bluuebunny/crossref_metadata_2025 huggingface dataset:
EFEKTIFITAS PEMBERIAN PIJAT TUINA DALAM MENINGKATKAN NAFSU MAKAN DAN GIZI PADA ANAK STUNTING (LITELATURE REVIEW)
Authors: Widianti Sherly , Murtiningsih
Published: January 2025

Stunting merupakan salah satu masalah gizi yang masih menjadi tantangan besar di berbagai negara, termasuk Indonesia. Berdasarkan data dari World Health Organization (WHO), sekitar 22% dari anak-anak Indonesia mengalami stunting, yang dapat berdampak jangka panjang terhadap kesehatan, perkembangan kognitif, dan produktivitas di masa depan. Salah satu faktor utama yang menyebabkan stunting adalah kurangnya asupan gizi yang memadai, yang sering kali berkaitan dengan nafsu makan yang rendah pada anak (WHO, 2018). Penelitin ini bertujuan untuk mengetahui efektifitas pemberian pijat tuina dalam meningkatkan nafsu makan dan gizi pada anak stunting. Hasil penelusuran dari database didapatkan 10 artikel yang sesuai dengan kriteria penelitian. Dari beberapa penelitian didapatkan bahwa efektifitas pemberian pijat tuina dalam meningkatkan nafsu makan dan gizi pada anak stunting semuanya efektif dan berhasil menaikkan berat badan dan nafsu makan pada anak. Dengan diketahuinya efektifitas pemberian pijat tuina dalam meningkatkan nafsu makan dan gizi pada anak stunting ini diharapkan menajemen rumah sakit mendapatkan solusi dalam penanganan masalah stunting dengan tehnik non farmakologi sehingga masalah kesehatan dapat teratasi dengan melalakukan pijat tuina.
HUBUNGAN ANTARA PENGETAHUAN DAN DUKUNGAN SUAMI DENGAN INTENSITAS KUNJUNGAN ANTENATAL CARE (ANC)
Authors: Astuti Desti Widya , Puspitasari Yepiza , Eka Pramanda Andini
Published: January 2025

Pemeriksaan antenatal (Antenatal Care/ANC) merupakan salah satu program pemerintah yang bertujuan untuk  mencegah penyebab morbiditas dan mortalitas pada ibu hamil dan anak. Tujuan penelitian adalah untuk mengetahui hubungan antara pengetahuan dan dukungan suami  dengan intensitas kunjungan antenatal care (ANC) di Praktik Mandiri Bidan (PMB) Irma Suryani Kota Prabumulih  tahun 2024. Metode penelitian bersifat analitik, dengan mengunakan pendekatan Cross Sectional. Hasil penelitian berdasarkan analisa bivariat diketahui bahwa dari 36 responden dengan pengetahuan  baik terdapat 33 responden (76,7%) yang melakukan kunjungan antenatal care lengkap sedangkan dari 7 responden yang memiliki pengetahuan kurang terdapat 2 responden (4,7%) yang melakukan kunjungan antenatal care lengkap. Dari 34 responden yang mendapat dukungan suami terdapat 32 responden (74,4%) yang melakukan kunjungan antenatal care lengkap sedangkan dari 9 responden yang tidak mendapat dukungan suami terdapat 3  responden (7%) yang melakukan kunjungan antenatal care lengkap. Simpulan ada hubungan yang bermakna antara pengetahuan  dengan kunjungan antenatal care dengan nilai Pvalue 0,001< ? 0,05 dan ada hubungan yang bermakna antara dukungan suami dengan kunjungan antenatal care dengan nilai Pvalue 0,000< ? 0,05
HUBUNGAN PENGETAHUAN DAN SIKAP IBU HAMIL DENGAN KEPATUHAN IMUNISASI TETANUS TOXOID (TT)
Authors: Anggriani Gustika , Septiana Maria , Anggella Ariesta
Published: January 2025

Imunisasi toxoid tetanus (TT) adalah suntikan vaksin tetanus untuk meningkatkan kekebalan sebagai upaya pencegahan terhadap infeksi tetanus.Tujuan penelitian adalah untuk mengetahui hubungan pengetahuan dan sikap ibu hamil dengan kepatuhan imunisasi tetanus toxoid (TT) di BPM Umi Kalsum Kota Prabumulih  tahun 2024. Metode penelitian bersifat analitik, dengan mengunakan pendekatan Cross Sectional. Populasi penelitian ini adalah seluruh ibu hamil yang berkunjung di BPM Umi Kalsum saat melakukan penelitian bulan Maret tahun 2024. Pengambilan sampel pada penelitian ini menggunakan metode teknik accindetal sampling yaitu sebanyak 40 orang. Instrumen penelitian berupa kuesioner. Hasil penelitian berdasarkan analisa univariat diketahui bahwa dari 40 responden terdapat 28 responden  (70%) yang melakukan imunisasi tetanus dan 12 responden (30%) yang tidah melakukan imunisasi tetanus, terdapat 28 responden (70%) yang memiliki pengetahuan baik dan  12  responden (30%) yang memiliki pengetahuan kurang, terdapat 30 orang (75%) yang memiliki sikap mendukung dan  10 responden (25%) yang memiliki sikap tidak mendukung. Dari  28 responden yang memilliki pengetahuan baik terdapat 24 responden (60%) melakukan imunisasi TT sedangkan dari 12 responden yang memiliki pengetahuan kurang terdapat 4 responden (10%) melakukan kepatuhan imunisasi TT dan Dari 30  responden yang memiliki sikap mendukung terdapat 25 orang (62,5%) yang melakukan imunisasi TT sedangkan dari 10 responden yang memiliki sikap tidak mendukung terdapat 3 orang (7,5%) melakukan imunisasi TT. Simpulan ada hubungan yang bermakna antara pengetahuan dengan kepatuhan imunisasi tetanus toxoid (TT) dengan nilai Pvalue 0,003< ? 0,05 dan ada hubungan yang bermakna antara sikap ibu dengan kepatuhan imunisasi tetanus toxoid (TT) dengan nilai Pvalue 0,005< ? 0,05
EFEKTIFITAS RUMPUT LAUT UNTUK PENINGKATAN KADAR HEMOGLOBIN PADA IBU HAMIL
Authors: Gustiani Ria , Qudratullah Fyzria
Published: January 2025

Badan kesehatan dunia (World Health Organization/WHO) melaporkan bahwa prevalensi ibu hamil yang mengalami defisiensi besi sekitar 40%, hal ini semakin meningkat seiring dengan pertambahan usia kehamilan. Kadar hemoglobin normal pada ibu-ibu hamil adalah 11 gr/ mmHg. Anemia dalam kehamilan sebagian besar disebabkan oleh kekurangan zat besi. Pencegahan anemia pada wanita hamil dapat dilakukan melalui berbagai cara salah satunya dengan mengkonsumsi Rumput Laut (sea weed). Penelitian ini menggunakan literarure review yaitu serangkaian penelitian yang berkenaan dengan metode pengumpulan data pustaka atau penelitian yang objek penelitiannya digali melalui beragam informasi kepustakaan (buku, enslikopedia, jurnal ilmiah, koran, majalah dan koran). Berdasarkan hasil literature riview diketahui bahwa Rumput laut jenis Eucheuma sp merupakan salah satu bahan makanan yang terdapat beberapa senyawa yang dibutuhkan dalam sintesis hemoglobin seperti zat besi, protein dan vitamin B kompleks, Eucheuma sp juga merupakan rumput laut yang mampu menstabilkan jumlah sel-sel darah merah, sel darah putih, dan Hemoglobin. Sehingga sangat efektif untuk meningkatkan kadar hemoglobin pada ibu hamil. Dengan adanya penelitian ini, maka diharapkan dapat menambah wawasan bagi ibu yang sedang  hamil untuk meningkatkan kesadaran anemia pada ibu hamil.
IDENTIFIKASI PENYAKIT TIDAK MENULAR (PTM) MELALUI KADAR GULA DARAH, ASAM URAT, KOLESTEROL PADA MASYARAKAT DI KELURAHAN SUKAJAYA
Authors: Mayumi Gultom Venny , Kurniawan C. Evi
Published: January 2025

Indonesia menghadapi peningkatan beban penyakit tidak menular. Kenaikan prevalensi PTM berkaitan erat dengan pola hidup, seperti kebiasaan merokok, kurangnya aktivitas fisik, serta rendahnya konsumsi buah dan sayur. PTM yang perlu dicegah adalah diabetes melitus, asam urat dan kolesterol. Pemeriksaan kesehatan secara rutin pada perilaku CERDIK sangat penting untuk mendeteksi gejala penyakit sejak dini sekaligus mengurangi biaya pengobatan. Tujuan penelitian ini untuk mengidentifikasi PTM melalui kadar gula darah, asam urat dan kolesterol masyarakat. Penelitian ini merupakan Penelitian kuantitatif dengan desain cross sectional. Populasi adalah seluruh penduduk di wilayah Penelitian. Sampel yang digunakan pada Penelitian ini diambil menggunakan teknik purposive sampling ditentukan yaitu sejumlah 83 orang dengan kriteria inklusi yang ditentukan. Hasil penelitian ditemukan kadar gula darah 2 jam sesudah makan katagori normal yaitu 75 orang (90.4%), pra diabetes 6 orang (7.2%), diabetes 2 orang (2.4%). Kadar asam urat rentang normal yaitu untuk laki-laki mayoritas 29 orang (34.9%), katagori tidak normal ? 7mg/dL sebanyak 15 orang (18.1%), perempuan rentang normal 30 orang (36.1%), tidak normal (? 6 mg/dL (10.8%). Kolesterol dalam darah mayoritas dalam rentang normal ? 200 mg/dL sebanyak 57 orang (68.7%) katagori sedang 26 orang (31.3%). Disarankan untuk masyarakat rutin menjalani pemeriksaan laboratorium mandiri atau ke fasilitas kesehatan terdekat, memperhatikan pola hidup sehat, seperti mengonsumsi makanan bergizi dan berolahraga secara teratur.
HUBUNGAN PENGETAHUAN DAN PERSONAL HIGYENE TERHADAP KEJADIAN KEPUTIHAN PADA IBU HAMIL DI KLINIK BP ANNISA BANYUASIN
Authors: Dewi Ratna , Ernawati Wahyu , Septiani Tety
Published: January 2025

Berdasarkan data WHO, keputihan pada ibu hamil sebesar 31,6% disebabkan oleh candida albicans. Keputihan fisiologis pada ibu hamil termasuk normal, maka ibu hamil harus melakukan personal hygiene didaerah genetalia selama hamil agar tidak terjadi keputihan patologis karena dampaknya dapat menyebabkan terjadi infertilitas, radang penyakit panggul, kelahiran premature, BBLR, KPD, serta dapat menyebabkan terjadinya kanker rahim, kehamilan ektopik, kebutaan pada bayi. Penelitian untuk mengetahui hubungan pengetahuan dan personal hygiene terhadap kejadian keputihan pada ibu hamil di klinik BP Annisa Banyuasin. Desain penelitian survey analitik dengan pendekatan cross sectional, populasi penelitian seluruh ibu hamil yang melakukan pemeriksaan kehamilan di klinik BP Annisa banyuasin. Teknik sampel total sampling berjumlah 85 orang. Instrumen penelitian kuesioner. Data dianalisis menggunakan Uji Chi Square. Penelitian univariat diketahui ibu hamil yang pengetahuan baik 23 (27,1%) dan pengetahuan kurang baik 62 (72,9%), personal hygiene ibu hamil yang baik 33 (38,8%) dan personal hygiene kurang 52 (61,2%), ibu hamil yang mengalami keputihan 49 (57,6%) dan yang tidak mengalami keputihan 36 (42,4%). Hasil analisis bivariat ada hubungan yang bermakna pengetahuan terhadap keputihan pada ibu hamil dengan nilai P=0,000 < ? (0,05) dan OR=11,600 dan ada hubungan yang bermakna personal hygiene terhadap keputihan pada ibu hamil dengan nilai P=0,000 <? (0,05) dan OR=28,519.  Kesimpulan ada hubungan yang signifikan antara pengetahuan ibu dan personal hygiene terhadap kejadian keputihan pada ibu hamil.
Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling
Authors: Dullah Ahmad Ubai , Utami Putri , Unjung Jumanto
Published: January 2025

Asthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research.
Prototyping Disaster Preparedness Information System: A Case of Pandeglang District, Indonesia
Authors: Juanara Elmo , Maeda Yasunobu , Hakim Ade Anggian
Published: January 2025

In December 2018, the tsunami caused by the eruption of Anak Krakatau Volcano (AKV) hit the coastal area of Pandeglang Indonesia. The tsunami killed hundreds of people and thousands were missing.  That moment teaches the importance of raising tsunami awareness by doing disaster preparedness and education. However, Pandeglang society still lacks disaster preparedness. This is evident from the absence of early warning and evacuation initiatives at the time of the tsunami. This research aims to develop a disaster preparedness information system to equip society with sufficient knowledge and skills in case of the next disaster. The method this research uses is Soft Systems Methodology (SSM) to obtain system requirements for the development of prototypes. The prototype of disaster preparedness information systems was developed as a result. The system can be accessed by using a smartphone or computer.
Inclusão Digital da Pessoa Idosa III: direito e interdisciplinaridade
Authors: Azevedo Ione Galoza de
Published: January 2025

“Nesta obra, explora-se como a tecnologia pode transformar a vida dos idosos, promovendo inclusão e autonomia. Com uma abordagem acessível, os autores apresentam iniciativas inspiradoras, como o projeto de extensão da FAMESC, em Bom Jesus do Itabapoana, que demonstra o poder da inclusão digital na prevenção do declínio cognitivo.

Os capítulos abordam a relevância das tecnologias assistivas, o impacto do endividamento e a importância das interações sociais para a qualidade de vida. Além disso, discute-se a educação em saúde e os direitos digitais, trazendo à tona reflexões sobre a espiritualidade na era virtual.

Este livro é um convite à reflexão e à ação, estimulando o leitor a compreender e promover uma sociedade mais inclusiva e conectada. Junte-se a essa importante conversa e descubra como cada um de nós pode fazer a diferença na vida dos idosos.”

Dr. Carlos Henrique Medeiros de Souza (Mantenedor da Faculdade Metropolitana São Carlos – FAMESC)
Newton Cotes Quadrature R-I Model for the Numerical Simulation of Nonlinear Equations
Authors: Inderjeet
Published: January 2025

An innovative set of closed Newton-Cotes quadrature techniques for numerical integration is introduced, using the value of arithmetic mean derivative at the midpoint. The existing closed Newton cotes quadrature technique and the computation of numerical integration using arithmetic mean derivative-based closed Newton cotes quadrature techniques are compared. Additionally, it is demonstrated that the proposed quadrature rule provided more precise solutions and an increase the order of precision (accuracy) over traditional closed Newton-Cotes quadrature formula. Error terms for the proposed approach and the existing approach are compared. Lastly, a few numerical examples show that the suggested approach's numerical superiority over an existing closed Newton-Cotes quadrature formula.
Cubic Spherical Neutrosophic Geometric Weighted Bonferroni Mean Operator for Selecting a Machine using MCDM Techniques
Authors: S. Sathyapriya
Published: January 2025

Neutrosophic cubic fuzzy sets (NCFSs) involve interval-valued and single-valued neutrosophic sets and are used to describe uncertainty or fuzziness more efficiently. The aggregation of neutrosopic cubic fuzzy information is crucial and necessary in a decision-making theory. To get a better solution to decision-making problems under a neutrosophic cubic fuzzy environment. The main objective of this study propose the Cubic Spherical Neutrosophic Geometric Bonferroni Mean Operator (CSNGBM) and the Cubic Spherical Neutrosophic Geometric Weighted Bonferroni Mean Operator (CSNGWBM) to find the best alternatives. In this study considers the 15 alternatives D1 to D5 and criteria ζ1 to ζ5 and to make the decision matrix deep interviews and collected opinions from three experts are:1) Industry Professionals 2) production and sales 3) technical advisers. Finally, the outcome of the results to find the greatest similarity value are taken to be the best alternatives by using neutrosophic cubic FMCDM.
Innovative Fuzzy Logic Methods for Congestion Control in Vehicular Ad-Hoc Networks
Authors: Animesh Kumar Jain
Published: January 2025

This research investigates the use of state-of-the-art fuzzy logic-based techniques for congestion control in vehicular ad hoc networks (VANETs), as VANETs are dynamic networks with high mobility and frequent topology changes. In time, it can therefore be difficult to maintain optimum performance using specific signal management strategies. The recommended algorithm uses real-time data and expert knowledge to dynamically change the transmission rate in response to changing network conditions. The main goal is to increase the number of s along with the fact that the method promises to be flexible and scalable in various VANET settings. The fuzzy logic method shows good results through several detailed simulations and performance comparisons with machine learning-based traditional ones. Hybrid An interesting algorithm is recommended technique achieves speeds of 20.1 Mbps, surpassing the current techniques with impressive growth above. Moreover, Packet loss is reduced to 4.7%, promoting reliable communication. And latency is reduced to 160 milliseconds, guaranteeing faster data transfers. The scalability of the algorithm (score 9) makes it ideal for expanding network demands in smart transportation systems. While resilient (score 9), future research directions include integrating additional environmental factors such as weather and road conditions. To further improve decision-making that guarantees smooth performance across a wide range of traffic conditions. By incorporating adaptive learning techniques into the fuzzy logic framework, the system may be able to develop and get better over time. Additionally, investigating hierarchical control structures might provide multi-layered network settings with more effective management. Validating the simulation results will require real-world installations and resolving privacy and security issues will be essential to guaranteeing dependable and secure operation in real-world situations. The outcomes demonstrate how well the suggested strategy works to achieve increased throughput, reduced latency, and less packet loss while maintaining the flexibility and scalability needed for upcoming vehicular networks.
A Novel Approach to Eliminate the Dead Code and Change the Looping Structure for Optimizing the Code
Authors: Tulshihar Patil
Published: January 2025

This article presents OptiCode, a complicated programming instrument that utilizes circle invariant code portability and dead code decrease, among other high-level code streamlining strategies, to develop code effectiveness further and reduce order time. Utilizing Circle Invariant Code Movement (LICM) and Conceptual Punctuation Trees (ASTs) for exact code examination, OptiCode productively distinguishes and kills repetitive code, as well as improves circle structures by eliminating 4.86% of dead code with a proficiency of 5.39% OptiCode outflanks other applications in correlation, as seen by its impressive arrange time reserve funds and amazing effectiveness appraisals. The outcomes show how significant these enhancement methods are for improving programming practicality and speed, furnishing designers with a valuable instrument for codebase streamlining.
Introduction: Code improvement alludes to the most common way of working on the effectiveness, execution, and nature of PC code without adjusting its usefulness or conduct. The goal of code advancement [29] is to produce code that executes quicker, consumes less memory, and has qualities, for example, further developed comprehensibility or practicality [5]. Some normal improvement strategies incorporate dead code end [11,30] and code invariant movement.
Objectives: To optimize the code by introducing the change in loop structure and removing the dead code.
Methods: Identification of the loops and unusful code is a challenging task. Once the problem is found, it is easy to mitigate the same. The dead-code optimization is introduced by identifying the non-performing, inaccessible code and deleting the same.
Results: The results of the experiment are satisfactory. The improvement in the performance by 6.96% is observed just by removing the approximately removing 5% code.
Conclusions: The identification and removal phase helps in the better performance of the code. The code optimization can save the space, time, human resources, and cost incurred during the development of the project. The improvement in the code also directly contributes into the efficiency of the ultimate code.
Micro Pre Separation Axioms in Circuit of Light System on Four Stroke Engines
Authors: P. Sathishmohan
Published: January 2025

The basic objective of this research work is to introduce and investigate the properties of micro-T0, T1, T2 spaces, micro semi-T0, T1, T2 and micro pre- T0, T1, T2 spaces using the concept of micro open, micro semi-open and micro pre-open sets and comparing them to the circuit system of light in four stroke engines.
Introduction: Levine’s introduction of generalized closed sets in 1970 [7], providing a foundational framework for subsequent developments. In 1975, Maheshwari and Prasad [9] gave the idea about the s-normal spaces and in 1990, Ashish Kar and Bhattacharyya [1] introduced the concept of weak separation axioms. In 1995, Chattpadhyay [3] investigated some properties of pre-T0, pre-T1 and pre-T2 spaces in topological spaces. In 2013, Lellis Thivagar [6] defined the new concept of Nano topology based on generalized closed sets. In 2019, Sathishmohan et al., [10] established the concepts of nano pre-T0, nano pre-T1 and nano pre-T2 spaces and obtain many interesting results. In 2019, Chandrasekar [2] introduced the concept of Micro topology which is a simple extension of nano topology with focus on micro pre-open and micro semi-open sets. In 2020, Hariwan Z. Ibrahim [4] introduced the separation axioms on micro topology with particular focus on micro open sets. In 2022, Wei Liu et al., [12] established the importance of battery in electric vehicles and at 2024, Liu. H et al., [8] studied the control of angle formation based on circuit diagrams. This motivates us to introduce and study some new concepts of micro-T0, T1, T2 spaces, micro semi-T0, T1, T2 spaces and micro pre- T0, T1, T2 spaces combining with the circuit of the four stroke engines.
Conclusions: In this paper, we introduced the notions of micro semi-T0, T1, T2 and micro pre-T0, T1, T2 by employing the concepts of separation axioms elucidating various associated properties. Based on the theorems and results, I have concluded that circuit of the light system in vehicles and the study of my spaces are more closely related to each other. Our intent is to further elaborate on those findings in forthcoming research endeavors, with a particular focus on exploring practical applications.
Design of an Improved Method for Personalized Learning Using Deep Q-Learning and CNN-Based Engagement Recognition
Authors: Lucky Haridas Sadrani
Published: January 2025

Adoption of personalized learning systems is on the rise. Traditional approaches to education, in most cases, cannot fulfill the diversified requirements of different learners and target various levels of engagement. Current methods are mainly focused on defined learning paths or limited information regarding levels of engagement, hence failing the adaptability test, which prevents real-time understanding and leveraging of data in such cases. This paper fills in the identified gaps by proposing a holistic, multi-faceted learning system and incorporating three advanced machine learning techniques: Deep Q-Learning for personalized content recommendations, Random Forest Classifier for predictive academic analytics, and Convolutional Neural Networks (CNNs) for real-time engagement tracking. Deep Q-Learning is used for adaptive providing learning resources by using the performance metrics of students in quizzes and the timestamp spent on tasks to optimize learning paths based on real-time feedback. Random Forest Classifiers will be used as they are robust for predicting academic success by using historical academic data as well as the engagement metrics gathered from smart classrooms. This includes the application of CNNs in visual engagement recognition, such as detecting a child's attention levels based on facial expressions and body language. Real-time engagement data is fed back into the learning system to further refine the contents recommended and academic predictions made. A dynamic and highly personal environment in the learning process is created through these practices by virtue of the continuous optimization of content and predictions based on individual student interactions. Preliminary results so far indicate a likely 20-30% improvement in learning outcomes, with around 85-90% accuracy in academic predictions, and a 15-20% increase in classroom engagement. This is highly sophisticated intelligent learning and offers best tools for faculty to improve performance and engagement.
Harnessing Blockchain to Safeguard Educational Integrity: A Transparent Future for Learning
Authors: Ashwani Goyal
Published: January 2025

This research paper explores the transformative potential of blockchain technology for educational integrity and transparency by presenting a feedback-oriented adaptive prototyping algorithm. Academic institutions face increasing challenges related to academic dishonesty. data security and identity verification. This requires a robust blockchain-based solution. Feedback-focused adaptive prototyping algorithms offer an open approach to developing non-user-centered educational models through iterative feedback loops from stakeholders. Relevant to students, educators, and administrators, a comparative examination with three other research approaches highlights the effectiveness and efficiency of feedback-based adaptive prototyping. The main presentation indicators demonstrate the success of the algorithm. Reduce the duration of use User Requirement Success Rate 85% Prototype Quality Index 0.92, decreased bug density. (increases user confidence), 20% cost reduction (emphasizes economic viability) and 95% transaction transparency frequency (promotes confidence in the integrity of academic results). Prototyping algorithms that Driven by adaptive feedback, it increases user confidence. Promote transparency and ensuring the integrity of academic records. These findings highlight the potential of blockchain to revolutionize educational practices. Guaranteed accuracy and reliability This research encourage continued exploration and deployment of blockchain solutions in education. The feedback-oriented adaptive prototyping algorithm offers a robust framework for educational institutions to address pressing challenges. Its implications extend to various educational contexts, emphasizing the importance of blockchain technology in safeguarding educational integrity. This study contributes to the increasing body of research on blockchain applications in education, highlighting the transformative potential of this technology in determining a transparent and trustworthy future for learning.
Enhancing Sports Pose Estimation with Unsupervised Domain Adaptation and Occlusion Handling
Authors: Zhihao Zhang
Published: January 2025

Introduction:In sports analysis, accurate motion pose estimation is crucial for enhancing athletes' technical performance and optimizing training programs.
Objectives:The scarcity of data in sports scenarios limits the generalization ability of existing models, especially when there are significant performance differences across different venues and conditions, making it difficult to meet practical application requirements. To address this challenge, this study proposes an innovative method based on unsupervised domain adaptation—the Subspace Adaptation Network (SAN).
Methods:  By minimizing the distance between the subspaces of motion data from the source and target domains, the network effectively mitigates the issue of cross-domain data distribution differences, significantly improving the model's adaptability in scenarios with limited labeled data. Additionally, we introduce a Scale Consistency Loss (SCL) function to ensure scale consistency across different sports scenarios, further enhancing the model's cross-scene generalization performance. To tackle the occlusion problem commonly encountered during training and competition, we designed a Worst-Case Estimation Module (WCEM) to specifically optimize the model's motion prediction performance in complex occlusion situations..
Results: Experimental results show that the proposed method achieved significant performance improvements in extensive evaluations on public datasets such as LSP, Human3.6M, PoseTrack2017, and PoseTrack2018.
Conclusions: These results validate the robustness and effectiveness of the proposed method in unsupervised domain adaptation and complex sports scenarios.
Study of Cycle-Path Connected graph by applying degree-based Topological Indices
Authors: B.K.Keerthiga Priyatharsini
Published: January 2025

Topological indices are tremendously advantageous for assaying colorful physicochemical stress parcels connected with a chemical emulsion. A topological index describes molecular structures by covering them into certain real figures. By converting chemical structures into numerical descriptors, these indices facilitate the development of quantitative structure-activity relationships (QSARs), which is the natural exertion of patches identified with their arrangements of chemical components. In this composition, we will cipher the topological indicators of the cycle-path connected graph. In addition, comparisons of the cycle-path connected graph dispense numerically and graphically.
Theoretical Framework of Financial Management Skills Among Special Needs Blind Students
Authors: Muhamad Abrar Bahaman
Published: January 2025

This review article explores the theoretical frameworks that underpin the development of financial management skills among special needs blind students. By examining various theoretical perspectives, including the Social Model of Disability, Human Capital Theory, and the Lifelong Learning Approach, this paper aims to highlight the importance of these frameworks in designing effective financial education programs. The article also discusses the implications of these theories for practice and policy, emphasizing the need for inclusive educational strategies that empower visually impaired individuals. Through this analysis, we aim to contribute to the understanding of financial management education within the context of special needs, ultimately fostering greater independence and economic empowerment for blind students.
M-open Maps and its Applications in Pythagorean Fuzzy Topological Spaces
Authors: B. Vijayalakshmi
Published: January 2025

In this paper, we introduce and investigate Pythagorean fuzzy M-open and closed maps in Pythagorean fuzzy topological spaces and also discuss about some properties and characterization of Pythagorean fuzzy maps. Also one real life applications, we applied entropy measure for decision making problem of diet selection based on the performance.
Resource Management in NOMA-based Wireless Communication using Hybrid Transit Search Archimedes Algorithm
Authors: Prasheel Thakre
Published: January 2025

Non-Orthogonal Multiple Access has proved to be one of the best candidate technology that can be used in 5G and next-gen wireless communication. NOMA allows sharing of same resources like time slot and frequency band with multiple users simultaneously but with differential power to extend spectral efficiency, large connectivity and fairness. To optimize the network performance adoption of a hybrid algorithm for resource and power allocation in NOMA is one of the big challenges to surge in demand for efficient resource management in next-gen wireless communication. This paper introduces a novel hybrid approach in NOMA, the Hybrid Transit Search Archimedes Algorithm (HTSAA-NOMA) for resource management in a network. The proposed algorithm combines the global exploration capabilities of the Transit search algorithm with the convergence capability of the Archimedes Algorithm ensuring optimal power allocation and subcarrier assignment. The effectiveness of the HTSAA-NOMA is validated by comparing its simulation results with individual optimization algorithms. The results demonstrate the significant improvement in fairness, throughput and computational efficiency. The paper offers the comprehensive framework for advancing resource allocation for next-gen wireless communication.
Cryptocurrency Price Prediction using Deep Learning Algorithms: A Comparative Study
Authors: Pappula Ashok
Published: January 2025

Introduction: Due to financial technology advances, cryptocurrency is a new asset with great potential for academics. Price volatility and dynamism make Cryptocurrencies prediction difficult.
Objectives: This paper presents three Recurrent Neural Network (RNN) methods for predict Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) prices to predict the price of Cryptocurrency.
Methods: This study uses three RNN algorithms LSTM, Bi-LSTM, and GRU to predict the prices of Bitcoin (BTC), Binance Coin (BNB), and Ethereum. (ETH)
 Results: BTC, ETH, and BNB prices are predicted using three machine learning algorithms. The model's accuracy was assessed using performance metrics. We followed by comparing actual and predicted prices of models. The GRU algorithm surpassed its competitors, attaining MAPE values of 2.562795642 for BNB, 2.921155091 for ETH, and 3.363400599 for BTC.
Conclusion: The value of cryptocurrency swings regularly. Since the cryptocurrency market is nonlinear, time series data is challenging to evaluate when making price predictions. Machine learning algorithms are used in many financial and economic activities. Investors, researchers, and professionals need cryptocurrency value forecasts. In this study, GRU outperformed than LSTM and Bi-LSTM algorithms in predicting prices of BTC, ETH and BNB.
Analyzing the Dynamics of HIV/AIDS Phobia with Stability and Awareness Strategies
Authors: Naresh Kumar Jothi
Published: January 2025

One of the risks to global health is sexually transmitted diseases (STDs). Usually, diseases do not cause death, but 50% of deaths are caused by fear of disease. This causes policymakers and experts studying diseases to become increasingly concerned. In this section we discuss about HIV/AIDS phobia, a particular type of nosophobia is an excessive and illogical dread of contracting HIV/AIDS. It is still a major global health concern due to its high death rate, and in most African nations as well as other places, it is the main source of HIV/AIDS anxiety. We develop a five-component deterministic model to examine how phobia affects HIV/AIDS dynamics within a particular population. The system's HIV/AIDS-free equilibrium is considered asymptotically stable when the effective reproduction number R_0< 1, and unstable in other cases. Additionally, we used to investigate the endemic equilibrium's stability by using the Lyapunov function, positivity, boundedness, Lipschitz condition, and the conditions for the existence of uniqueness are discussed. The outcome demonstrates that everyone must have awareness about HIV/AIDS. Unfortunately, those affected with HIV/AIDS don’t fear; take healthy food, and medicines; and exercise regularly; the counselling about HIV/AIDS will prolong life and reduce the phobia of HIV/AIDS in the population. The HIV/AIDS phobia epidemic model is analyzed by way of Matrix Laboratory.
Revolutionizing Highway Navigation: Exploring the Potential of LIFI Technology
Authors: B. Ramana Kumar
Published: January 2025

This research work tackles the challenge of reliable navigation in congested urban environments by proposing a novel system that combines LiFi and LoRa technologies. In existing methods of the navigation there are complications with interference with the infrastructure and limited bandwidth so these can be overcome by the presented prototype. The LiFi transmitter which is placed in the streetlights, uses visible light for the transmission of the information of location at particular co-ordinates, which is received by the LiFi receivers equipped in the vehicles, that transmitted information will be directly given to the navigation system, which provide with continuous seamless navigation even in the places where there no service available, moreover this advanced navigation system is also capable of broadcasting the present location of the vehicle through the LoRa module to the outer world, by which the present location of the vehicle can be monitored, thereafter in the prototype, transmitted LoRa signal is received by the LoRa module and displayed in the LCD, this LoRa module has very long range and uses very low power when compare to the cellular network. In the non-serviceable locations like terrines, valleys, tunnels this idea can be a major contribution in the field of navigation.
Enhancing Short- and Medium-Term Electricity Load and Price Forecasting Using a Hybrid CNN-AM Model
Authors: Jaya Shukla
Published: January 2025

By using a unique hybrid model, this study seeks to improve the precision and dependability of short- and medium-term power load and price forecasts in the energy market. The paper tackles the urgent need for sophisticated forecasting tools that can handle the complexity of energy data by combining the ensemble empirical mode decomposition (EEMD) algorithm with a convolutional neural network using an attention mechanism (CNN-AM). By efficiently extracting intrinsic mode functions that are essential for precise predictions, EEMD optimises the decomposition process of raw data. Concurrently, the CNN-AM improves the model's capacity to highlight important aspects of the data, which raises the forecasts' interpretability and accuracy. The model's performance is verified on a variety of datasets, demonstrating its potential for reliable use in practical situations. Future research directions are examined, such as dynamic model updates to adjust to changing market conditions, overall model resilience, and the inclusion of external factors affecting load and price. The ultimate goal of this research is to provide energy industry stakeholders with a reliable forecasting tool that will enable strategic decision-making and well-informed risk management.
Advanced Weather Forecasting with Machine Learning: Leveraging Meteorological Data for Improved Predictions
Authors: Sunil Khatri
Published: January 2025

Objectives: This study proposes a machine learning approach for improving weather forecasting accuracy with reduced resource requirements, focusing on rainfall and flooding predictions in urban regions.
Methods: The study used historical meteorological data (2009–2023) from Indian regions, applying supervised learning models, including regression and ensemble methods. Data preprocessing steps included handling missing values, outlier detection, and normalization. The models were evaluated using Accuracy, MAE and MSE value to determine prediction accuracy and reliability.
Findings: The results indicate that AI models, especially LSTM, provide significant accuracy improvements with 80.11% accuracy. These models performed well in predicting short-term weather phenomena such as rainfall in urban flood-prone areas like Mumbai. The method demonstrated the potential to produce reliable forecasts with limited computational resources. The findings complement existing research, adding value by showcasing the adaptability and scalability of resource-efficient ML models for local meteorological applications. This work highlights the practical implications for urban planning and flood preparedness.
Novelty: A cost-effective machine learning framework for accurate local weather forecasting, addressing scalability and computational constraints.
Estudio de la asociatividad en las MIPYMES de la provincia de Imbabura- Ecuador, caso de estudio industrias manufactureras
Authors: Yepez Jenyffer , Negrete Karla , Alarcón Marco
Published: January 2025

La investigación realizada a las MIPYMES de la provincia de Imbabura a través de la aplicación de una encuesta enfocada en conocer si las MIPYMES tienen conocimiento de la asociatividad y los beneficios que genera, busca identificar si las empresas se encuentran interesadas en gestionar un modelo de consorcio asociativo en función del sector económico en el cual se encuentran clasificadas de acuerdo con la actividad laboral realizada, para ello se dividió la investigación en fases. Fase I. identificación de la población y selección de la muestra mediante fórmulas estadísticas y mayor número de empresas de acuerdo al sector económico y cantón obteniendo una muestra de 328 para fines de la presente investigación, fase II, se realizó el levantamiento de información a través de la aplicación de una encuesta en donde se recopiló información acerca de la planificación estratégica gestionada en cada MIPYME y se identificó que un número bajo de empresas cuentan con una planificación estratégica y estructura organizacional, fase III con la finalidad de analizar si las empresas se encuentran interesadas en formar consorcios asociativos se elaboraron seis preguntas, de las cual se obtuvo que existe desconocimiento de los beneficios que otorga un consorcios asociativo y que se encuentran interesadas en gestionar actividades para mejorar su posicionamiento tanto nacional como internacionalmente.
Rezension von: Zwirello, Martin, Die sozialen, wirtschaftlichen und rechtlichen Beziehungen zwischen den oberschwäbischen Reichsstädten Ulm, Biberach und Ravensburg und ihren geistlichen Institutionen im Spätmittelalter
Authors: Klaus Graf , Zwirello Martin
Published: January 2025

Martin Zwirello: Die sozialen, wirtschaftlichen und rechtlichen Beziehungen zwischen den oberschwäbischen Reichsstädten Ulm, Biberach und Ravensburg und ihren geistlichen Institutionen im Spätmittelalter. Konstanz: UVK 2018; XXXIV + 280 S.; 59,00 EUR
Rezension von: Herkle, Senta u.a. (Hrsg.), 1816 - das Jahr ohne Sommer
Authors: Hauke Marie-Kristin , Herkle Senta
Published: January 2025

Senta Herkle/Sabine Holtz/Gert Kollmer-von-Oheimb-Loup (Hg.): 1816 – Das Jahr ohne Sommer. Krisenwahrnehmung und Krisenbewältigung im deutschen Südwesten. (Veröffentlichungen der Kommission für Geschichtliche Landeskunde in Baden-Württemberg, Reihe B: Forschungen 223). Stuttgart: W. Kohlhammer Verlag 2019; 260 S., geb., 8 Farb-, 28 s/w-Abb. 28,00 EUR
CREACIÓN DE UN CHATGPT PERSONALIZADO PARA LA MATERIA NARRATIVAS DIGITALES DE LA MAESTRÍA EN ENTORNOS DIGITALES DE LA UBE
Authors: Avello-Martínez Raidell
Published: January 2025

Este artículo examina la implementación de “Todo sobre Narrativas Digitales”, un ChatGPT personalizado, dentro de la Maestría en Educación con Mención en Entornos Digitales en la Universidad Bolivariana del Ecuador, involucrando a 57 estudiantes del grupo Paralelo 23 durante el año académico 2022-23. La herramienta fue diseñada para facilitar la comprensión y producción de narrativas digitales entre los estudiantes. A través de la observación directa y la recopilación de comentarios textuales, se evaluó la utilidad de la herramienta en el aprendizaje autónomo y su impacto en la motivación estudiantil y el desarrollo de proyectos. El estudio identificó desafíos significativos relacionados con la accesibilidad, debido a la necesidad de una suscripción a ChatGPT Plus y la variabilidad en la experiencia previa de los estudiantes con tecnologías similares, lo que afectó la adopción y eficiencia de la herramienta. A pesar de estas barreras, la respuesta general a “Todo sobre Narrativas Digitales” fue positiva, resaltando su potencial para enriquecer la educación digital. El trabajo concluye enfatizando la necesidad de abordar la equidad de acceso y la formación en el uso de herramientas de IA para asegurar una integración educativa efectiva y equitativa.
PARQUE CULTURAL TURUBAMBA: UN PROCESO GESTADO DESDE, PARA Y CON LA GENTE
Authors: Vicente Correa Christian Fernando , Rivera Moyano Pamela Suhan
Published: January 2025

Desde 2013 el Ex Registro Civil de Turubamba, al sur del Distrito Metropolitano de Quito, cerró sus puertas debido a supuestos daños estructurales; en 2017 un grupo de vecinos organizados, de diferentes disciplinas y cargos dirigenciales, empezaron a realizar actividades con el objetivo de darle un buen uso al predio, mismo que venía siendo un foco de inseguridad, insalubridad y mala imagen urbana y que por su extensión, cerramientos y forma, era un borde urbano. Dentro de las acciones como comunidad se realizaron tomas pacíficas del espacio, marchas, ferias, entre otras actividades; es así que desde C3 Arquitectura integral, uno de los colectivos que forman parte del grupo de gestores del proceso, se plantea generar un estudio de la realidad urbana del sector, a la par de un levantamiento de información técnica, administrativa y social, mediante mesas de trabajo, encuestas en colaboración con la Facultad de Arquitectura y Urbanismo de la Universidad Central del Ecuador, en donde se concluyó que la mejor opción sería un equipamiento recreativo – cultural, teniendo después audiencias y hojas de rutas con autoridades, desembocando en la construcción del Parque Cultural Turubamba, inaugurado por el Municipio del Distrito Metropolitano de Quito el 29 de octubre de 2023.
case report on hypothyroidism treated constitutionally with homoeopathic medicine causticum 1M
Authors: Raj Aswathi , Hemapriya R , Amma C.R. Krishna Kumari
Published: January 2025

Hypothyroidism is a condition in which the thyroid gland is not able to produce enough thyroid hormone. Thyroid hormones can disrupt such things as heart rate, body temperature, and all aspects of metabolism1. Hypothyroidism is most prevalent in older women. Major symptoms include fatigue, cold sensitivity, constipation, dry skin and unexplained weight gain. Homoeopathy has a major role in the treatment of hypothyroidism1.Acoording to Homoeopathy best medicine is the simillimum and it acts when the remedy covers the patient constitutionally i.e it is similar to the patient at mind level and physical level. Causticum was selected as the simillimum.
Epistemología de construcción de paz y noviolencia: una investigación social en la UNAM sobre asambleas y paros
Authors: Patella Pietro Ameglio , Figueiredo Myriam Fracchia , Glower Ávila Alma Patricia , Castillo Magaña Reyna Liliana
Published: January 2025

Describimos y analizamos, desde una mirada de la epistemología de paz y de la resistencia civil noviolenta, un proceso de investigación social realizada por estudiantes y profesores de la facultad de Filosofía y Letras de la UNAM (FFyL), en la Ciudad de México, que se aplicó a la comunidad estudiantil, entre agosto del 2022 y septiembre del 2023, acerca de qué pensaba la comunidad estudiantil respecto a las tradicionales formas de representación estudiantil que operaban  (asambleas y paros para el cumplimiento de pliegos petitorios) y qué sugerencias ofrecían para dinamizar y avanzar en las demandas y organización estudiantil. El objetivo fue ofrecer ejes conceptuales y operativos de cómo en la academia es posible construir colectivamente dispositivos de investigación y conocimiento noviolentos, que puedan ser desarrollados para la formación de los jóvenes activistas sociales que además, permiten ejercer formas legítimas y científicas de presión hacia las autoridades o grupos de poder involucrados en las conflictividades internas, en la búsqueda conjunta de soluciones.
Fluid Evolution and Cu-Co Ore-Forming Processes in the Katanga Copperbelt (DR Congo)—Insights from Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Analyses of Fluid Inclusions
Authors: Torres Jessica , Muchez Philippe
Published: January 2025

This study focuses on the mineralization and fluid evolution of deposits in the Katanga Copperbelt, DR Congo, where two main hypogene mineralization stages have been proposed. Each of these events presents distinct signatures of fluid salinity and temperature: the first occurred during diagenesis, with a low salinity of 1.2 to 20.9 wt % NaCl equiv and a low temperature of 80° to 252°C; the second took place during maximum burial and orogenesis of the sedimentary strata, with a higher salinity of 32.3 to 59.6 wt % NaCl (+ KCl) equiv and higher temperature of 209° to 359°C. These later high-temperature, high-salinity fluids pervasively affected the first stage of nodules and lenses hosting ore minerals, which is confirmed by the extensive recrystallization features of carbonates in mineralized nodules and veins, reflecting multiple growth phases. Elemental composition assessed through laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analyses in fluid inclusions provides evidence for fluids enriched in K, Ba, Cu, Co, Pb, and Zn. Albitization of K-feldspar during water-rock interactions most likely caused the strong enrichment in potassium and barium. The high metal concentrations measured in the fluid inclusions are interpreted to result from the intense leaching of metals from the basement rocks and the overlying siliciclastic sedimentary sequence by descending Cl-enriched brines.