bluuebunny/crossref_metadata_2025 huggingface dataset:
Wound healing – characteristics of the ideal dressing
Authors: Orlińska Kinga , Komosińska-Vassev Katarzyna , Olczyk Krystyna , Glaesel Marcin , Olczyk Paweł
Published: November 2023

Gojenie się ran to proces dynamiczny, którego celem jest przywrócenie homeostazy oraz funkcjonalności uszkodzonej tkanki. To wysoce złożony, wieloetapowy proces, którego zaburzenie prowadzi do powikłań i problemów zdrowotnych osoby poszkodowanej. Omawiany proces przebiega w ustroju ludzkim dwiema drogami. Pierwszą z nich jest ziarninowanie, drugą rychłozrost. Niezależnie od sposobu gojenia się rany, poszczególne fazy tego procesu wzajemnie nachodzą na siebie, gdzie przed zakończeniem fazy poprzedniej rozpoczyna się następna. Rozgraniczenie poszczególnych faz ma charakter czysto praktyczny. Wyróżnia się cztery fazy gojenia: fazę hemostazy, fazę zapalenia, fazę proliferacyjną – inaczej fazę replikacji i syntezy – oraz fazę remodelingu.
Proces gojenia się ran jest procesem naturalnie zachodzącym w organizmie, długotrwałym i złożonym, który zachodzi w przypadku zranienia. Nieprawidłowy przebieg gojenia może skutkować wystąpieniem ran przewlekłych, martwic czy nadmiernego bliznowacenia. Wspomaganiem tego naturalnie zachodzącego w organizmie procesu jest leczenie ran. W przypadkach wymagających takiego wsparcia stosuje się opatrunki, które są elementem niezbędnym, mającym zastosowanie w ochronie zdrowia. Idealny opatrunek powinien: tworzyć barierę przed czynnikami zewnętrznymi, utrzymywać odpowiednie środowisko w łożysku rany (odpowiednia temperatura, wysoka wilgotność, lekko kwaśne pH, umożliwienie wymiany gazowej), absorbować nadmiar krwi oraz wysięku, utrzymywać ranę w czystości – oczyszczać z tkanki martwiczej i toksyn – nie przywierać do rany, aby uniknąć jej uszkodzenia w trakcie wymiany opatrunku, nie wykazywać działania uczulającego, a także drażniącego.
Twentieth International Conference on New Directions in the Humanities. Conference Proceedings
Authors: Morales-Cabezas Jerónimo
Published: November 2023

Proceedings of the Seventeenth International Conference on Interdisciplinary Social Sciences, hosted by the National and Kapodistrian University of Athens, Greece, 21-23 July 2022. The conference featured research addressing the following special focus: “At the Crossroads of Paradigms: Considering Heterodoxy in the Social Sciences” and annual themes: • Theme 1: Social and Community Studies. • Theme 2: Civic and Political Studies. • Theme 3: Cultural Studies. • Theme 4: Global Studies. • Theme 5: Environmental Studies • Theme 6: Organizational Studies. • Theme 7: Educational Studies. • Theme 8: Communication.
Twelfth International Conference on Food Studies. Conference Proceedings
Authors: Aguilar Uscanga Blanca Rosa
Published: November 2023

Proceedings of the Twelfth International Conference on Food Studies, hosted by the Marymount Manhattan College, New York, USA, 23-24 October 2022. The conference featured research addressing the following special focus: “Imagining the Edible: Food, Creativity, and the Arts” and annual themes: • Theme 1: Food Production and Sustainability. • Theme 2: Food, Nutrition, and Health. • Theme 3: Food Politics, Policies, and Cultures.
Impacto del trabajo híbrido en la satisfacción laboral por generaciones de colaboradores del sector consumo masivo en Lima Metropolitana, 2022
Authors:   , Espilco Palomino Nallely Stephania , Villacorta Caceres Sandra
Published: November 2023

La finalidad de la presente investigación es determinar el impacto del trabajo híbrido en la satisfacción laboral por generaciones de colaboradores del sector consumo masivo en Lima Metropolitana en el año 2022. Para ello, se realizó una investigación tomando como población tres de las empresas más representativas dentro de Lima Metropolitana y se determinó una muestra de 291 trabajadores que laboraban bajo la modalidad híbrida en empresas de este rubro. Como objetivo específico, se analizó el impacto del trabajo híbrido en la satisfacción laboral, estudiando el desglose de ambas variables. Primero, la variable trabajo híbrido, la cual se encontraba compuesta por: (i) Flexibilidad y (ii) autonomía. Y segundo, la variable satisfacción laboral, que se dividía las siguientes dimensiones: (i) Motivación, (ii) compromiso organizacional y (iii) equilibrio entre vida personal y laboral; segmentando el análisis por generaciones, siendo estas: (i) Generación Z, 18 a 28 años; (ii) Generación Y, 29 a 41 años; (iii) X, 42 a 53 años y (iv) Boom, 54 a 73 años. A su vez, se indica que la presente investigación es cuantitativa de tipo probabilístico con un muestreo estratificado. Asimismo, se realizó un cuestionario con escala de Likert, el cual se encuentra conformado por 26 ítems que analizaron las variables en cuestión. Finalmente, los resultados evidencian que la hipótesis general fue aceptada, ya que la muestra encuestada presentó una correlación inversa negativa débil, pero significativa. Es decir, mientras el colaborador pertenezca a una generación etaria menor, mayor satisfacción laboral presentará.
Centro Especializado de Alto Rendimiento de Voleibol y Voleibol Sentado en San Borja
Authors:   , Salcedo Peña Rodrigo
Published: November 2023

La investigación de este proyecto surge por el interés por el voleibol y por deporte de alto nivel, así como por las ganas de emplear la arquitectura como una herramienta para mejorar las condiciones de la infraestructura deportiva en el país. La principal función del CEAR es poder ofrecerles a los voleibolistas y paravoeiboistas de alto nivel en el Perú todas las facilidades que les permitan llegar a su mayor nivel tanto físico como mental con miras a competencias deportivas internacionales. El nuevo Centro debe considerar espacios principalmente destinados a: entrenamientos, residencia, atención médica, recreación y competencia. Es por esta razón que en el presente documento se ha desarrollado un marco teórico, conceptual y el estudio de proyectos referenciales de la tipología y del énfasis. Se analizó la realidad actual del voleibol y del deporte en país, así como la de los usuarios que se beneficiarán con el proyecto (deportistas de alto nivel) con la finalidad de reconocer sus características y necesidades que se verán plasmadas en el programa arquitectónico; así también como las características del entorno y del terreno propuesto para el proyecto.
Diversity and distribution of Pteridophytes in Biligiri Rangaswamy Temple (BRT) Tiger Reserve, Karnataka
Authors: C. Bagathsingh , A. Benniamin
Published: November 2023

An updated list of the pteridophytes known to occur in the Biligiri Rangaswamy Temple (BRT) Tiger Reserve has been presented. It comprises two classes, 24 families, 49 genera, and 102 species, including 11 subspecies. Four families were identified as monotypic. Among the 102 species collected, six were endemic to south India, 10 were confined to south India and Sri Lanka, and eight were in the threatened category. We make available baseline biodiversity information on the pteridophytes of the study area and provide details on taxonomic diversity and distribution along the elevation gradient. The relationship between elevation and species richness in the study area showed a unimodal pattern. Color photographs of species from each family are also provided. The presence of many threatened and endemic species in the area implies that a high priority should be given to protecting and conserving these species of pteridophytes in the BRT Tiger Reserve.
Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs
Authors: Vermani Kunal , Noliya Amandeep , Kumar Sunil , Dutta Kamlesh
Published: November 2023

Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks.
Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification.
Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies.
Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers.
Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble.
Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,
Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism
Authors: Zamani Nur Azmina Mohamad , Kamaruddin Norhaslinda
Published: November 2023

Background: Due to the increased interest in cryptocurrencies, opinions on cryptocurrency-related topics are shared on news and social media. The enormous amount of sentiment data that is frequently released makes data processing and analytics on such important issues more challenging. In addition, the present sentiment models in the cryptocurrency domain are primarily focused on English with minimal work on Malay language, further complicating problems.
Objective: The performance of the sentiment regression model to forecast sentiment scores for Malay news and tweets is examined in this study.
Methods: Malay news headlines and tweets on Bitcoin and Ethereum are used as the input. A hybrid Generalized Autoregressive Pretraining for Language Understanding (XLNet) language model in combination with Bidirectional-Gated Recurrent Unit (Bi-GRU) deep learning model is applied in the proposed sentiment regression implementation. The effectiveness of the proposed sentiment regression model is also investigated using the multi-head self-attention mechanism. Then, a comparison analysis using Bidirectional Encoder Representations from Transformers (BERT) is carried out.
Results: The experimental results demonstrate that the number of attention heads is vital in improving the XLNet-GRU sentiment model performance. There are slight improvements of 0.03 in the adjusted R2 values with an average MAE of 0.163 (Malay news) and 0.174 (Malay tweets). In addition, an average RMSE of 0.267 and 0.255 were obtained respectively for Malay news and tweets, which show that the proposed XLNet-GRU sentiment model outperforms the BERT sentiment model with lesser prediction errors.
Conclusion: The proposed model contributes to predicting sentiment on cryptocurrency. Moreover, this study also introduced two carefully curated Malay corpora, CryptoSentiNews-Malay and CryptoSentiTweets-Malay, which are extracted from news and tweets, respectively. Further works to enhance Malay news and tweets corpora on cryptocurrency-related issues will be expended with implementing the proposed XLNet Bi-GRU deep learning model for greater financial insight.
Keywords: Cryptocurrency, Deep learning model, Malay text, Sentiment analysis, Sentiment regression model
Towards Smart and Green Features of Cloud Computing in Healthcare Services: A Systematic Literature Review
Authors: Arega Aschalew , Sharma Durga Prasad
Published: November 2023

Background: The healthcare sector has been facing multilateral challenges regarding the quality of services and access to healthcare innovations. As the population grows, the sector requires faster and more reliable services, but the opposite is true in developing countries. As a robust technology, cloud computing has numerous features and benefits that are still to be explored. The intervention of the latest technologies in healthcare is crucial to shifting toward next-generation healthcare systems. In developing countries like Ethiopia, cloud features are still far from being systematically explored to design smart and green healthcare services.
Objective: To excavate contextualized research gaps in the existing studies towards smart and green features of cloud computing in healthcare information services.
Methods: We conducted a systematic review of research publications indexed in Scopus, Web of Science, IEEE Xplore, PubMed, and ProQuest. 52 research articles were screened based on significant selection criteria and systematically reviewed. Extensive efforts have been made to rigorously review recent, contemporary, and relevant research articles.
Results: This study presented a summary of parameters, proposed solutions from the reviewed articles, and identified research gaps. These identified research gaps are related to security and privacy concerns, data repository standardization, data shareability, self-health data access control, service collaboration, energy efficiency/greenness, consolidation of health data repositories, carbon footprint, and performance evaluation.
Conclusion: The paper consolidated research gaps from multiple research investigations into a single paper, allowing researchers to develop innovative solutions for improving healthcare services. Based on a rigorous analysis of the literature, the existing systems overlooked green computing features and were highly vulnerable to security violations. Several studies reveal that security and privacy threats have been seriously hampering the exponential growth of cloud computing. 54 percent of the reviewed articles focused on security and privacy concerns.
Keywords: Cloud computing, Consolidation, Green computing, Green features, Healthcare services, Systematic literature review.
Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification
Authors: Ahmed Zishan , Shanto Shakib Sadat , Jony Akinul Islam
Published: November 2023

Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly.
Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks.
Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them.
Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification.
Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques.
Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis
The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors
Authors: Wiryadinata Doni , Sugiharto Aris , Tarno Tarno
Published: November 2023

Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection.
Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool.
Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report.
Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered.
Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction.
Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.
Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy
Authors: Baharuddin Fikri , Naufal Mohammad Farid
Published: November 2023

Background: The learning assessment of elementary schools has recently incorporated Bloom's Taxonomy, a structure in education that categorizes different levels of cognitive learning and thinking skills, as a fundamental framework. This assessment now includes High Order Thinking Skill (HOTS) questions, with a specific focus on Indonesian topics. The implementation of this system has been observed to require teachers to manually categorize or classify questions, and this process typically requires more time and resources. To address the associated difficulty, automated categorization and classification are required to streamline the process. However, despite various research efforts in questions classification, there is still room for improvement in terms of performance, particularly in precision and accuracy. Numerous investigations have explored the use of Deep Learning Natural Language Processing models such as BERT for classification, and IndoBERT is one such pre-trained model for text analysis. 
Objective: This research aims to build classification system that is capable of classifying Indonesian exam questions in multiple-choice form based on Bloom's Taxonomy using IndoBERT pre-trained model.
Methods: The methodology used includes hyperparameter fine-tuning, which was carried out to identify the optimal model performance. This performance was subsequently evaluated based on accuracy, F1 Score, Precision, Recall, and the time required for the training and validation of the model.
Results: The proposed Fine Tuned IndoBERT Model showed that the accuracy rate was 97%, 97% F1 Score, 97% Recall, and 98% Precision with an average training time per epoch of 1.55 seconds and an average validation time per epoch of 0.38 seconds.
Conclusion: Fine Tuned IndoBERT model was observed to have a relatively high classification performance, and based on this observation, the system was considered capable of classifying Indonesian exam questions at the elementary school level.
Keywords: IndoBERT, Fine Tuning, Indonesian Exam Question, Model Classifier, Natural Language Processing, Bloom’s Taxonomy
A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images
Authors: Bin Zahir Md. Jawwad , Azim Muhammad Anwarul , Chy Abu Nowshed , Islam Mohammad Khairul
Published: November 2023

Background: COVID-19 is a highly contagious respiratory disease with multiple mutant variants, an asymptotic nature in patients, and with potential to stay undetected in common tests, which makes it deadlier, more transmissible, and harder to detect. Regardless of variants, the COVID-19 infection shows several observable anomalies in the computed tomography (CT) scans of the lungs, even in the early stages of infection. A quick and reliable way of detecting COVID-19 is essential to manage the growing transmission of COVID-19 and save lives.
Objective: This study focuses on developing a deep learning model that can be used as an auxiliary decision system to detect COVID-19 from chest CT-scan images quickly and effectively.
Methods: In this research, we propose a MobileNet-based transfer learning model to detect COVID-19 in CT-scan images. To test the performance of our proposed model, we collect three publicly available COVID-19 CT-scan datasets and prepare another dataset by combining the collected datasets. We also implement a mobile application using the model trained on the combined dataset, which can be used as an auxiliary decision system for COVID-19 screening in real life.
Results: Our proposed model achieves a promising accuracy of 96.14% on the combined dataset and accuracy of 98.75%, 98.54%, and 97.84% respectively in detecting COVID-19 samples on the collected datasets. It also outperforms other transfer learning models while having lower memory consumption, ensuring the best performance in both normal and low-powered, resource-constrained devices.
Conclusion: We believe, the promising performance of our proposed method will facilitate its use as an auxiliary decision system to detect COVID-19 patients quickly and reliably. This will allow authorities to take immediate measures to limit COVID-19 transmission to prevent further casualties as well as accelerate the screening for COVID-19 while reducing the workload of medical personnel.
Keywords: Auxiliary Decision System, COVID-19, CT Scan, Deep Learning, MobileNet, Transfer Learning
AN OVERVIEW OF FAMILY COPING MECHANISM IN CARING FOR PATIENTS WITH MENTAL DISORDERS IN THE AREA OF UPT PUSKESMAS TIKUNG LAMONGAN
Authors: Wijayanti Endah Sri , Safa Ayu Shinta Banafsa , Iswatun Iswatun
Published: November 2023

Introduction: Family coping mechanism is a family response in dealing with a problem. Each family has different coping mechanisms according to the family's ability to solve problems. Problems with mental disorders cause crises and pressure for caring families, family members who experience mental disorders so that they can affect family coping mechanisms. This study aims to describe the mechanism of family coping in caring for patients with mental disorders in the UPT Puskesmas Tikung Lamongan area. Methods: The research method uses descriptive with a survey time approach. The population in this study were all families who had family members with mental disorders who were recorded in the medical records of UPT Puskesmas Tikung as many as 84 people. The sample technique used purposive sampling, based on inclusion and exclusion criteria, a sample of 55 respondents was obtained. The instrument in this study used the Coping Health Inventory for Parent (CHIP) questionnaire. Results: The results showed that most families had adaptive coping mechanisms, namely 32 respondents (58.2%) and almost half had maladaptive coping mechanisms, namely 23 respondents (41.8%). Conclusions: This study illustrates that families with mental disorders in the UPT Puskesmas Tikung Lamongan area use good coping in dealing with mental disorders. Families are expected to continue to use adaptive coping in providing support and motivation to family members who experience mental disorders so that they are always routinely taking medication.
DESCRIPTION OF EATING PATTERN IN PATIENTS WITH DIABETES MELLITUS AT IBNU SINA GRESIK HOSPITAL
Authors: Syifaul Fikri Muhammad , Harianto Susilo , Okviasanti Fanni
Published: November 2023

Introduction: Poor dietary management is still commonly found in diabetes mellitus patients. Diet can be observed from the amount of food, type of food, and food schedule. The purpose of this research is to find out the diet of people with diabetes mellitus at the Ibnu Sina Gresik Hospital. Methods: This study was used a descriptive research design. The sample population in this study study were outpatient diabetes mellitus at Internal Medicine Polyclinic Ibnu Sina Gresik Hospital with the number 96 respondents. Sampling was taken using consecutive sampling who met the inclusion and exclusion criteria. Researchers collected data using dietary questionnaires. Univariate data analysis used is editing, coding, scoring, tabulation, and descriptive analysis. Results: This study was used a descriptive research design. The sample population in this study study were outpatient diabetes mellitus at Internal Medicine Polyclinic Ibnu Sina Gresik Hospital with the number 96 respondents. Sampling was taken using consecutive sampling who met the inclusion and exclusion criteria. Researchers collected data using dietary questionnaires. Univariate data analysis used is editing, coding, scoring, tabulation, and descriptive analysis. Conclusions: The diet off patients diabetes mellitus based on the amount of food is categorized as sufficient, based on the type of food categorized as bad, and based on the eating schedule is categorized as sufficient.
THE RELATIONSHIP BETWEEN PARENT PARENTING PATTERNS AND LEARNING ACHIEVEMENT IN V-GRADE SCHOOL CHILDREN AT SDN KERONCONG MAS PERMAI
Authors: Nurdiyanti Devia , Safitri Alfika , Puspita Sari Rina
Published: November 2023

Introduction: Parenting is a comprehensive interaction between parents and children. School age in Indonesia is between 7 and 12 years old. This study aims to determine the relationship between parenting parents and learning achievement in fifth grade school children at SD Keroncong Mas Permai. Methods: This research is quantitative with a cross sectional study design with a sample of 104 students, The sampling used was Proportional Stratified Random Sampling. Data collection used a questionnaire on parenting patterns and the results of fifth grade students' even semester report cards. The analysis used is bivariate Pearson correlation with a significance level of a<0.05. Results: This research shows that some parents apply authoritarian parenting with a percentage of 49.0% and permissive have no percentage, while most parents apply democratic parenting with a percentage of 51.0%, so parents have an adequate role. major in educating children with the achievement of student achievement in school. The results of the Pearson correlation test with a significance level of a < 0.05 obtained a = 0.000, where a < 0.05 means there is a significant relationship between parenting patterns and learning achievement in class V school children at SDN Keroncong Mas Permai. Conclusions: Based on the results of the analysis above, it was found that there was a relationship between parenting parents and learning achievement in fifth grade school children at SDN Keroncong Mas Permai.
THE RELATIONSHIP BETWEEN ROLE OF THE FAMILY WITH LEVEL OF DEPRESSION IN ELDERLY
Authors: Sairozi Ali , Tin Lutfiandini Cherlys , Wijayanti Endah sri , Iswatun Iswatun
Published: November 2023

Introduction: Depression is a Introduction: Depression is an emotional disorder in the form of feeling depressed, unhappy, worthless and lack of enthusiasm in life. Elderly people are vulnerable to depression because of their low health status, loss of a partner due to death, and the low level of social support they receive. The family has an important role for the elderly in developing, preventing and overcoming health problems. This study aims to determine the relationship between the role of the family and the level of depression in the elderly in Sungegeneng Village, Sekaran District, Lamongan Regency. Methods: the research used a cross-sectional design, the samples in this study were elderly people and families who lived at home with elderly people in Sungegeneng Village, Sekaran District, Lamongan Regency who met the inclusion criteria with a sample size of 39 people. Spearman rho correlation testing was carried out using SPSS. Results: The results of the Spearman test and data analysis using the SPSS 11.5 for Windows program between the role of the family and the level of depression in the elderly in Sungegeneng Village, Sekaran District, Lamongan Regency, obtained a result of ρ = 0.000 with a significance level of α = 0.01, so H0 is rejected, meaning there is a significant relationship Between the role of the family and the level of depression in the elderly. Conclusions: There is a relationship between the role of the family and the level of depression in the elderly in Sungegeneng Village, Sekaran District, Lamongan Regency.
DESCRIPTION OF NURSING INTERNSHIP STUDENT'S ANXIETY DURING THE PANDEMIC ERA
Authors: Mardhika Amellia , Dewi Anggraeni Selvyana , Pangestu Mei Tyas Anestasia , Fadliyah Lailatul , Sulpat Emuliana
Published: November 2023

Introduction: COVID-19 changed students' educational activities, especially during internships taking into account the condition of the number exposed to COVID-19. A preliminary study conducted on 11 students showed that they all felt anxious about undertaking an internship during the pandemic. This can negatively impact quality of life, learning, academic performance, and clinical practice performance. The aim of this study is to find out the description of students' anxiety during internships. Methods: This research is a quantitative observational study with a descriptive research design—a sample of 148 students with a purposive sampling method. The variable in this study was detained students who were undergoing internships during the start of the COVID-19 pandemic. The instrument used in this study was the Hamilton Anxiety Rating for Scale (HAM-A), which had been tested for validity and reliability to see the level of student anxiety during the internship. The results of the questionnaire were then analyzed through descriptive/univariate analysis. Results: Almost all respondents had a mild anxiety level, 120 respondents (81.08%). Conclusions: A suitable coping mechanism for students during internships, is a strategy to deal with anxiety. Clarifying information, consistently implementing health protocols correctly, maintaining health by exercising regularly, eating healthy food, and communicating well with family and friends must still be carried out by students so that the support system remains stable
THE RELATIONSHIP BETWEEN NURSES’ CHARACTERISTICS AND CONTINUING PROFESSIONAL DEVELOPMENT
Authors: Dhian Sulistyowati Arlina , Nurhidayati Istianna , Sudarsono Nur Isnaini
Published: November 2023

Introduction: Continuing Professional Development (CPD) is a professional development activity intended to maintain and improve nurses’ knowledge, skills, and competencies so that they are able to update and develop their skill set. One of factors which influence the continuing professional development is the individual characteristics of nurses including age, gender, length of service, and employment status. The current study aims to determine the relationship between nurses’ characteristics and the continuing professional development of executive nurses. Methods: This study employed a Quantitative Correlation with a Cross-sectional design. The sample consisted of 157 nurses selected using a cluster random sampling technique. Meanwhile, the data analysis was performed utilizing the Chi-Square test. Results: The findings revealed that 65% of nurses successfully met their continuing professional development. The Chi-Square test result indicate that no relationship was found between age, gender, length of service, and employment status with the continuing professional development of executive nurses. Meanwhile, a relationship was found between education and continuing professional development of executive nurses. Conclusions: According to the nurses’ characteristics, only their education demonstrated a correlation with the continuing professional development of executive nurse.
Effectiveness of Thromboprophylaxis Agents Following Hip Fracture
Authors: Negm Ahmed M. , Yamaura Lisa , Clarke Ashley , Chaudhry Daniyya , Cherppukaran Tanya , Schneider Prism
Published: November 2023

Background:

Multiple studies have compared different pharmacologic thromboprophylaxis agents after hip fracture surgery, including aspirin, unfractionated heparin (UFH), low-molecular-weight heparin (LMWH), direct oral anticoagulants (DOAC), and warfarin, resulting in variability in clinical practice. To guide clinical management, a systematic review and network meta-analysis (NMA), which enables the simultaneous assessment of the effects of multiple interventions for the same patient population, was performed. This study aimed to determine the comparative effectiveness of thromboprophylaxis in reducing venous thromboembolism (VTE) in patients with surgically treated hip fractures.

Methods:

The primary outcome was the effect of the treatment on the VTE rate, and the secondary outcome was the treatment effect on the bleeding rate. Relevant studies were identified by a systematic search of Embase, MEDLINE, and the Cochrane Central Register of Controlled Trials (CENTRAL) from January 2000 to February 2022. Title, abstract, and full-text screening; data extraction; and risk-of-bias assessment were performed. All studies examining thromboprophylaxis interventions (DOAC, LMWH, UFH, aspirin, and warfarin) in patients with a surgically treated hip fracture were included. Bayesian NMA was performed, and dichotomous outcome data were pooled using the odds ratio. Interventions were ranked using the surface under the cumulative ranking curve (SUCRA) for each outcome.

Results:

A total of 19 studies were included after the screening of 466 citations and 77 full-text articles. Of the included studies, 15 studies had a high overall risk of bias. The NMA of the VTE outcome included 19 studies, 49,409 participants, and 6 thromboprophylaxis interventions. The NMA of the bleeding outcome included 3 studies, 18,163 participants, and 3 interventions. The mean age ranged from 43.5 to 86.2 years among the included studies. No thromboprophylaxis intervention was statistically different from any other intervention in its effect on the VTE or bleeding rate in hip fracture patients.

Conclusions:

This NMA demonstrated that there was no difference between the thromboprophylaxis interventions in reducing VTE or bleeding rates in hip fracture patients. More robust randomized controlled trials are needed to determine the most effective thromboprophylaxis interventions for patients with hip fractures.

Level of Evidence:

Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.
PEMETAAN DAN ANALISIS KEBISINGAN LALU LINTAS BERDASARKAN JUMLAH KENDARAAN DI PERSIMPANGAN TABEK GADANG, KOTA PEKANBARU
Authors: Sasmita Aryo , Reza Muhammad , Akmal Wahyu
Published: November 2023

Tingginya mobilitas penduduk menyebabkan keramaian lalu lintas yang berdampak berdampak ke lingkungan di sepanjang jalan yang dilewati kendaraan. Salah satunya adalah peningkatan intensitas kebisingan lalu lintas. Tujuan penelitian ini adalah untuk mengukur intensitas kebisingan, memetakan intensitas kebisingan, dan menganalisis pengaruh antara volume lalu lintas terhadap kebisingan transportasi pada Simpang Tabek Gadang Kota Pekanbaru. Pengumpulan data berupa tingkat kebisingan dan volume kendaraan. Pengambilan nilai kebisingan menggunakan Sound Level Meter (SLM) selama 2 hari, yaitu pada hari senin pukul 5 hingga 6 sore yang mewakili jam puncak dan hari Minggu pada hari Minggu pukul 6 hingga 7 pagi yang mewakili jam sepi kendaraan. Hasil penelitian menunjukkan tingkat kebisingan pada jam puncak didapatkan sebesar 78,17 dBA – 94,60 dBA. Sedangkan pengukuran kebisingan pada jam sepi kendaraan yaitu sebesar 76,53 dBA – 86,67 dBA. Pola penyebaran kebisingan pada area kebisingan tertinggi yang ditandai dengan warna merah yang memiki rentang kebisingan >90 berada pada titik 2 dan 13. Pengaruh volume lalu lintas pada tingkat kebisingan adalah berbanding lurus, yaitu dengan dengan meningkatnya volume kendaraan akan meningkatkan tingkat kebisingan, Pada Jam puncak volume kendaraan terdapat 10.535 unit menghasilkan kebisingan 86,04 dbA, sedangkan pada jam sepi kendaraan dengan jumlah kendaraan 3.246 unit menghasilkan kebisingan 79,96 dbA.
RETRACTED: Performance Analysis of Quantum Annealing with CNN Model For Analyzing and Predicting Abnormalities in DNA Sequence of Pleuro Pulmonary Blastoma
Authors: reddy Raswitha , T Santhisri
Published: November 2023

The authors have requested that this preprint be removed from Research Square.
Optimal Adaptive Sliding Mode Controller Design for a Wavelet-based Approximated Nonlinear System
Authors: PATHAK RAJEEV RANJAN , Paul Rimi , Sengupta Anindita
Published: November 2023

This work presents an optimization-based robust Adaptive Gain Sliding Mode (AGSM) controller for a nonlinear system where wavelet-based approximation technique is employed in contrary to the linearization technique. To find out the mother wavelet corresponding to the best approximation of nonlinear model, Minimum Description Length (MDL) criterion is utilized. Three different wavelet series are considered to find the best mother wavelet function. The constant of the sliding surface follows dynamical adaptive nature using Harmony Search Algorithm (HSA) based optimization technique to mitigate the extent of chattering. On the other hand, the gain adaptation method is used to calculate the gain of the switching control law of Sliding Mode Control. To ensure the stability of the closed-loop system with adapted gain as well as optimal sliding constants, Lyapunov Stability Criterion is exploited. To validate the performance of the proposed controller (Optimal AGSM Controller) in real time, an Inverted Pendulum System is taken with its wavelet approximated model. The results obtained from simulation as well as hardware validate the efficacy of the proposed design with the assurance of handling unbounded disturbances, parametric uncertainties, and chattering.
Towards a genomic resolution of the Phengaris alcon species complex
Authors: Lucek Kay , Blattner Lucas , Cornet Camille , Chittaro Yannick , Erhardt Andreas , Jaun Andreas , Jost Bernhard , Winiger Nathalie , Wymann Hans-Peter , Dušej Goran
Published: November 2023

Taxonomic entities below the species level often pose difficulties for conservation practice, especially when they are ecologically distinct from the nominal species. Genomic tools provide the opportunity to study and potentially resolve such cryptic diversity. The Alcon blue butterfly Phengaris alcon species complex is such a cryptic example, comprising different ecotypes or even subspecies, one of them is the high elevation taxon P. rebeli from the European Alps. We sequenced a first reference genome for Phengaris alcon. Furthermore, we generated whole genome resequence data for individuals of three Swiss ecotypes, i.e., the low elevation P. alcon, the mid elevation and high elevation Alpine P. rebeli and integrated genomic data from across Europe to study the relationship among these ecotypes. At a European scale, our results suggest that biogeography and the evolutionary context of diversification of the P. alcon complex is more multifaceted than previously suggested, falling in the range of more recent ecological speciation. In Switzerland, the three ecotypes were genetically isolated with only limited current gene flow between them. Past gene flow, however, could have given rise to the mid elevation ecotype. Our findings emphasise that high elevation P. rebeli should be treated as a distinct species. Our study highlights how the availability of reference genome assemblies allows to address so far open taxonomic questions in conservation research and that broadscale studies are needed to understand the biogeographic history of apparent diversification.
Nationwide incidence and blindness trends in patients with diabetic retinopathy using age-period-cohort analysis
Authors: Park Sue , Lee Ju-Yeun , Bae Kunho , Lee Sangjun
Published: November 2023

Objectives: To estimate the epidemiologic trends of blindness in patients with diabetic retinopathy (DR) and investigate the age-, period-, and cohort-specific effects on blindness.
Methods: A total of 1.5 million patients with DR aged 20 years or older were included from the Korean National Health Claims database from 2005 to 2019. Non-proliferative DR/ proliferative DR (NPDR/PDR) cohorts were constructed separately. Participants were identified as having a blindness in at least one eye. The incidence of blindness were calculated. Using a log-linear Poisson age-period-cohort (APC) analysis model, each effect on blindness were estimated for each study group.
Results: The incidence of blindness was 2162.36 per 100,000 in the NPDR group and 5339.29 in the PDR group. The blindness rate sharply decreased after 2011, with annual decreases of 5.6% and 4.4% in the NPDR and the PDR groups, respectively. People born between 1920 and 1930 had the highest overallrisk of blindness, with the risk decreasing rapidly after that. For those born after 1980, the risk started to increase in both sexes. Among the APC models, the combination model of age, period, and cohort effects showed the highest explanatory power (0.96).
Conclusions: In this nationwide long-term study, blindness in DR was not due to a single epidemiologic cause but rather a combination of biological age, social determinants, and healthcare policies. The increased risk of blindness in individuals in their 20s and 30s may even increase in the future and should not be ignored. Therefore, vigilance of younger patients are recommended.
Retinal Microstructure Based on Optical Coherence Tomography Predicts Visual Prognosis in Diabetic Macular Edema with Extended Intraretinal Cysts and Subretinal Fluid.
Authors: Zhu Lili , Tang Qunwu , Ren Peifang , Lou Dinghua , Tong Jianping
Published: November 2023

Purpose
We investigated the association between the retinal microstructure on optical coherence tomography (OCT) and the visual outcomes of diabetic macular edema (DME) with extended intraretinal cysts (IRCs) (maximum diameter ≥ 200 μm) and serous retinal detachment (SRD) who underwent anti-vascular endothelial growth factor intravitreal injections.
Methods
We retrospectively reviewed 58 eyes from 43 DME patients with extended IRCs and subretinal fluid who were treated with three consecutive intravitreal injections of ranibizumab with subsequent as-needed administrations. The OCT images were assessed at baseline and at 1, 3, 6, and 12 monthsafter treatment. The association of the baseline retinal microstructure on OCT with photoreceptor disruption at 12 months was investigated. We also evaluated the relationships between visual outcomes and baseline OCT parameters.
Results
The mean logarithmic minimum angle of resolution (logMAR) best-corrected visual acuity (BCVA) decreased from 0.66 ± 0.34 before treatment to 0.46 ± 0.32 after treatment (p < 0.001). ELM (R = 0.535, p < 0.001) and EZ (R = 0.612, p < 0.001) disruption at 12 months, but not CFT at 12 months (R = 0.185, p = 0.164), were associated with the logMAR BCVA at 12 months. The extent of EZ disruption (R = 0.256, p = 0.039) and the area ratio of SRD spaces (R = 0.409, p = 0.002) at baseline were positively correlated with the extent of EZ disruption at 12 months. The extent of ELM disruption (R = 0.641, p < 0.001) and the area ratio of IRCs (R = 0.287, p = 0.008) at baseline were positively correlated with the extent of ELM disruption at 12 months. The area ratio of SRD spaces (R = 0.400, p = 0.006) and the extent of ELM disruption (R = 0.393, p = 0.014) at baseline were predictive of the logMAR BCVA at 12 months.
Conclusion
The extent of EZ and ELM disruption was significantly associated with baseline OCT imaging characteristics. Both the baseline area ratio of SRD spaces and ELM disruption might be useful OCT biomarkers to predict the visual outcomes of DME patients with extended IRCs and SRD spaces.
Trial registration number and date of registration (retrospectively registered):
Spectral change of curcumin in DMSO-NaOH solution after exposure to air
Authors: Roy Bahnishikha , Singha Joydeep , Goswami Santanu , Saikia Jyoti Prasad
Published: November 2023

Curcumin (CurH3) forms a blue derivative (CurH2−) when it combines with superoxide radicals. The blue colouring is caused by the proton loss of curcumin to the superoxide radicals. In DMSO, in the presence of excess NaOH and dissolved oxygen, superoxide radicals are produced by consuming the hydroxyl ion which causes the deprotonation of curcumin (Cur3−) and gives the corresponding orange colour. The production of superoxide radicals increased as it was more exposed to air (maybe oxygen). As a result, the pH drops, and the orange derivative is protonated by one H+ ion, producing the corresponding blue colour derivative (CurH2−). The conversion of the orange derivate to the blue derivative may suggest the presence of oxygen in the surrounding atmosphere, allowing the feasibility of a novel oxygen sensor. The physicochemical characteristics and stability of this blue-coloured curcumin derivative are investigated. The distinct colour shifts of curcumin upon the addition of different volumes of NaOH were also investigated. The UV-Vis and FTIR analyses were used to study the stability of the blue curcumin derivative.
Wave Mechanics of Microwave Absorption Films: Part 2 - Distinguishing Film from Material
Authors: Liu Yue , Liu Ying , Drew Michael
Published: November 2023

A groundbreaking wave mechanics theory for film has emerged, introducing innovative concepts that diverge significantly from the conventional microwave absorption framework. This pioneering theory suggests that optimizing absorption in films can be achieved through the phenomenon of wave cancellation, enabling efficient absorption with materials exhibiting reduced attenuation power and diminished microwave penetration. It delineates a clear distinction between films and materials, offering a streamlined and more relevant theory as a substitute for the often intricate and ambiguous impedance matching concept. By defining the material's attenuation power within the film, this research provides a fresh perspective on physics, conclusively demonstrating that film absorption differs from material attenuation. This constitutes a significant advancement in our understanding of film physics. It also underscores the fundamental difference in physics between film and its constituent material.