bluuebunny/crossref_metadata_2025_split huggingface dataset:
Infant and Young Child Feeding Practices among Mothers Attending a Government Hospital
Authors: Rai Munawatee , Bhandari Menuka , Khanal Ranjeeta 
Published: February 2024

Background: Appropriate young children (from birth to two years of life) feeding practice is very crucial to boost up children’s survival and optimum growth and development. Early initiation of breastfeeding (within an hour of childbirth), exclusive breastfeeding for six months and providing nutritionally adequate and appropriate complementary feeding for six months and continued breastfeeding up to two years of age or beyond are the major components of infant and young child feeding (IYCF) practices. The objective of the study was to find out the IYCF practices of mothers.
Methods: Descriptive cross-sectional research was used and all 180 mothers attending government hospital of Biratnagar were the sample. Interview-based data collection was used by a self-developed, structured and semi-structured questionnaire. Data were analyzed using descriptive statistics using SPSS version 25.0.
Result: Less than half (40%) mothers started breastfeeding within an hour after delivery, 90.6% fed colostrum, 73.3% practiced exclusive breast feeding and 89.4% continued to feed breastmilk along with complementary feed to under two years children. Among mothers, 68.3% started feeding complementary food at appropriate age, 70.6% of the mothers fed minimum numbers of time or more according to their child age, 80% mothers used to provide diverse food and 66.1% children were receiving minimum acceptable diet.
Conclusion: The prevalence of timely initiation of complementary feeding at 6 months, minimum dietary diversity, minimum feeding frequency and minimum acceptable diet were satisfactory as almost two third and more children have met minimum acceptable diet. However, practice of early initiation of breast feeding is still low.
Pelatihan Desain Grafis Dengan Aplikasi Canva Untuk Anggota AMI di Masa Pandemi Covid 19
Authors: Leliana Intan , Pramelani Pramelani , Sartika Rawit , Shidiq Fajar 
Published: February 2024

Pengabdian kepada masyarakat merupakan pelaksanaan pengamalan ilmu pengetahuan, teknologi dan seni budaya langsung pada masyarakat. Tahun 2021 ini program studi Hubungan Masyarakat Universitas Bina Sarana Informatika diberikan kesempatan untuk melakukan Pelatihan Desain Grafis Dengan Aplikasi Canva Untuk Anggota Asosiasi Museum Indonesia (AMI) Di Masa Pandemi Covid 19 yang beralamat di Sekretariat Museum Bank Mandiri, Jakarta Barat dengan peserta Anggota dari Asosiasi Museum Indonesia (AMI) DKI Jakarta”Paramita Jaya”. Museum memiliki peranan besar bagi sumber pengetahuan dan wawasan. Selain sebagai sarana rekreasi, dengan mengunjungi museum, masyarakat juga dapat memiliki referensi visual sekaligus merasakan peristiwa sejarah di masa lalu. Pengunjung merupakan salah satu unsur terpenting dari tujuan pendirian suatu museum. Pengunjung dan museum ibarat dua sisi yang tidak dapat dipisahkan. Oleh karena itu, berbagai museum berlomba-lomba untuk menarik atensi pengunjung. Salah satu elemen fundamental untuk mengusik rasa ketertarikan pengunjung adalah penyajian koleksi yang ditunjang dengan tata pamer yang apik. Selain itu, faktor lain yang menarik minat masyarakat untuk singgah ke museum adalah melalui publikasi terbitan yang memuat berbagai berita dan tulisan yang berkaitan dengan museum, seperti bahasan koleksi serta program publik yang disajikan di museum tersebut. Oleh karena itu penting desain menarik untuk menarik pengunjung sangat dibutuhkan. Solusi untuk mengatasi permasalah tersebut yakni dengan mengadakan pelatihan Desain Grafis menggunakan Aplikasi Canva diharapkan dapat memudahkan Para anggota dan manajemen Asosiasi Museum Indonesia (AMI)”Paramita Jaya” dengan mudah merancang berbagai material kreatif atau mendesain poster, brosur bahkan infografik atau video secara online meskipun dalam Masa Pandemi Covid-19 Ini.
Perbandingan Algoritma Klasifikasi dalam Analisis Sentimen Opini Masyarakat tentang Kenaikan Harga Bbm
Authors: Basedt Ngabdul , Supriyadi Eko , Nugroho Agus Susilo 
Published: February 2024

Kenaikan harga bahan bakar minyak (BBM) telah menjadi permasalahan yang cukup kompleks dan kontroversial . Peningkatan harga BBM memengaruhi berbagai aspek ekonomi dan sosial, termasuk inflasi, biaya produksi, dan tarif transportasi di Indonesia. Klasifikasi sentimen menggunakan algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan metode algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan algoritma klasifikasi sentimen menghasilkan akurasi yang paling tinggi didapatkan oleh algoritma Naive Bayes dengan akurasi sebesar 80,28%. Kedua adalah algoritma Support Vector Machine (SVM) dengan akurasi sebesar 73,89%. Algoritma yang memiliki nilai akurasi paling kecil adalah algorima K-Nearest Neighbor (KNN) dengan akurasi sebesar 50,00%.
أبعاد التنمية المستدامة كمقاربة لتحقيق مقاصد الشريعة وفق الاقتصاد الإسلامي: قراءة في المنهج والآليات
Authors: Salwa Hamed Almaala  , Mohamed Almehdi Shanin  
Published: February 2024

ملخص البحث
تهدف هذه الدراسة إلى استكشاف مفهوم التنمية المستدامة في الاقتصاد الإسلامي وتحليلها من منظور المقاصد الشرعية. تعتمد الدراسة على المنهج النوعي والطرق البحث المكتبي لتحليل المفاهيم والأفكار المتعلقة بالمقاصد الشرعية والتنمية المستدامة. تشمل المقاصد الشرعية الخمسة جوانب الحياة الإنسانية بأبعادها المختلفة، وتركز على تحقيق المصلحة ومنع المفسدة. يعتبر الاقتصاد الإسلامي جزءًا من التنمية المستدامة، حيث ينبغي للإنسان المحافظة على الأرض وتنمية بيئتها واقتصادها ومستوى المعيشة بأبعادها الروحية والأخلاقية والحضارية. تحتاج الدول إلى تبني رؤى وطنية لتحقيق أبعاد الاستدامة من خلال مؤسسات الحكومة والقطاع الخاص والمجتمع المدني. يجب أيضًا أن تلتزم المجتمعات الإسلامية بحماية البيئة كجزء من البناء الثقافي الإنساني. بالتالي، يسعى البحث إلى استكشاف كيفية تحقيق مقاصد الشريعة الخمسة من خلال التنمية المستدامة وحماية البيئة والموارد الطبيعية، وضمان العدالة الاقتصادية والرشادة بواسطة الحوكمة. تعزز الدراسة أيضًا نظرة الاقتصاد الإسلامي في تناول أبعاد التنمية المستدامة وتحقيقها.
The Appreciative Intelligence Framework of Islamic Microfinance Instrument for Quantity Rationing to Increase Participation of People at the Bottom of the Economic Pyramid
Authors: Wulandari Permata 
Published: February 2024

This study solicited Islamic microfinance practitioners for a framework of appreciative intelligence for Islamic microfinance instruments in order to surmount obstacles in the practice of quantity rationing assessment. Based on methods of purposive sampling, 26 Islamic Microfinance Institutions (MFIs) in Indonesia were selected. One instrument was created to capture data, namely an in-depth interview to characterize the data associated with the proposed solution. The findings of this study provide several solutions for quantity rationing issues in order to increase the impact of BMT on welfare-improving objectives. The majority of respondents agreed that a write-off and the role of zakah, infaq, and shadaqah are the most desirable solutions in the event of financing default. In the event of financing default, rescheduling the financing and sending a card for an ATM to BMT are additional options. Lastly, some BMT practitioners propose using savings as collateral and expanding the role of zakah, infaq, and shadaqah in BMT to address financing default. The majority of BMT borrowers concurred that a special margin on time deposits made 2-3 months prior to the peak season would address the problem of lack of liquidity during the peak season. In addition, a gift of savings is an alternative solution. The majority of BMT practitioners choose a financing donor as the most desirable solution and continue to use channeling and other BMT as solutions for insufficient own capital sources. BMT must provide funding on a low nominal basis in order to ensure that impoverished people are treated equitably. Standardization of borrowing limits must also be implemented.
STUDYING HISTORICAL FIGURES IN UKRAINIAN HISTORY LESSONS AS A MEANS OF FORMING STUDENTS' CREATIVE THINKING
Authors: Molotkina Valentyna 
Published: February 2024

The article analyzes the methods and techniques of organizing the study of a historical figure in the history lessons of Ukraine, which are most often used in the practical work of teachers, and finds out their possibilities as a means of forming students' creativity.The main methodological techniques used by the teacher when working with a historical portrait in the process of forming the competence of students' research and cognitive activity are considered.It has been found that in the formation of students' creativity of thinking in the history of Ukraine lesson, an important place is occupied by educational activities aimed at forming cause-and-effect relationships and research competencies.Modern methods of teaching history offer various options for organizing students' work on studying a historical figure in Ukrainian history lessons. In particular, an important role in shaping students' creativity in forming an idea of a historical figure is played by the use of the artistic and journalistic variant. The teacher's organization of cognitive activities based on an illustrative portrait should also be aimed at developing students' creativity.It has been established that the consideration of a historical figure should be carried out in its dynamics, development, which will allow to convey the evolution of a person's views throughout life. The teacher should not allow one-sided assessments. This danger can be avoided by presenting the material alternatively, using different sources, showing the figure from different points of view, paying attention to the activities of diverse historical figures, not just positive ones. This will form the skills of independent critical evaluation of a person (and not only historical). The student should learn to consider a historical figure and his or her actions in relation to the realities of specific events of the time when he or she lived, and not from the standpoint of the present.Learning about the life and work of a historical figure in all its contradictions and complexity in the context of social processes will foster creative thinking and interest in history as a subject. A thoughtful study of the personality of a prominent figure will contribute to both a deeper knowledge of history and a positive pedagogical impact on the formation of a student's personality.At the same time, analyzing a particular personality can be the key to understanding the events in which they participated. When studying the life and work of a historical figure, you should avoid a dry list of the main dates and facts of their biography. Instead, it is necessary to create vivid, imaginative portraits that will be remembered by students and will influence the formation of their personality and values.
The digitalisation of the music industry in Brazil
Authors: De Marchi Leonardo 
Published: February 2024

Abstract
The article analyses the digitisation of the music industry in Brazil, taking into consideration the new productive structure that emerges from its ‘creative destruction’. To do so, it presents a historical overview of the record industry in the country and assesses the key components of the structural transformation of its economy in the early twenty-first century, from the sharp decrease in the consumption of CDs and DVDs to the entry of the global streaming services that presented an international modus operandi to the local music industry, or what has come to be known as the celestial jukebox. In the conclusion, it discusses the challenges for the distribution and circulation of music produced in Brazil in the global digital music market.
Ovarian Cancer Detection Based on Elman Recurrent Neural Network
Authors: Farhan Hameed R. , Kod Muayad S. , Taqi Arwa , Ghazi Suleiman A. 
Published: February 2024

The early detection of cancers increases the possibility of health recovery and prevents the disease from becoming a silent killer. This study introduces an effective method for identifying ovarian cancer (OC) using Elman Recurrent Neural Network (ERNN), which can recognize cancer via mass spectrometry data. The network has a topology of 100 input neurons for receiving data, five neurons for hidden and context layers, and two output nodes to indicate the status. The proposed method uses reduced-size features, including ion concentration levels at specific mass/charge values, which are trained using various learning algorithms to determine the suitable one that achieves the best results. The experimental results show that all the training algorithms achieve about 100% performance rate, with the Levenberg Marquardt (LM) being the most accurate and fastest algorithm, which converges after six epochs and achieves 0.0035, 0.0045 and 0.0045 mean square errors for training, validation, and test performances, respectively. Based on comparative results, the proposed LM-ERNN method outperforms other OC detection methods and holds promise for detecting other types of cancer.
Molecular epidemiology of multidrug-resistant Klebsiella pneumoniae, Enterobacter cloacae, and Escherichia coli outbreak among neonates in Tembisa hospital, South Africa
Authors: Osei Sekyere John , Mmatli Masego , Bosch Anel , Ntsoane Ramathetje Virginia , Naidoo Harishia , Doyisa Sinenhlanhla , Maningi Nontuthuko E. , Mbelle Nontombi Marylucy , Said Mohamed 
Published: February 2024

BackgroundAn outbreak of multidrug-resistant Klebsiella pneumoniae, Escherichia coli, and Enterobacter cloacae infections in a neonatal ward within a tertiary hospital in South Africa resulted in the mortality of 10 patients within six months. In this work, the genomic epidemiology of and the molecular factors mediating this outbreak were investigated.MethodsBacterial cultures obtained from clinical samples collected from the infected neonates underwent phenotypic and molecular analyses to determine their species, sensitivity to antibiotics, production of carbapenemases, complete resistance genes profile, clonality, epidemiology, and evolutionary relationships. Mobile genetic elements flanking the resistance genes and facilitating their spread were also characterized.ResultsThe outbreak was centered in two major wards and affected mainly neonates between September 2019 and March 2020. Most isolates (n = 27 isolates) were K. pneumoniae while both E. coli and E. cloacae had three isolates each. Notably, 33/34 isolates were multidrug resistant (MDR), with 30 being resistant to at least four drug classes. All the isolates were carbapenemase-positive, but four blaOXA-48 isolates were susceptible to carbapenems. BlaNDM-1 (n = 13) and blaOXA-48/181 (n = 15) were respectively found on IS91 and IS6-like IS26 composite transposons in the isolates alongside several other resistance genes. The repertoire of resistance and virulence genes, insertion sequences, and plasmid replicon types in the strains explains their virulence, resistance, and quick dissemination among the neonates.ConclusionsThe outbreak of fatal MDR infections in the neonatal wards were mediated by clonal (vertical) and horizontal (plasmid-mediated) spread of resistant and virulent strains (and genes) that have been also circulating locally and globally.
Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm
Authors: Huang Legang , Lu Jiankuan , Xiao Ying , Zhang Xiaofei , Li Cong , Yang Guangchao , Jiao Xiangfei , Wang Zijie 
Published: February 2024

ObjectiveThis study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction.MethodsA comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes.ResultsDeep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy.ConclusionDeep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management.
Dynamic surveillance of lymphocyte subsets in patients with non-small cell lung cancer during chemotherapy or combination immunotherapy for early prediction of efficacy
Authors: Zhen Shanshan , Wang Wenqian , Qin Guohui , Lu Taiying , Yang Li , Zhang Yi 
Published: February 2024

BackgroundNon-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths worldwide. Lymphocytes are the primary executors of the immune system and play essential roles in tumorigenesis and development. We investigated the dynamic changes in peripheral blood lymphocyte subsets to predict the efficacy of chemotherapy or combination immunotherapy in NSCLC.MethodsThis retrospective study collected data from 81 patients with NSCLC who received treatments at the First Affiliated Hospital of Zhengzhou University from May 2021 to May 2023. Patients were divided into response and non-response groups, chemotherapy and combination immunotherapy groups, and first-line and multiline groups. We analyzed the absolute counts of each lymphocyte subset in the peripheral blood at baseline and after each treatment cycle. Within-group and between-group differences were analyzed using paired Wilcoxon signed-rank and Mann-Whitney U tests, respectively. The ability of lymphocyte subsets to predict treatment efficacy was analyzed using receiver operating characteristic curve and logistic regression.ResultsThe absolute counts of lymphocyte subsets in the response group significantly increased after the first cycle of chemotherapy or combination immunotherapy, whereas those in the non-response group showed persistent decreases. Ratios of lymphocyte subsets after the first treatment cycle to those at baseline were able to predict treatment efficacy early. Combination immunotherapy could increase lymphocyte counts compared to chemotherapy alone. In addition, patients with NSCLC receiving chemotherapy or combination immunotherapy for the first time mainly presented with elevated lymphocyte levels, whereas multiline patients showed continuous reductions.ConclusionDynamic surveillance of lymphocyte subsets could reflect a more actual immune status and predict efficacy early. Combination immunotherapy protected lymphocyte levels from rapid decrease and patients undergoing multiline treatments were more prone to lymphopenia than those receiving first-line treatment. This study provides a reference for the early prediction of the efficacy of clinical tumor treatment for timely combination of immunotherapy or the improvement of immune status.
Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses
Authors: Lin Zhiheng , Li Xinhan , Shi Hengmei , Cao Renshuang , Zhu Lijun , Dang Chunxiao , Sheng Yawen , Fan Weisen , Yang Zhenghui , Wu Siyu 
Published: February 2024

BackgroundCervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated.MethodsWe utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes.ResultsThrough our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator—the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines.ConclusionThis study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.