bluuebunny/crossref_metadata_2025 huggingface dataset:
EXPLORING THE HUMAN CIRCULATORY SYSTEM THROUGH SYSTEM DYNAMICS: A MODEL-BASED APPROACH
Authors: Iapăscurtă Victor
Published: February 2025

System dynamics is a robust methodology for understanding the behavior of complex systems over time. By employing feedback loops, stocks, flows, and time delays, this approach provides a comprehensive framework for simulating and analyzing dynamic systems. The application of system dynamics to the human circulatory system presents numerous possibilities, benefits, and practical applications that can significantly enhance our understanding and management of cardiovascular health. This article details experimental results from modeling myocardial infarction conditions using a six-compartment model developed in the NetLogo integrated development environment, incorporating BehaviorSpace for extensive simulations. For result analysis, specialized packages in R, Python, and Wolfram Mathematica were utilized to ensure rigorous data interpretation. The results demonstrate promising fidelity when compared to existing literature and real-time patient data, indicating the model's potential for clinical applications. By illustrating the interactions within the circulatory system, this research not only contributes to theoretical knowledge but also offers practical insights into disease management and intervention strategies, paving the way for improved cardiovascular health outcomes.
ANALYSIS METHODOLOGY OF OPENSTREETMAP DATA COMPLETENESS (CASE STUDY: CHISINAU CITY)
Authors: Sîrbu Rodica
Published: February 2025

In recent years, special attention has been paid to free and open geospatial data sources accessible to the whole world, which have benefits for all sectors of the national economy. Taking into account the interest in this topic, the article proposes a data completeness analysis for OpenStreetMap, that is based on crowd sourcing, compared to data sources available at the national level, such as statistical information from Public Institution Real Estate Cadastre registers or the Spatial Data Infrastructure of the Republic of Moldova (NSDI), Agency Geodesy, Cartography and Cadastre. Thus, a general methodology for completeness analysis framework is designed based on the characteristics of OSM data, since is a well-known standard. Secondly, an attempt was made to apply a quality assessment model for OSM data through three different quality elements: completeness, thematic accuracy and positional accuracy based on the visual and quantitative analyses taking into account the spatial data that already exists on national platforms. The Chisinau city was selected as a case study/area of interest, and the results of the presented research can subsequently be applied to the entire Republic of Moldova. Based on the analysis performed, it can conclude that spatial data sources, whether from official sources or those based on crowd sourcing, still need to be supplemented with both spatio-temporal and qualitative information.
THE ROLE OF PROCYANIDINS IN GRAPES AND WINES: EFFECTS ON QUALITY AND COMPOSITION
Authors: Arseni Alexandra , Crudu Sorina
Published: February 2025

Proanthocyanidins in grapes and wine are oligomers and polymers of flavan-3-ols, predominantly connected through C4–C8 and/or C4–C6 bonds (B-type). These compounds significantly influence red wine’s sensory profile, particularly its color and mouthfeel. In addition, proanthocyanidins are recognized for their antioxidant and radical-scavenging properties, capable of neutralizing reactive oxygen species and preventing cellular damage caused by oxidative stress. These properties give them an important role in protecting health, with beneficial effects on the body by reducing the risk of cardiovascular diseases and cancer. The aim of the article is to explore the role of proanthocyanidins in grapes and wine, focusing on their contributions to wine’s sensory characteristics such as color, astringency, and mouthfeel. It aims to examine the factors influencing their composition, the transformations they undergo during winemaking and aging, and their broader implications for wine quality, stability, and health benefits. The objectives of the study are to investigate the physical, chemical, and biochemical properties of procyanidins, as well as the influence of factors such as grape variety, ripeness, health status, climatic conditions, viticultural techniques, grape processing methods, wine stabilization, and aging techniques on their content.
Teknik Prediksi Pembangunan Ekonomi Berbasis Ekonomi Hijau Pada Perencanaan Daerah
Authors: Santoso Dwi Budi , Pangestuty Farah Wulandari , Saraswati Erwin , Fuadi Hamdan
Published: February 2025

Teknik prediksi pembangunan ekonomi berbasis ekonomi hijau pada perencanaan daerah  di latar belakangi oleh keterbatasan kemampuan  SDM Bappeda Kabupaten Malang melakukan analisis teknik prediksi secara optimal, seperti pengelompokan indikator pilar pembangunan ekonomi hijau, menetapkan target pilar utama kesejahteraan sosial. Hambatan ini menunjukkan perlunya dukungan tambahan, baik dari pemerintah untuk memastikan keberlanjutan dan efektivitas program pengabdian ini. Kegiatan pengabdian kepada masyarakat dalam rangka pelatihan teknik prediksi pembangunan ekonomi berbasis ekonomi hijau telah sukses dilaksanakan pada Rabu, 10 Oktober 2024. Kegiatan ini berlangsung di Aula Gedung Dekanat Lama Fakultas Ekonomi dan Bisnis Universitas Brawijaya dan diikuti oleh 30 anggota BAPPEDA Kota Malang. Pelatihan tersebut mencakup pendalaman konsep ekonomi hijau serta praktik teknis dalam memprediksi indikator pembangunan yang berbasis pada pilar ekonomi, sosial, dan lingkungan. Partisipasi aktif dari para anggota BAPPEDA menunjukkan antusiasme tinggi dalam memahami dan mengimplementasikan konsep ini dalam dokumen perencanaan daerah. Dengan suasana yang mendukung dan fasilitas yang memadai, kegiatan ini berhasil memberikan pemahaman yang mendalam serta meningkatkan kompetensi peserta, yang diharapkan dapat mendukung upaya penyusunan perencanaan pembangunan daerah yang berkelanjutan.
Evaluasi Tata Kelola Sistem Informasi Arsip Digital (SIAD) Menggunakan Framework COBIT 2019 di Disdukcapil Kabupaten Malang
Authors: Amelisa Eka Puspita Roro , Marcus Ronald David , Muslikh Ahmad Rofiqul
Published: February 2025

Kemajuan teknologi informasi (TI) memberikan peluang besar bagi instansi pemerintah untuk meningkatkan kualitas layanan publik. Namun, Dinas Kependudukan dan Pencatatan Sipil Kabupaten Malang masih menghadapi tantangan dalam mengoptimalkan tata kelola Sistem Informasi Arsip Digital (SIAD). Hal ini memengaruhi efektivitas dan efisiensi pengelolaan arsip, serta layanan administrasi kependudukan. Penelitian ini menggunakan framework COBIT 2019 untuk mengevaluasi tingkat kapabilitas tata kelola pada SIAD. Fokus penelitian mencakup lima domain utama, yaitu APO12 - Manajemen Risiko, BAI09 - Manajemen Aset, DSS03 - Manajemen Masalah, DSS05 - Manajemen Keamanan Layanan, dan MEA03 - Kepatuhan terhadap Regulasi. Data dikumpulkan melalui kuesioner berbasis skala Guttman dan dianalisis menggunakan model capability level ISACA. Hasil penelitian menunjukkan bahwa seluruh domain telah mencapai Level 4 (Predictable) dengan kategori Largely Achieved, yang berarti proses telah terdokumentasi dan berjalan sistematis. Namun, ditemukan kesenjangan pada pemantauan real-time, analisis prediktif, dan otomatisasi proses. Penelitian ini merekomendasikan penerapan teknologi pendukung, pelatihan sumber daya manusia, serta evaluasi dan audit berkala untuk meningkatkan tata kelola TI. Penelitian ini memberikan panduan strategis bagi Disdukcapil untuk mencapai tata kelola TI yang lebih efisien dan berkelanjutan.
Pendekatan Metode DBSCAN dan Fuzzy C-Means untuk Klasterisasi Skala Prioritas Stunting
Authors: Lubis Putri Natasya , Hasibuan Muhammad Siddik
Published: February 2025

Stunting merupakan permasalahan kesehatan yang berdampak pada perkembangan fisik dan kognitif anak, serta berkontribusi terhadap kesenjangan sosial dan ekonomi di Indonesia. Penelitian ini bertujuan untuk mengembangkan metode klasterisasi skala prioritas penanganan stunting dengan menggabungkan algoritma DBSCAN (Density-Based Spatial Clustering of Applications with Noise) dan Fuzzy C-Means (FCM). Data diperoleh dari platform Aksi Bangda Kemendagri (2021–2024) dan diolah menggunakan Python. DBSCAN digunakan untuk mengidentifikasi wilayah dengan konsentrasi tinggi kasus stunting dan mendeteksi outlier, sementara FCM membantu menentukan prioritas intervensi berdasarkan tingkat keparahan. Hasil penelitian menunjukkan bahwa DBSCAN menghasilkan dua klaster utama dan sejumlah outlier, sedangkan FCM membagi data menjadi tiga klaster berbasis derajat keanggotaan. Pendekatan ini berpotensi menjadi alat analitik dalam mendukung kebijakan percepatan penurunan angka stunting secara lebih efektif di Indonesia.
Analisis Kepuasan Pengguna Aplikasi Livin by Mandiri Menggunakan Metode E-Servqual dan Importance Performance Analysis (IPA)
Authors: Tanamal Ulfa Ferdiayanti
Published: February 2025

Teknologi informasi yang semakin berkembang pesat mengharuskan industri memproduksi inovasi terbaru untuk dapat mempermudah kelancaran kehidupan sehari-hari dalam masyarakat. Hal ini menyebabkan terjadinya peningkatan interaksi antara manusia dan komputer. Perkembangan teknologi juga memunculkan berbagai jenis layanan, salah satunya adalah kemudahan melakukan transaksi. Selain digunakan sebagai sarana mencari informasi, dalam media promosi juga memerlukan penggunaan internet, bertransaksi dan melakukan kegiatan lainnya dengan mudah dan cepat. Livin by Mandiri merupakan layanan mobile banking digital dari Bank Mandiri yang berfungsi untuk transaksi, pengecekan saldo, top up, dan lainnya. Aplikasi Livin by Mandiri dapat mendeskripsikan kenyamanan dalam melakukan transaksi dan dapat menarik perhatian nasabah baru untuk mencoba kemudahan dalam bertransaksi. Pada penelitian ini menggunakan pendekatan kuantitatif, karena berfokus untuk mengetahui hubungan antar variabel yang digunakan. Strategi yang dipakai berdasarkan tujuan adalah mengidentifikasi bagaimana kepuasan pengguna terhadap kualitas layanan elektronik aplikasi mobile banking Livin by Mandiri. Hasil penelitian analisis gap persepsi dan ekspektasi pada tabel 4.15 dengan rumus performance-importance kualitas layanan menunjukkan rata-rata gap yang terdapat pada dimensi yaitu, dimensi efficiency (0,10), fullfilment (-0,04), system availability (0,14), responsiveness (0,14), compensation (-0,17), privacy (0,15), contact (0,20), design (-0,06). Hasil dari 9 hipotesis, terdapat 3 hipotesis yang ditolak sehingga dianggap tidak adanya pengaruh terhadap kepuasan pelanggan yaitu meliputi variable fulfillment (pemenuhan), compensation (benefit atau manfaat), dan contact (kontak). Hipotesis yang diterima yaitu efficiency (sistem yang efisien), system availability (kesediaan sistem), responsiveness (respon), trust (kepercayaan), privacy (keamanan) dan design (desain aplikasi).
Analisis Sentimen Publik Pada Aplikasi X Terhadap Kenaikan UKT Di Indonesia Menggunakan Algoritma Naive Bayes
Authors: Bandaharo Bandaharo , Hasugian Abdul Halim
Published: February 2025

Pertumbuhan teknologi digital dan internet telah memungkinkan untuk memperoleh dan menyelesaikan berbagai tugas akademik dengan lebih cepat. Media sosial juga telah menjadi semakin penting sebagai platform untuk opini publik tentang berbagai topik, salah satunya adalah kebijakan perguruan tinggi terkait Uang Kuliah Tunggal (UKT). Kebijakan ini sering menjadi isu yang kontroversial jika itu berkaitan tentang kenaikan UKT. Permasalahan terhadap kenaikan UKT sering menjadi perhatian bagi orangtua maupun mahasiswa tersebut. Sehingga dibeberapa kalangan, kenaikan UKT dapat menjadi masalah bagi mereka. Analisis Sentimen Publik adalah langkah yang tepat untuk menghitung tingkat opini positif dan negatif terkait isu kenaikan UKT. Dengan tujuan untuk mengukur sentimen publik terhadap kenaikan UKT algoritma Naive Bayes. Penelitian ini menggunakan data yang bersumber dari aplikasi X (Twitter) yang di crawling menggunakan Google Colab dengan jumlah data yaitu 1390 dan juga menggunakan metode Naive Bayes sebagai penyelesaian masalah. Hasil yang diperoleh setelah melakukan penelitian yaitu terdapat sebanyak 457 komentar negatif dan 933 komentar positif, kemudian untuk hasil evaluasi model diperoleh skor F-1 sebesar 0,82 atau 82%, akurasi sebesar 0,748 atau 74,8%, presisi sebesar 0,81 atau 81%, dan recall sebesar 0,84 atau 84%. Diharapkan kedepannya, penelitian ini mampu memberikan evaluasi kembali terhadap kementrian pendidikan apakah kenaikan UKT ini layak atau tidak untuk diperdayakan.
Pengembangan Platform E-Commerce UMKM Berbasis Laravel dengan Blackbox Testing dan Metode Waterfall
Authors: Angel Reddis , Agustina Wilda , Nurhasanah Nurhasanah , Mauluddin Aliffian Cahya , Handayani Rissa Nurfitriana
Published: February 2025

UMKM (Usaha Mikro, Kecil, dan Menengah) memiliki peran penting dalam perekonomian Indonesia. Namun, banyak UMKM, khususnya di sektor MPASI (Makanan Pendamping ASI), menghadapi tantangan dalam memasarkan produk mereka secara efektif di era digital. Penelitian ini bertujuan untuk mengembangkan platform website e-commerce berbasis Laravel guna mendukung UMKM dalam memperluas jangkauan pemasaran produk MPASI secara efisien. Platform ini dikembangkan menggunakan metode pengembangan perangkat lunak Waterfall, yang mencakup tahap perencanaan, analisis, desain, implementasi, dan pengujian. Pengujian menggunakan metode Blackbox untuk memastikan fungsionalitas sistem sesuai kebutuhan pengguna. Hasil pengujian menunjukkan bahwa platform ini berhasil meningkatkan efisiensi proses pemasaran dengan tingkat keberhasilan fitur mencapai 95% berdasarkan uji fungsionalitas. Dengan platform ini, UMKM diharapkan dapat mengoptimalkan pemasaran digital dan meningkatkan jangkauan pelanggan secara signifikan.
Gestão participativa em áreas protegidas: análise comparativa dos Parques Nacionais no Brasil, Argentina e Chile
Authors: Carvalho Rita de Cássia Pereira de , Putrick Simone Cristina , Silva Maria Majaci Moura da , Leal Marcia da Luz , Nascimento Caroline Gomes do , Azevedo Wyma Wendell da Silva
Published: February 2025

Este artigo investiga as formas de gestão participativa em áreas protegidas nos parques nacionais do Brasil, Argentina e Chile. A pesquisa adota uma abordagem qualitativa, baseada na análise documental de planos de manejo, legislações ambientais e acordos internacionais. Os resultados indicam que, embora os documentos de gestão prevejam a participação social, a prática efetiva enfrenta desafios significativos. A falta de uma cultura consolidada de participação, o desconhecimento das comunidades sobre suas oportunidades de engajamento e a escassez de recursos limitam a governança participativa. No entanto, exemplos de sucesso, como nos parques nacionais do Iguaçu (Brasil) e Iguazú (Argentina), mostram que a cooperação internacional pode ser uma estratégia eficaz. A pesquisa revela que a gestão participativa não depende apenas de instrumentos legais, mas também de fatores subjetivos, como o compromisso dos gestores e a construção de diálogos com as comunidades. Estudos futuros podem expandir a análise para outros países e utilizar métodos quantitativos para avaliar o impacto dessas políticas de forma mais objetiva.
Educação Profissional e Tecnológica (EPT): uma proposta de utilização de HQ como prática inclusiva
Authors: Jimenez Sandra Regina , Matos Marilyn Aparecida Errobidarte de
Published: February 2025

A presente pesquisa tem como objetivo refletir sobre a utilização de histórias em quadrinhos (HQs) como prática pedagógica inclusiva na Educação Profissional e Tecnológica (EPT), especificamente no Instituto Federal de Mato Grosso do Sul (IFMS), considerando a deficiência como marcador social e o ideal de educação inclusiva. Como metodologia utilizou-se do estudo descritivo, tipo análise teórico-reflexiva, elaborada a partir de uma revisão narrativa com base na bibliografia apresentada sobre: a história da educação de pessoas com deficiência, a criação dos Institutos Federais, o cenário atual da educação inclusiva no IFMS e o entendimento de prática pedagógica relacionando-a ao que a EPT preconiza. Propõe-se a junção das HQs com o tema inclusão em consonância com a nova linguagem após a inserção das HQs na era digital. Por fim, enfatiza-se a necessidade de que o IFMS tenha um processo contínuo de aprimoramento das propostas pedagógicas, possibilitando a incorporação de temas como a inclusão.
Arranjo colaborativo em educação enquanto estratégia de promoção do desenvolvimento territorial
Authors: Santos Claudilson Souza dos , Batista Carlos Alberto Santos , Barros Edonilce da Rocha
Published: February 2025

Historicamente, a educação pública brasileira é ofertada isoladamente pelas esferas governamentais, revelando-se ineficiente diante das demandas. Entretanto, a partir da Constituição Federal de 1988 e da LDB nº 9.394/96, estabeleceu-se o regime de colaboração entre os entes da federação, como estratégia de desenvolvimento das políticas públicas educacionais, o que suscita uma governança de relações horizontalizadas entre os governos e destes com a sociedade. Esta é uma pesquisa bibliográfica de acesso livre nas bases de dados Scielo, Google Acadêmico, a textos em língua portuguesa, cujo conteúdo se relaciona ao tema e ao objetivo estabelecido. Com o advento da Lei nº 11.107/2005 (BRASIL, 2005) e do Parecer CNS/CEB nº 01/2012 (BRASIL, 2012), estas mencionam a formalização de consórcios e arranjos, com participação pública, privada e/ou por meio de organizações sociais, como estratégia de desenvolvimento de ações territoriais. Contudo, a literatura menciona, que na prática, no Brasil, o arranjo educacional, ainda não recebe a atenção devida dos pesquisadores e dos gestores, exemplificando com a existência de pouco mais de 13 arranjos educacionais ativos no país. O arranjo educacional pode ser concebido como estratégia efetiva de governança exercida por multiatores em uma rede colaborativa de participação social, com vistas à promoção do desenvolvimento territorial. Portanto, diante dos estudos, concebe-se o arranjo educacional enquanto estratégia de desenvolvimento territorial, a partir da constituição de governança social horizontalizada, em busca da promoção da melhoria da educação brasileira. Este artigo tem como objetivo, analisar o arranjo colaborativo em educação enquanto estratégia de promoção do desenvolvimento territorial.
Quality of Sleep among Dialysis patient at Bapuji Hospital, Davanagere
Authors: Banu A Anjum , GM Paramesh , Shetty G Pavithra
Published: February 2025

Background: Sleep is essential for all living beings in the earth. Sleep energizes us to work in all sector. Sleep is essential to every process in the body. Various types of sleep disturbances are common among patient with end stage renal disease (ESRD) and it is an important and determining factor in the quality of life in dialysis patient. Although many chronic dialysis patients complain of poor sleep. Sleep is one of the most important factors for dialysis patient, as it can affect their physical and mental health. Objectives: 1. To assess the quality of sleep among dialysis patients. 2. To determine the association between quality of sleep among dialysis patient with selected demographic variables. Methods: A descriptive survey design was adopted for the study. Samples were selected using non probability convenient sampling technique; samples consist of 40 dialysis patients at Bapuji Hospital, Davanagere. Data from the study participants was collected by standardized rating scale on importance of sleep quality among dialysis patient. Data collected was analyzed using descriptive and inferential statistics. Results: Findings of the study revealed that the mean age of the participants is 49.85±5.641 years. Among the participants, 10% of the dialysis patient had poor sleep quality, 85% of the patient had moderate sleep quality, 5% of the patient had good sleep quality. The mean of sleep quality score is 47.75 (56.84%) with the standard deviation of 5.821. The sleep quality is significantly associated with frequency of Dialysis/Week. Conclusion: The findings of the study conclude that majority of the dialysis patient had moderate sleep quality and the sleep quality is significantly associated with frequency of dialysis per week. We need to improve sleep quality and we have to take proper interventions to improve the sleep quality among dialysis patient. Sleep can be improved by relaxation, meditation.
A Comparative Study to Assess the Effectiveness of Jacobson’s Progressive Muscle Relaxation Therapy Versus Deep Breathing Exercise on the Level of Pain and Stress among Cancer patients receiving Chemotherapy admitted in SMVMCH, Puducherry
Authors: G. Sathyavathy
Published: February 2025

According to the most recent data from the National Cancer Registry, one in eight men and one in nine women will develop some kind of cancer in India. Age, gender, ethnicity, local environmental circumstances, cuisine, and genes are only a few of the numerous factors that influence cancer types. Jacobson’s gradual muscle relaxation therapy and deep breathing techniques are successful, cancer patients undergoing chemotherapy report less stress and pain overall. The aim of the study is to evaluate the effectiveness of Jacobson’s Progressive Muscle Relaxation Therapy Versus Deep Breathing Exercise on the Level of Pain and Stress among Cancer Patients Receiving Chemotherapy. A concurrent true experimental group design was used for the investigation. 280 people were included in the sample, which was chosen using a purposive sampling method. It reveals that the Comparison of pain scores among cancer patients receiving chemotherapy between the groups before intervention shows that in Group I, In Group II, the mean pain score was 5.63±2.41, whereas the mean pain score overall was 5.53±2.49. According to the review, the mean post-test rating of 6.4% was lower than the pre-test score of 37.9 % and this score is mostly in decreasing the degree of torment in Jacobson’s dynamic muscle unwinding strategy. Hence, the Jacobson’s Progressive Muscle Relaxation Therapy is much effective in reducing the degree of level of pain and stress among cancer patients receiving chemotherapy.
Algorithms for Computing Domination and Total Domination Numbers of RNPCG
Authors: Renuka Lakshmi Avvari
Published: February 2025

Introduction: This paper presents an efficient algorithmic framework for computing the domination number (γ) and total domination number (γt) of graphs, with a focus on applications in collaboration networks. Using a greedy approach combined with minimality verification, the algorithm identifies dominating and total dominating sets that optimize connectivity within the graph structure.
Objectives: To design and implement algorithms for computing the domination number of RNPCGs, focusing on both time complexity and practical efficiency.
Methods: The methodology is demonstrated on the Rolf Nevanlinna Prize Collaboration Graph (RNPCG), showcasing its ability to handle complex, real-world datasets. Results reveal key insights into the connectivity and influence metrics of prominent academic networks, with implications for optimizing resource dissemination in such systems. The proposed algorithm balances computational efficiency with accuracy, offering a robust tool for graph-theoretic analysis in both theoretical and applied contexts
Results:  A detailed computational complexity analysis was conducted for both the domination number and total domination number algorithms. It was found that the proposed solutions offer polynomial-time complexity for graphs of moderate size, making them practical for typical use cases. However, the complexity does increase with the size of the graph, especially as the randomness in RNPCGs increases.The algorithms were also compared with exact brute-force solutions for small graphs, showing that our approach provides a significant reduction in computation time while maintaining accuracy.
Conclusions: In this study, we developed an efficient algorithm for computing the domination number (γ) and total domination number (γt) in graphs, with a focus on its application to the Rolf Nevanlinna Prize Collaboration Graph (RNPCG). By leveraging a greedy approach and minimality verification, the algorithm effectively identified dominating and total dominating sets, providing insights into the connectivity and structural properties of the RNPCG. The analysis revealed the influence and connectivity of key vertices, illustrating the practical utility of domination metrics in understanding complex collaboration networks. The results emphasize the significance of domination parameters in optimizing resource dissemination and maintaining connectivity within academic or similar real-world networks.
Enhanced Privileged Access Management with Identity Access Management to Improve the User’s Safety Measures in Online Circumstances
Authors: K. R. Sumathi
Published: February 2025

Network safety measures are any actions done to protect the information integrity and usability of the user/customer network. Hardware and software technology issues are mutually exclusive. It chases many threats. It prevents intrusions from entering the user network or from spreading. Businesses that network computers and systems may experience issues with one user that impacts the network as a whole. Although networking has many advantages, it also raises the threat of safety measures troubles like information loss, safety measures flaws, unauthorized intrusions similar to hackers, viruses. The risk of unauthorized access or network damage can be reduced by the user taking preventative measures. Since it might not be feasible or economical to completely eliminate all risks, the importance of conducting an IT risk assessment cannot be emphasized when choosing what steps to take. Privileged Access Management techniques and tools are utilized to implement control over prominent admittance and acquiescence for customers, user-accounts, progressions, and methods in Information Technology surroundings. Through reducing their attack surface, privileged access management helps companies lessen the damage that external attacks and internal carelessness or misconduct can cause to their operations. The possibility of implementing Privileged Access Management and the effects ofPAM in the current situation are similar to an overview of user-related risks. In addition to comparing it with the current approaches, this paper suggested an improved PAM with IAM adoption.
Design of an Integrated Model with Contrastive Predictive Coding and Model-Agnostic Meta-Learning for Adaptive and Explainable Mobile Forensic Analysis
Authors: Preeti Dudhe
Published: February 2025

Handling multimodal & multiactivity data samples has been one of the major challenges in mobile forensic analysis that includes handling diverse, sequential data types like call logs, messages, and GPS records. Traditional supervised models struggle with dynamic and unlabeled mobile data and require large amounts of labeled datasets and time-consuming retraining as data patterns evolve. The integration of CPC, MAML, and SHAP for an advanced self-supervised learning framework is proposed in this paper to surpass the drawbacks mentioned above. This proposed architecture is structured such that it capitalizes on CPC's ability to draw high-quality temporal representations from unlabeled data by encoding sequential dependencies in mobile data, making it apt for capturing the nuanced behavioral patterns. After CPC, MAML provides rapid adaptability with learning initialization parameters generalizing across forensic tasks using minimal retraining, thus being important for quick adaptation in an evolving data environment. SHAP improves the transparency of models by giving feature importance scores that would help forensic analysts understand and validate model predictions. These methods, taken together, provide a robust pipeline which addresses the quality of representation as well as the interpretability of a model, making the framework flexible to complex real-world mobile forensic data with a minimal amount of dependency on labeled samples. Empirical results have shown a significant improvement, where CPC enhanced the quality of the representation up to 10%, while MAML accelerated the adaptation speed by 20% to 25%, and SHAP reached up to 90% interpretability of model decisions. It will bring a transformative approach toward the mobile forensic analysis that significantly improves the ability to extract accurate and explainable insights in the support of high-stakes evidence validation process.
MHD Three-Dimensional Porous Flow with Heat Source and Chemical Reaction: a Numerical Study
Authors: K.Srihari
Published: February 2025

In prior research on three-dimensional flow problems has predominantly focused on deriving analytical solutions. However, numerical studies addressing such three-dimensional flow problems are still relatively limited. So, In the present paper numerical attempt is made to study the impact of a heat source and chemical reaction on the free convection flow of a viscous, incompressible fluid within a porous medium, confined by an infinite vertical porous plate subject to constant suction and periodic permeability. A magnetic field is applied perpendicular to the flow. To solve this problem, the governing non-linear equations with boundary conditions are first transformed into ordinary and partial differential equations of zeroth and first order, respectively, using the perturbation method. Subsequently, the partial differential equations, which describe three-dimensional flow, are reduced to coupled non-linear differential equations using appropriate substitutions. These resulting coupled non-linear equations are then approximated into a system of equations via finite difference methods. The findings for the velocity and temperature profiles are presented and analyzed graphically. It is found that both velocity and temperature increase with the introduction of the heat source parameter.
Analyzing Air Traffic Control System Reliability with Markov Process Methods
Authors: Anmol Gupta
Published: February 2025

This paper concludes the mathematical models of the Markov process to examine the reliability of Air Traffic Control (ATC) systems. The safety and effectiveness of air travel depend significantly on reliability of ATC systems, making it necessary to evaluate & enhance their performance using robust analytical techniques. We use continuous-time Markov chains to model the different operational, failure states and degrade state. We analyse Markov process alongside various component failures and repairs of the ATC that may occur during its operation. To assess the system's performance, we calculate reliability and mean time to failure (MTTF). Sensitivity analysis is used to identify the critical components of the ATC.
Influence of Masonry Strength and Column Reinforcement on the Lateral Performance of Infilled RC Frames
Authors: Jonathan T Mark
Published: February 2025

Masonry infill strength and column flexural, shear reinforcement are the influencing factors in the lateral performance of infilled frame. Various numerical and experimental studies performed in the past decades bring out the beneficial and adverse effects of masonry infilled frame. In this numerically study masonry of different strengths i.e., Concrete Masonry Unit (CMU), Clay Brick (CB), Flyash brick (FA) and Aerated Autoclaved Concrete block (AAC) have been used as infill in a reinforced concrete frame and the behaviour is observed. Similarly, for strong masonry, CMU infilled frame, the flexural and shear reinforcement of column has been varied. From the numerical analysis performed it has been observed that irrespective of the masonry strength all the specimens showed predominant flexural failure of column whereas CMU infilled frame with a flexural reinforcement of 6.4% failed by shear. It is concluded that when the shear capacity of the frame is more than the flexural shear capacity of the frame the failure mode is observed to be flexure.  From the conclusions a simple and effective analytical method for the identification of shear failure in column considering the influence of masonry strength has been proposed.
Finite-Dimensional Hilbert Spaces: Optimal Basis Selection and Its Applications in Signal Processing and Numerical Analysis
Authors: Mansourah H. Al-Yazidi
Published: February 2025

This paper presents a self-contained theoretical and practical framework for optimal basis selection in finite-dimensional Hilbert spaces, with applications in signal processing and numerical analysis. Based on quantum-optical state theory and dagger category frameworks, we give a rigorous mathematical development that yields a criterion for basis selection, enabling a proper trade-off between approximation accuracy and computational efficiency. The study introduces new methods to represent discrete signals via Wigner functions, along with explicit error bounds for numerical approximations. Our framework contains both theoretical foundations and practical implementations, illustrated by case studies showing the validity of the approach. Hence, it makes three key contributions: it gives a unified treatment of the basis selection criteria; it provides efficient numerical methods with provable convergence properties; and it presents practical implementation strategies for signal processing applications. Results indicate that our finite-dimensional approach attains high accuracy yet remains computationally efficient, with fidelity measures above 0.99 for optimal parameter choices. To further validate the performance of the framework, extensive numerical experiments are provided, which show the usefulness of the framework in a variety of applications from quantum state representation to digital signal processing. This work also addresses some of the theoretical challenges in dimensional scaling and error propagation, establishing a basis for future developments in the field. Our results show that finite dimensional methods offer significant benefits in mathematical tractability and computational realization at the cost of little accuracy for most practical applications.
Point Set Neutrosophic Domination in Single Valued Neutrosophic Graph
Authors: R. Poornavalli
Published: February 2025

In this paper, we demonstrate a concept of point set neutro-sophic domination, 2-point set neutrosophic domination, connected point set neutrosophic domination, point set tree neutrosophic domination with appropriate example. Some of their theoretical properties are investigates.
Isolating malicious nodes in the Internet of Things through a Trust-Based Mechanism
Authors: Zakiya Manzoor Khan
Published: February 2025

Abstract: Introduction:  IoT (Internet of Things) systems are open to attacks because of their resource-limited and ad hoc architecture. These systems support industrial and medical services and are used to manage massive amounts of data. IoT systems thus become vulnerable to a range of attackers, such as sporadic hackers, cybercriminals, hacktivists, and even governmental organizations. These hackers' main objective is to compromise IoT devices in order to obtain sensitive data, including credit card numbers, location information, bank account credentials, and health information. The version number attack is one serious danger; it is a malicious activity that negatively affects network performance. Malicious nodes launch this attack by overloading the network with hello packets, which causes a denial-of-service (DoS) condition. This study suggests a trust-based mechanism, in which each node is given a trust value determined by its activity, to solve this problem. The network isolates and labels as malicious the nodes with the lowest trust values. After the suggested scheme is put into practice in NS2, its effectiveness is assessed in terms of throughput, packet loss, delay, and energy usage.
A Secure Framework based on Sensor-Cloud architecture for Healthcare data using enhanced Elliptic Curve Encryption
Authors: Munish Saran,
Published: February 2025

Healthcare data security is of utmost importance in today's era due to the several reasons such as Privacy Protection, Legal and Regulatory Compliance, Protection against Data Breaches and Cyber Attacks, Patient’s Safety and Continuity of Care, Research and Innovation, Trust and Patient’s Confidence. There must be a security authority deployed that can provide efficient as well as transparent defence mechanism. Traditional technique makes use of access control lists which allows the provision for coarse-grained access to data. Whereas the proposed approach makes use of Attribute-Based Encryption (ABE) which provides fine-grained access control of data. Fine-grained access mechanism provides detailed level of authorization as compared to coarse-grained access mechanism. Ciphertext Policy Attribute Based Encryption (CP-ABE) provides fine-grained access mechanism but involves some complex computations due to which there is computational delay involved.  Our proposed approach makes use of enhanced ECC encryption algorithm based on user defined attributes on the sensor-cloud architecture, in order to provide an efficient as well as transparent security mechanism through which data owner can have complete control over their data by specifying the role based access control rules. The proposed fine grained access control approach guarantees the confidentiality and integrity and at the same time reducing the overall computational overhead and in turns makes the system more efficient as well as robust. The results clearly depicts that the proposed approach achieves faster encryption, decryption and key generation along with the more complex ECC keys as compared with traditional CP-ABE.
A Hybrid Model for Brain Tumor Segmentation using VGG16 and ResNet50
Authors: V.R. Elangovan
Published: February 2025

Cancers of the brain are among the worst illnesses a person may get. The course of medical therapy is mostly determined by the tumor's location and kind. Neuro specialists and radiologists must carefully review Magnetic Resonance Imaging (MRI) pictures in order to arrive at a definitive diagnosis of a malignancy. Treatment option mapping, disease progression monitoring, and image-based tumor segmentation are of utmost importance in medical imaging because they provide information vital for cancer analysis and diagnosis. Some have speculated that deep learning might be the key to better brain cancer diagnosis and treatment. With its state-of-the-art segmentation and detection capabilities, the segmentation strategy has significantly improved the removal of abnormal tumor regions from the brain. In this study, we published a VGG16 and ResNet50 hybrid model for MRI brain tumor segmentation. With the use of the ResNet50 algorithm and the VGG16, a Transfer Learning method, brain tumors may be detected in segmented pictures. The findings show that our suggested approach is more accurate and performs better than other models when compared side by side.
Results on Fixed Points in Vector Valued G-Metric Spaces
Authors: Pooja Yadav
Published: February 2025

Introduction: Fixed point theory furnishes a robust framework in mathematics for examining the function and system behavior by centering on fixed points. Kirk et al. brought forth the concept of cyclic transformation in 2003. Cyclic contraction broadens the standard contraction transformations in metric space. By relaxing the requirement of the triangle inequality, -metric space extends the scope of metric spaces. Vector -metric space is GMS in which the metric is lattice valued.
Objectives: We give the concept of a vector  metric space and define some related concepts. We establish some fixed point results on vector -metric space by using cyclic contraction. To underscore the practical implications and importance of our findings, we offer a range of examples and corollaries.
Methods: This research paper outlines fixed point theorems for self-transformations in vector -metric space, with the help of cyclic contraction.
Conclusions: Within this manuscript, we illustrate fixed point theorems for self-transformations in V-complete vector -metric space by utilizing cyclic contraction. These outcomes are expected to inspire researchers to explore problem-solving opportunities in diverse areas such as differential equations and functional analysis.
Introduction: Fixed point theory furnishes a robust framework in mathematics for examining the function and system behavior by centering on fixed points. Kirk et al. brought forth the concept of cyclic transformation in 2003. Cyclic contraction broadens the standard contraction transformations in metric space. By relaxing the requirement of the triangle inequality, -metric space extends the scope of metric spaces. Vector -metric space is GMS in which the metric is lattice valued.
Objectives: We give the concept of a vector  metric space and define some related concepts. We establish some fixed point results on vector -metric space by using cyclic contraction. To underscore the practical implications and importance of our findings, we offer a range of examples and corollaries.
Methods: This research paper outlines fixed point theorems for self-transformations in vector -metric space, with the help of cyclic contraction.
Conclusions: Within this manuscript, we illustrate fixed point theorems for self-transformations in V-complete vector -metric space by utilizing cyclic contraction. These outcomes are expected to inspire researchers to explore problem-solving opportunities in diverse areas such as differential equations and functional analysis.
Investors Personality Traits and Mutual Fund Investment Decisions: A Regression Analysis of Risk Tolerance and Investment Choices
Authors: Aparna Ger
Published: February 2025

This research examines the factors influencing High-Risk Fund Investment (HRFI) and Low-Risk Fund Investment (LRFI) among individual investors. Through regression analysis on a comprehensive dataset, the study reveals significant relationships between various psychological, educational, and demographic factors and investment behaviour. The findings highlight the importance of age and investment knowledge in promoting risk-taking tendencies, while conservative traits and risk perception significantly impact low-risk investment choices. The robust R-squared values obtained from the regression models indicate a strong predictive capability of the analysed factors on investment decisions. This research provides essential insights for financial advisors and policymakers to enhance investor education and encourage informed decision-making. Future studies should explore additional variables and adopt longitudinal approaches to gain a deeper understanding of evolving investment behaviors in a dynamic financial landscape.
Android Malware Detection and Classification with Analysing Permission API’s using Recurrent Neural Network
Authors: Vijay Sonawane
Published: February 2025

Android applications often contain vulnerabilities that can be exploited by malicious software, making automated malware detection is crucial. Since software vulnerabilities can have severe repercussions, a great deal of research and development effort has been put into developing mitigation strategies. Over the past few years, a number of different approaches have been tried in an effort to lessen the danger posed by vulnerabilities in software. The proposed approach involves multiple stages, including APK recompilation, feature extraction from manifest permissions, model training, testing, and performance analysis. The process begins with APK recompilation, where Android package files are decompiled using specialized tools to extract critical components such as manifest.xml, which contains permissions requested by the application. These permissions serve as key indicators of potential malicious intent. The extracted XML-based permissions and additional metadata are then transformed into structured feature vectors. Feature selection techniques are applied to retain the most relevant attributes, reducing noise and enhancing classification accuracy. The core classification module leverages Recurrent Neural Networks (RNNs) to analyze the extracted features and identify malicious patterns. The model undergoes supervised training using a labeled dataset comprising both benign and malicious APKs. During training, the RNN learns temporal dependencies and feature interactions, improving its ability to detect sophisticated malware. Once trained, the model is tested on a separate dataset to evaluate its classification performance. Various evaluation metrics such as accuracy, precision, recall, and F1-score are used to measure effectiveness. Finally the proposed model is compared with traditional machine learning classifiers and existing deep learning techniques. The results demonstrate that RNN-based classification significantly enhances malware detection accuracy due to its ability to process sequential patterns within application permissions. This research contributes to the field of Android security by presenting an efficient, scalable, and automated malware detection framework, leveraging deep learning for real-time classification of malicious applications.
Transforming Multimodal Sentiment Analysis and Classification with Fusion-Centric Deep Learning Techniques
Authors: Vinitha V
Published: February 2025

Multimodal Sentiment Analysis (MSA) has has become an important field of research, integrating information since text, visuals, video, and speech modalities to derive thorough physiological insights. Despite substantial advancements, current methodologies frequently regard various modalities uniformly, neglecting the preeminent impact of text during sentiment analysis and ignoring address of redundant and irrelevant data generated during multimodal fusion. This study proposes the Enhanced Multi-modal spatiotemporal attention network (EMSAN), to integrate key features across modalities designed to develop the robustness and generalization of sentiment and emotion predictionfrom video data. It consists various phases such as multimodal feature extraction, fusion, and detection of sentiment polarityto integrate key features across modalities. Extensive experiments carried out on the publicly available Multimodal Emotion Lines Dataset (MELD) that show that the suggested method performs with an accuracy of 92.28% in capturing complicated sentiment and emotion. The comparison showed that the suggested method worked better than other baseline models, which made it possible to develop sentiment analysis in a number of different multimodal frameworks.
A Cutting-Edge Security Solution System for Smart Home by Applying the Intelligent AI And BC Framework to Secure Data
Authors: Sameena Shaik
Published: February 2025

Revolutionary technologies like blockchain and artificial intelligence (AI) have come together due to the fast Internet of Things (IoT) growth. Thanks to these technologies coming together, complex infrastructures like "smart homes," "smart cities," and "smart industries" have become possible to build. While IoT gadgets may provide excellent connection and convenience, they often use insecure conventional network interfaces. These older communication protocols and interfaces may be vulnerable if they aren't securely upgraded and protected. This research has developed a strong framework that utilizes the AI along with blockchain technology capabilities to work with the security concerns related to smart home systems (SHS). To start, the research used an isolation forest (IF) algorithm with random segmentation, anomaly score computation, route length, as well as thresholding phases to get rid of the weird data in a normal dataset for SHS. To further categorize the data as either attack or non-attack, the dataset is then used to train classification algorithms including Catboost, K-nearest neighbors (KNN), support vector machines (SVM), and linear discriminate analysis (LDA). When protecting sensitive information from data manipulation assaults, it is also stored in an interplanetary file system (IPFS). In order to save non-attack data safely, IPFS functions as an onsite storage system; the generated hash is then sent to the immutable register of the blockchain. Different performance metrics were used to assess the proposed framework.