This paper deals with the existence of reversible geodesics over a Finsler space with some (α,β)-metrics. The conditions for a Finsler space (M,F) to be with reversible geodesics are obtained. We study some geometrical properties of F with reversible geodesics and prove that the Finsler metric F induces a weighted quasi-metric d_F on M.
Enhanced Generative Adversarial Network for Medical Image Inpainting using Automatic Multi-Task Learning Approach
Authors: Poonam L Rakibe
Published: January 2025

Disease categorization, severity confirmation, and future hazard avoidance based on patient medical visual data require an automated Computer Aided Diagnosis (CAD) system. However, distorted medical images can greatly damage diagnosis. Thus, medical image classification requires enhancing diagnostic imaging accuracy and reconstructing damaged regions. These issues have lately received attention in the context of medical image inpainting using Deep Learning (DL) methods. The recent Enhanced Generative Adversarial Networks (EGANs) have proven effective image inpainting solutions, but suffered from the complex and non-optimized training process due to mode collapse and non-convergence. In this paper, Optimized Multi-Tasking GAN (OMT-GAN) automatic medical image inpainting model for performance enhancement is proposed. As the name suggests, the OMT-GAN consisted of three individual tasks for the medial image inpainting such as image edge detection, image completion, and organ boundary generation. A lightweight OMT-GAN model that consists of optimized GAN models is designed in this paper. The conventional GAN models optimized in this paper reduce the complexity and enhance the performances by altering the encoder and decoder layers. Training and evaluation of the OMT-GAN model is done on publicly available medical image. Compared to state-of-the-art approaches, the proposed OMT-GAN had superior Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Universal image Quality Index (UQI). The SSIM, PSNR, and UQI is improved by 5.31 %, 4.72 %, and 4.67 % compared to existing methods.
Exploring the Babylonian Method and Trigonometric Approach for Square Root Calculation a Python-Based Analysis
Authors: M. Venkatachalapathy
Published: January 2025

This paper explores various applications for solving square root values using the Babylonian method. By thoroughly analyzing this ancient method, we have derived a formula for calculating square roots, which is demonstrated and verified through Python programming. This formula allows for the computation of square roots for any number, both rational and irrational. While the exact square root cannot always be expressed through a simple formula, this study draws connections between the Babylonian method and trigonometric functions to create an effective approach for square root approximation. We demonstrate the practicality of this approach through Python, providing a computational tool based on this historical technique.
Blockchain Technology Based Traceable and Safe Pharmaceutical Supply Chain Framework: An Effective Method for Searchable Pharma Chains
Authors: Vijay U. Rathod
Published: January 2025

The paper explores the potential applications of emerging technologies to develop a blockchain-constructed pharmaceuticals supply chain traceability organization. In essence, a blockchain-based medication tracking system is vital for both government regulation and corporate operations. A reliable drug traceability system would make it simple for patients and those working in the drug supply chain to determine where the drug disappeared or has been.  The drug traceability system is designed according to a number of concepts. First and foremost, the blockchain system for drug traceability must to accurately represent the supply chain's actual drug transaction logic, particularly with regard to drug-related data. Second, privacy and authenticity should be guaranteed by the drug traceability system. We propose Hyperledger Fabric, a potential blockchain-based decentralized architecture, to fulfill important medication traceability requirements, such as privacy, trust, openness, security, authorization and authentication, and scalability. We suggest, talk about, and contrast two possible drug traceability architectures: centralized and decentralized. We enumerate and discuss some open research questions related to medication tracking using blockchain technology. The pharmaceutical business can benefit from the suggested blockchain architectures.
Ensemble Learning Based Defect Detection Systems for Industrial Applications
Authors: Deepali Ujalambkar
Published: January 2025

The efficiency and effectiveness of conventional human inspection techniques for surface defects are very low in most industrial products. The modern industry requires immediate identification of the non-conforming parts. Human inspectors cannot be relied upon for this all the time. Although these processing techniques will help overcome some of the limitations, they don't do well with the complex textures, noise, and light variations. The paper aims to fill in this gap by proposing deep learning techniques for better defect detection and classification. The developed model, on training with various surface textures datasets, presented excellent performance in detecting the cracks, patches, inclusions, rolled edges, pitting, and scratching defects. By adding ResNet50 and MobileNetV2 additive into an ensemble framework model, the detection of the surface problem became more accurate and reliable. This advanced method can potentially be helpful to the industry both in diagnosis and classification.
An Analysis Of Virtual Reality-Based Rehabilitation Method For Children With Hemiplegic Cerebral Palsy
Authors: Sarita Dnyaneshwar Sapkal
Published: January 2025

A potentially effective method for enhancing upper extremity function in children with hemiplegic cerebral palsy is virtual reality-based intervention. This literature review seeks to consolidate existing research that illustrates the effectiveness of virtual reality-based therapy in enhancing upper limb motor skills and functionality within this population. Hemiplegic cerebral palsy is a condition marked by motor dysfunction on one side of the body. Improving upper limb functionality is one of the main problems associated with this condition during rehabilitation. Though helpful, traditional therapeutic approaches frequently have issues with participation and customized adaption This review systematically examines recent studies that utilize VR technology to address these limitations by providing interactive, immersive environments that can be tailored to each child's needs. Key findings from the literature indicate that VR-based interventions can significantly improve motor skills, coordination, and overall upper extremity function in these children. The review highlights various VR approaches, including game-based therapies and simulated tasksThe review concludes that while VR interventions show promising results Additional research is essential to enhance these therapies, comprehend their long-term effects, and tackle practical issues such as accessibility and affordability. The integration of VR into rehabilitation programs presents a progressive step forward in pediatric therapy, offering a dynamic tool to complement traditional methods and enhance the quality of life for children with hemiplegic cerebral palsy.
Evaluating the Performance of SQL-Based vs. Python-Based Data Processing in Cloud Computing for Machine Learning Applications
Authors: Bharath Muddarla
Published: January 2025

In cloud computing environments, efficient data processing is essential for machine learning applications, where the choice of processing tools directly impacts performance and scalability. This study compares SQL-based and Python-based data processing to evaluate their effectiveness in handling large datasets and supporting machine learning workflows. Through experiments on AWS using Amazon Redshift for SQL and Pandas/Dask for Python, we analyzed processing speed, memory utilization, scalability, and integration complexity across different tasks. Results indicate that SQL outperforms Python in speed and memory efficiency for simple, structured data transformations, making it ideal for large-scale data cleaning and aggregation tasks. However, Python offers greater flexibility and seamless integration with machine learning frameworks, proving advantageous for complex transformations and feature engineering. Statistical analyses confirm SQL’s strength in handling high-volume structured data, while Python is better suited for tasks requiring intricate preprocessing and machine learning model integration. These findings suggest that a hybrid approach can combine the strengths of both SQL and Python for optimal data processing in cloud-based machine learning workflows.
Evaluation of Infinite Integrals Involving the H-Function of Two Variables
Authors: Nitesh Kumar Sahu
Published: January 2025

In this research, we delve into the intricate realm of infinite integrals featuring the H-function of two variables, a powerful mathematical construct that encompasses various special functions. By exploring the H-function's versatility, we uncover novel solutions to integrals that emerge in diverse fields such as applied mathematics, physics, and engineering. Through rigorous analysis, we derive concise closed-form expressions for a range of infinite integral classes, shedding new light on their properties and applications.
Power Dominator Coloring and Power Dominator Equitable coloring for Hierarchical Tree-Based Network Architecture
Authors: G. Navamani
Published: January 2025

A power dominator coloring of a graph G is defined as a proper coloring where each vertex in V(G) power dominates every vertex within at least one color class. The minimum number of colors needed for a power dominator coloring of G is denoted as χ_pd (G). Furthermore, a proper coloring of a graph G is considered equitably k-colorable if the sizes of any two color classes differ by at most one, i.e. |(|C^i |-|C^j |)|≤1, 1≤i,j≤ k and the minimum number of colors needed for an equitable coloring of  G is denoted as χ_e (G). The power dominator equitable coloring of a graph G defines a proper k-coloring where each vertex in V(G) power dominates all the vertices of at least one color class and also it satisfies the inequality |(|C^i |-|C^j |)|≤1, 1≤i,j≤ k and the minimum number of colors needed for a power dominator equitable coloring of G is denoted as  χ_pde (G). In this study, we commence a research on this parameter and determine the power dominator equitable chromatic number χ_pde   for certain hierarchical tree-based network architectures. The results are then compared with traditional domination concepts.
On Beta Generalized Pre-I-Closed Sets in Ideal Topological Spaces
Authors: S. Gowri
Published: January 2025

The Purpose of this paper is to describe and explore some of the new concept of closed sets called Beta generalized pre-I-closed sets in ideal topological spaces, which shows a few examples. Additionally, we define and examine the features of Beta generalized pre-I-open sets in ideal topological spaces.
Exploring Transformations and Properties of Secondary J Binormal Matrices: Closure Challenges and Skew-Symmetric Conversions
Authors: K.Jaikumar
Published: January 2025

This paper investigates the conditions for a secondary J binormal matrix to transition into a secondary J normal matrix and examines the analogous transformation for a secondary Jbnbinormal matrix to become a secondary J normal matrix. We also analyze the distinctive properties associated with secondary binormal is matrices. Moreover, we address a crucial issue: the lack of closure under addition for secondary binormal matrices. Furthermore, we investigate the essential requirements and conditions that are both necessary and sufficient for transforming a secondary J skew-symmetric matrix into a secondary binormal matrix.
Exploring the Mathematical Landscape of p-adic Numbers: Unveiling Uniqueness and Ostrowski’s Theorem
Authors: Prakasam. S
Published: January 2025

In this paper, we investigate on the p –adic number, metric and absolute value over the words more précises, we shall construct the uniqueness theorem and ostrowki’s theorem by using p – adic number. We explain a p – adic absolute value, non-archimedean axioms, after that we study their metric space and completion of X to XP.
Introduction: The p-adic numbers, where p is any prime number, originate from an alternate way of deﬁning the distance between two rational numbers. Kummer first discovered p-adic numbers in 1850. The p-adic numbers are useful because they provide another toolset for solving problems, one which is sometimes easier to work with than the real numbers.
Methods: the strong inequality of p-adic numbers, uniqueness theorem for p- adic number and also Ostrowski’s theorem for p-adic numbers.
Results: the absolute value p –adic metric is non-Archimedean, all triangles are isosceles in a non - Archimedean metric space,  a non –Archimedean metric space theorem and completion of X to XP.
Conclusions: we have defined a p – adic number and p – adic metric. After that, we give their uniqueness and Ostrowski’s theorem.
Advances in Nonlinear Variational Analysis for Cyber Criminal Detection via Smartphone Communication Patterns
Authors: K. Swetha
Published: January 2025

The increasing dependence on digital communication needs sophisticated ways for identifying cybercrime, particularly on mobile devices. This paper provides a comprehensive methodology that uses Natural Language Processing (NLP) tools to analyze communication patterns and detect suspicious activity. To generate communication patterns, text is preprocessed, vectorized with TF-IDF, metadata is extracted, and clustering is performed. Anomalies are identified by calculating deviations from these patterns, and suspicious signals are reported for further study. The proposed strategy outperformed baseline techniques in terms of accuracy, precision, recall, and the F1 score. Statistical study supported the method's performance, highlighting its potential as a significant tool for improving cybersecurity measures. In addition, the methodology employs Nonlinear Variational Inequalities to improve anomaly detection in communication patterns, addressing deviations more robustly within a cybersecurity framework