bluuebunny/crossref_metadata_2025 huggingface dataset:
Penerapan Artificial Neural Network Untuk Memprediksi Error dalam Perancangan Aplikasi Monitoring Tetes Cairan Infus
Authors: Astutik Liya Yuni , Syafii Imam
Published: July 2024

Decision-making in error prediction during the design process of infusion drip monitoring applications plays a crucial role. Designing this application is necessary because manual monitoring by medical staff is prone to errors and inaccuracies. Therefore, the need for accurate predictions in both planning and error management must be further investigated. This research discusses the benefits of the Artificial Neural Network (ANN) methodology in addressing error values in infusion drip monitoring applications during the design process. ANN is chosen for its ability to handle data complexity and non-linear patterns in infusion drip rates. Errors in infusion dosage can be fatal, ranging from patient instability to severe complications. Designing infusion drip monitoring applications automates the process and ensures accuracy, reducing the workload of medical staff and enhancing patient safety. This application also allows for more consistent and real-time monitoring, enabling quicker medical intervention when issues arise. The ANN methodology used includes both forwardpropagation and backpropagation, employing a binary sigmoid activation function with a learning rate of 0.03 and a maximum epoch setting of up to 1000. The research results indicate that the model-building procedure consists of several stages: (1) Determining input based on infusion drip rate readings. (2) Splitting the data into training and testing datasets. (3) Normalizing the data. (4) Building the forwardpropagation and backpropagation algorithm by determining the number of hidden layers, optimal input, and model weights. (5) Denormalizing the data. (6) Testing the model's accuracy. The ANN simulation revealed the best network structure using a 3-40-1 configuration (3 input variables, 40 hidden layers, and 1 output). The results achieved an average error prediction accuracy of 98.6%.
Sentimen Analisa Ulasan Aplikasi Access by KAI pada Google Play Store menggunakan Algoritma K-NN
Authors: Jiana Nur Afina , Hartono Budi
Published: July 2024

Access by KAI is an application developed by PT. Kereta Api Indonesia (KAI) that plays an important role in simplifying the train ticket booking process for its users. The application has been downloaded more than 10 million times through the Google Play Store, with a review rating of 2.2 out of a scale of 1 to 5. This relatively low rating indicates a level of dissatisfaction among users. Moreover, the high number of negative reviews compared to positive reviews could also cause bias in the analysis results. Reviews from other users greatly influence the perception of the app's performance and quality among potential users. Therefore, this study aims to conduct a sentiment analysis of the application reviews published on the Google Play Store. The review data analyzed includes 1300 negative reviews and 457 positive reviews considered most relevant to provide a comprehensive overview of user perception. The method used in this analysis is the K-Nearest Neighbor (K-NN) Algorithm. The results of the study show an accuracy rate of 80%, a precision of 73%, a recall of 63%, and an f1-score of 65%. These findings are expected to provide insights for the app developers to improve the quality and performance of the Access by KAI application.
Segmentasi Pelanggan Menggunakan Fuzzy C-Means dan FP-Growth Berdasarkan Model LRFM untuk Rekomendasi Produk
Authors: Rahmah Astriana , Afdal M
Published: July 2024

Bazmart Pelalawan is a part of the National Zakat Agency (BAZNAS) program in Pelalawan Regency, which has implemented strategies to retain customers. However, these strategies have not yet succeeded in fully understanding customer characteristics, resulting in a decline in customer trust and their willingness to shop again. Additionally, Bazmart lacks proper guidelines for offering products that meet customer needs. This research aims to enhance product recommendations by integrating LRFM analysis into data mining techniques. The parameters considered include customer LRFM values, customer segmentation, and products frequently purchased together over a year of transaction data. Fuzzy C-Means and FP-Growth algorithms were used for segmentation and association analysis. The segmentation results identified two customer clusters with a Davies-Bouldin Index (DBI) value of 0.628, indicating good cluster quality. In the association analysis, a minimum support (minsup) of 30% and a minimum confidence (mincof) of 70% were used, resulting in 8 rules for cluster 1 and 17 rules for cluster 2. From the two association pattern results, the highest rules were obtained, namely in Drinks and Snacks and Bread with a support value of 0.426 and a confidence value of 0.926 resulting in a value of 0.394. These rules provide insights that Bazmart Pelalawan can use to develop more effective and targeted direct marketing strategies for each customer cluster. Thus, this research is expected to help Bazmart Pelalawan better understand customer characteristics and improve customer loyalty through more targeted product recommendations.
Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning
Authors: Nur Zikri Kholifah , Wijaya Rifki , Wulandari Gia Septiana
Published: July 2024

This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Linear Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.
Penggunaan Naïve Bayes Classifier dalam Analisis Sentimen Ulasan Aplikasi McDonald's: Perspektif Pengguna di Indonesia
Authors: Kurniawan Salsha Dara Shinta , Fauzy Akhmad
Published: July 2024

The McDonald's mobile app has become popular among users, who often review their experiences through various review platforms. However, the sheer number of reviews suggests that the app's performance is still not satisfactory. This research aims to analyze public sentiment towards McDonald's app reviews using the Naïve Bayes Classifier algorithm. This algorithm was chosen because of its ability to classify text based on probability and its wide use in sentiment analysis. The research process began with the collection of review data totaling 4.996. Of these, 1.575 data showed neutral sentiment, while 2.137 data revealed positive sentiment, and 1.104 data showed negative sentiment towards the app. However, for the purposes of analysis using the Naïve Bayes algorithm, the focus is only on data that has positive and negative sentiment labels. Thus, the total amount of data used is 3.241 data, consisting of 2.137 positive data and 1.104 negative data. Followed by text pre-processing which includes cleaning, normalization, stopwords, stemming, tokenizing. The dataset is then divided into training data (80%) and testing data (20%). Naïve Bayes Classifier algorithm is used to classify the reviews into positive, negative, and neutral categories ignored. The results show that this model has an 90% accuracy rate in classifying sentiment. This analysis is necessary for the company's evaluation in order to know the public sentiment regarding the McDonald's app. The conclusion of this study shows that although the Naïve Bayes Classifier algorithm is quite effective in the sentiment classification of McDonald's app reviews, it is not enough to classify the sentiment of the McDonald's app.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values
Authors: Windarto Agus Perdana , Rahadjeng Indra Riyana , Siregar Muhammad Noor Hasan , Yuhandri Muhammad Habib
Published: July 2024

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Prediksi Potensi Keterlambatan Pembayaran Biaya Kegiatan Sekolah Menggunakan Algoritma Naïve Bayes
Authors: Solehan Solehan , Sugiarto Sugiarto , Mahdiana Deni , Kharmytan Yan Baktra
Published: July 2024

Tuition fees are one of very important component in the implementation of education and school development, because tuition fees are the main requirement to be able to implement school programs that have been designed in the one school year activity plan, apart from that, school fee is also used for maintenance or development of school facilities and infrastructure. Cempaka Vocational School is a private school in Central Jakarta, region two which requires its students to pay school activity fees by paying for each activity (7 days before the activity takes place) or in installments every month (total activity costs for one year divided by twelve ). Meanwhile, based on data obtained from the school treasurer, there were 23.2% arrears in the 2020/2021 school year and 38.7% arrears in the 2021/2022 school year (The data used in this research was taken from student payment data for the 2020/2021 and 2021/2022 school years ) which has not been resolved by students, this will become a big problem for Cempaka Vocational School if a solution is not immediately found to overcome this problem. The aim of this research is to build a prediction system using the Naïve Bayes method which will produce an accurate or late classification in paying school activity fees to be used as a recommendation in policy making and finding solutions early on so that there are no delays in paying school fees by students. /i. The results of this research produced an accuracy of 70.83%, precision of 70.59% and recall of 85.71 so that it could predict delays in school activity costs according to the needs of Cempaka Vocational School.
Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM)
Authors: Arif Siti Novianti Nuraini , Siregar Amril Mutoi , Faisal Sutan , Juwita Ayu Ratna
Published: July 2024

Cardiovascular disease (CVD) is a general term for disorders related to the heart, coronary arteries, and blood vessels. These diseases are most commonly caused by blocked blood vessels, either due to fat buildup or internal bleeding. According to the WHO, each year, cardiovascular diseases account for 32% of all deaths, which translates to about 17.9 million people annually. The numerous factors causing CVD make it challenging for doctors to diagnose patients who are at low or higher risk of heart attacks. A machine learning model is needed for the early recognition of heart attack symptoms. Supervised learning models such as KNN and SVM were used in previous studies without feature selection, with datasets obtained from Kaggle. PCA was applied to reduce data dimensions to smaller variables. With the use of confusion matrix and ROC curve evaluations, the accuracy results of the previous KNN model improved from 83.6% to 90.16%. The SVM model also saw an accuracy increase from 85.7% to 86.88%. The use of PCA feature selection demonstrated an improvement in accuracy in the study. The KNN model, with a higher accuracy rate of 90.16%, is better for classifying individuals as normal or diagnosed with a heart attack.
Implementasi Algoritma Transformers BART dan Penggunaan Metode Optimasi Adam Untuk Klasifikasi Judul Berita Palsu
Authors: Subagyo Ageng Ramdhan , Sasongko Theopilus Bayu
Published: July 2024

Classification is a process of identifying new data provided based on validation of previous data. One classification process that can be used is fake news classification. The classification process requires as little time as possible to get maximum results, so a faster method is needed to classify news. The BART algorithm can be a method that can be used to carry out classification and use Adam optimization to improve the performance of the algorithm. The aim of this research is to classify fake news, whether the BART algorithm and Adam optimization are able to provide good results and to label whether the news is fake or not. The results of this process are based on the use of a dataset of 65% for training, 30% for validation, and 5% to produce 2 BART models. With the additional use of Adam optimization and several other parameters for the training process, the first model was able to provide accuracy performance of 92.88%, training loss reached 12.2%, and validation loss reached 28.4% and the second model produced an accuracy of 92.63 %, training loss 15% and validation loss reaching 20.2%. In the first model, it can predict 105 data labeled negative and 1306 positive data. Meanwhile, the second model was able to predict 128 data labeled negative and 1283 positive data.
Analisis Sentimen Cyberbullying Pada Media Sosial X Menggunakan Metode Support Vector Machine
Authors: Syahira Melani Alka , Kurniawan Rakhmat
Published: July 2024

Twitter or the platform now known as social media X is now one of the social networks most popular. Its popularity not only does it have a positive impact but it also has a negative impact on both users and non-users of the X platform. The negative impact is a lot many social media users use it to insult or defame, known as Cyberbullying. Cyberbullying is a deliberate act and occurs virtually through verbal intimidation or ongoing harassment on the internet or social media. Cyberbullying can cause serious emotional impacts for the victim, such as depression, behavior changes, mood swings, and sleep and appetite disorders. To overcome this problem, sentiment analysis using data from X to determine the level of accuracy with the Support Vector Machine algorithm. Data was collected through Crawling as many as 1000 data, then Preprocessing was carried out. After preprocessing, data labeling was carried out manually, there were 157 positive data and 843 negative data. Then, the data was separated into two parts, namely 80% training data and 20% testing data. The results of data processing showed 87% accuracy, 88% precision, 99% recall, and 93% f1-score.
Resultados quirúrgicos de la herniorrafia inguinal en los pacientes mayores de 80 años: Revisión sistemática y metaanálisis
Authors: Ramírez-Giraldo Camilo , Van-Londoño Isabella , Navarro-Alean Jorge Alberto , Rojas-López Susana
Published: July 2024

Introducción. Los pacientes octogenarios y nonagenarios conforman un grupo etario en progresivo crecimiento. La hernia inguinal es una patología que aumenta progresivamente con la edad. Este trabajo tuvo como objetivo conocer los resultados quirúrgicos de los pacientes mayores de 80 años a quienes se les realizó herniorrafía inguinal.
Métodos. De acuerdo con las guías PRISMA, se realizó una revisión sistemática de PubMed, Embase y Google Scholar. Se incluyeron estudios que reportaron la incidencia de complicaciones y mortalidad después de una herniorrafía inguinal en los pacientes octogenarios y nonagenarios. Se calculó la proporción de pacientes con complicaciones después de una herniorrafía inguinal según los datos presentados, con su respectivo intervalo de confianza del 95 %.
Resultados. Catorce estudios reportaron un total de 19.290 pacientes, entre quienes se encontró una incidencia acumulada de infección del sitio operatorio de 0,5 % (IC95% 0,460 - 0,678), seroma de 8,7 % (IC95% 6,212 - 11,842), hematoma de 2,6 % (IC95% 2,397 - 2,893), dolor crónico de 2,1 % (IC95% 0,778 - 4,090) y recidiva de 1,2 % (IC95% 0,425 - 2,284), para una morbilidad de 14,7 % (IC95% 9,525 - 20,833).
Conclusión. Las complicaciones de la herida quirúrgica, el dolor crónico y la recidiva en los pacientes mayores de 80 años a quienes se les realiza herniorrafia inguinal son comparables con las de la población general.
PLANO DE ALEITAMENTO MATERNO:
Authors: PIAHUI DOS SANTOS BRUNA , XAVIER RAVELLI ANA PAULA , BASSO ZANLORENZI GISELE , PETEAN PARMEJIANI ELEN , ROSSI KISSULA SOUZA SILVANA REGINA , DIAS ALDRIGHI JULIANE , PEREIRA MARTINS RAFAELLA , LOEWEN WALL MARILENE
Published: July 2024

Objetivo: Descrever o processo de construção do material didático “Meu plano de aleitamento materno” para educação, apoio e promoção do aleitamento materno para gestantes e pessoas lactantes. Método: Este é um estudo metodológico realizado em três fases: diagnóstico situacional, revisão de literatura e contextualização da prática profissional, e construção da tecnologia educacional baseada em políticas públicas e programas de aleitamento materno brasileiros. O estudo ocorreu de março de 2022 a outubro de 2023 e utilizou-se o software Canva para construção do produto. Resultados: Desenvolveu-se uma tecnologia inédita a nível nacional que integra educação e cuidado em saúde por meio do plano de aleitamento materno. Considerações finais: Considera-se que o material didático intitulado “Meu plano de aleitamento materno” é um recurso eficaz para promover a autonomia da mulher no ciclo gravídico-puerperal e pode ser utilizado na enfermagem para educar, promover e apoiar o aleitamento materno em diversos níveis de atenção à saúde.
The effects of mixes of peat and olive pomace at various ratios on the vegetative growth of potted grapevine saplings
Authors: Odabaşıoğlu Mehmet İlhan , Sakar Ebru
Published: July 2024

Numerous studies have been conducted in order to utilize the olive pomace, the solid waste left over from the oil processing of olives, which is widely cultivated, especially in Mediterranean countries, and to consider it a raw material that has added value instead of being considered waste. Some of these research are focused on establishing the re-utilization of olive pomace in agricultural production. Although some studies have reported that olive pomace can be utilized as fertilizer, soil improvement regulator, solid media culture, and even mulching material, this study was carried out due to the lack of sufficient scientific data on whether or not this material can be used as a growth medium in the cultivation of potted grapevine saplings. The study was carried out by growing ungrafted grapevine saplings of Vitis vinifera L. cv. Hatun Parmağı on media with peat and olive pomace at different ratios for six months, and then some vegetative growth parameters were examined. Due to the elevated olive pomace ratios in the growth medium, shoot and root growth of the grapevine saplings were restricted. The chlorophyll index and root fresh weight decreased dramatically when more than 25% (v/v) and 20% (v/v) crude pomace were available in the growth medium, respectively. Moreover, the availability of olive pomace in the medium significantly reduced shoot length, number of leaves, shoot weight, and leaf weight of grapevine saplings, regardless of the amount of olive pomace. However, it was found that 15% (v/v) or less of crude olive pomace could be utilized in growth medium mixtures when growing potted grapevine saplings; however, higher ratios would prevent the saplings from attaining marketable quality. While designing new studies, examining different grape varieties, fruit species, and lower olive pomace ratios would contribute to new and more comprehensive findings on the utilization of olive pomace in growing potted saplings.
Teachers’ Beliefs About Language Diversity and Multilingual Learners: A Systematic Review of the Literature
Authors: Gallagher Melissa A. , Scrivner Shaimaa
Published: July 2024

Teachers’ beliefs influence their teaching practices. Given the U.S. Secretary of Education’s push to increase multilingualism, this systematic literature review examines teachers’ beliefs about language diversity and multilingual learners in relation to teacher experiences, teaching practices, and external factors. Using 17 search terms, we searched seven databases for empirical, peer-reviewed studies that examined K–12 teachers’ language beliefs. We analyzed 63 studies that met our criteria using a framework of teacher beliefs. Our findings indicate that researchers use myriad terms to capture the participating teachers’ beliefs. These terms could be grouped into dominant language, subtractive, additive, and pluralist beliefs. We also found connections between teachers’ beliefs on one side and their life experiences, instructional practices, and external factors, such as high-stakes tests and school contexts, on the other side. We added teachers’ beliefs about families, content and knowledge, and diversity to the framework, as well as external factors.
The drumming signals of the British endemic stonefly Brachyptera putata (Newman) (Plecoptera: Taeniopterygidae)
Authors: Macadam Craig R.
Published: July 2024

The current study details the male drumming signals of Brachyptera putata (Newman) and compares it with that of other European species in the genus.
Bir Şehrin Hafızasını Aralamak: Prusias Ad Hypium (Konuralp) Antik Kenti
Authors: Pınarcik Pınar , Berkant Evrensel Barış
Published: July 2024

Eskiçağ’da Bithynia bölgesinin sınırları içinde yer alan Prusias Ad Hypium (Konuralp) antik kenti, günümüzde Düzce il merkezinin 8 km kuzeyine lokalize edilir. Anadolu’daki sayısız antik kentten biri olan Konuralp; antik tiyatrosu, atlı kapısı, su kemerleri, Roma köprüsü, surları ve Osmanlı dönemine ait yapı kalıntılarıyla tarihsel süreci gözlemlediğimiz bir yerleşimdir. Bu çalışmada 2017-2018 yılları arasında gerçekleştirilen “Prusias Ad Hypium’dan Konuralp’e Düzce’nin Kültürel Mirası” adlı projeden elde edilen verilerle, antik kentin geçmişinin günümüze yansımaları, Eskiçağ tarihine ait mekânların toplumsal hafıza, kolektif bellek oluşumundaki rolü, inşa sürecindeki kentin kültürel kimliğinin oluşumuna katkıları irdelenmiştir. Bu makalede kullanılan yöntem özellikle günümüzde tarih bilimi tarafından sık kullanılan ancak Eskiçağ alanında kullanımına çok rastlanılmayan sözlü tarih çalışması olmuştur. Çoğunlukla yerel halktan 50 yaş üstü kişilerle yapılan görüşmelerde anılar, mekânsal alanlarda yaşanan anılar, ritüeller, söylenceler, kısacası hafızalarında kalanları satır aralarına kaydedilmiştir. Bu çalışma ile anlatıların kaybolmadan kayıt altına alınması ve bölge insanında tarih bilinci oluşturarak kültürel mirasın korunmasına yönelik adımların atılması hedeflenmiştir. Aynı zamanda ortak hafıza mekânları da tespit edilmiştir. Toplumsal süreklilik için geçmişten günümüze taşıdığımız ortak hafıza kentleşme sürecinde çok önemlidir. Hem somut hem somut olmayan kültürel mirasa ait kayıt altına alınan veriler ışığında arkeolojik mekânlar ve eserlerle ilgili o dönemde yaşatılan ancak günümüzde yaşatılmayan ritüellerin, söylencelerin ortaya çıkarılması da diğer bir amacı oluşturmuştur.
THE INVESTIGATION OF HEALTH TOURISM POTENTIAL: THE CASE OF SAMSUN PROVINCE
Authors: Oruç Muhammet Ali , Caner Şennur , Vatansever Kemal
Published: July 2024

THE INVESTIGATION OF HEALTH TOURISM POTENTIAL: THE CASE OF SAMSUN PROVINCE

Abstract

Aim: To determine the current situation by evaluating the health services offered to health tourists in Samsun province and to reveal the health tourism potential with health tourism data.

Methods: The population of this descriptive cross-sectional study consists of health tourists who received health services in Samsun province between 2017-2021. Data were obtained through the foreign patient tracking system and e-nabız.

Results: The number of health tourists has increased with increasing momentum over the years. According to the type of arrival of health tourists, health services were provided mostly within the scope of health tourism. The clinics to which health tourists mostly applied are emergency medicine, gynaecology and ophthalmology clinics. The highest number of health tourist applications were in July, August and September.

Conclusion: It was determined that Samsun province has a health tourism potential. This potential should be developed by integrating medical tourism with other types of health tourism and by carrying out joint activities with the co-operation of all stakeholders.

Keywords: Health, Health Tourism, Health Tourists, Medical Tourism
Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data
Authors: Moerth Eric , Sidak Kevin , Maliga Zoltan , Moeller Torsten , Gehlenborg Nils , Sorger Peter , Pfister Hanspeter , Beyer Johanna , Krüger Robert
Published: July 2024

We present Cell2Cell, a novel visual analytics approach for quantifying and visualizing networks of cell-cell interactions in three-dimensional (3D) multi-channel cancerous tissue data. By analyzing cellular interactions, biomedical experts can gain a more accurate understanding of the intricate relationships between cancer and immune cells. Recent methods have focused on inferring interaction based on the proximity of cells in low-resolution 2D multi-channel imaging data. By contrast, we analyze cell interactions by quantifying the presence and levels of specific proteins within a tissue sample (protein expressions) extracted from high-resolution 3D multi-channel volume data. Such analyses have a strong exploratory nature and require a tight integration of domain experts in the analysis loop to leverage their deep knowledge. We propose two complementary semi-automated approaches to cope with the increasing size and complexity of the data interactively: On the one hand, we interpret cell-to-cell interactions as edges in a cell graph and analyze the image signal (protein expressions) along those edges, using spatial as well as abstract visualizations. Complementary, we propose a cell-centered approach, enabling scientists to visually analyze polarized distributions of proteins in three dimensions, which also captures neighboring cells with biochemical and cell biological consequences. We evaluate our application in three case studies, where biologists and medical experts use Cell2Cell to investigate tumor micro-environments to identify and quantify T-cell activation in human tissue data. We confirmed that our tool can fully solve the use cases and enables a streamlined and detailed analysis of cell-cell interactions.
Nostalgia and Health: A Longitudinal Network Analysis of Different Nostalgic Experiences
Authors: Huang Kuan-Ju , Uricher Raphael
Published: July 2024

The study examines the long-term dynamics of the relationship between nostalgia and health using a population-based longitudinal sample in the Netherlands (N = 958). We identified five types of nostalgia–Home, Peers and shared experiences, Emotional security, Innocence, and Leisure and media–and explored their relationships with health using network analyses. We found bidirectional relationships between nostalgia and health over a one-year interval. Self-rated health and mental health negatively predicted nostalgia centered on Peers and shared experiences, Emotional security, and Innocence. Nostalgia, especially Emotional security and Innocence, negatively predicted self-rated health and mental health. The effects were further moderated by age. Cross-lagged relationships from nostalgia to health were found in younger but not older adults, while relationships from health to nostalgia were found primarily among older adults. In sum, we demonstrate the importance of considering age and type of nostalgia when exploring long-term relationships between nostalgia and health.
Effects of Geometric Stress Concentration in the Design of Thick Compound Cylinders- A Review
Authors: Kiplagat Naftali , Masu Leonard , Nziu Patrick
Published: July 2024

This paper reviews some of the critical considerations that are important during the design of high-pressure vessels. These critical considerations include Stress Concentration Factors (SCF), measurement of stress distribution, and geometric design parameters affecting stress concentration in the design of pressure vessels with a specific focus on thick compound cylinders. Further, the motivation of this study is to analyze stress concentration that occurs after the provision of cross bores in pressure vessels. Stress analysis can be performed using experimental, analytical, and numerical techniques. Hence, the study concluded that there is no known analysis carried out on the effects of cross bores on thick compound cylinders using Finite Element Analysis (FEA). Thus, there is need for understanding the effects of optimization of geometric parameters of cross bore size, shape, location, obliquity and thickness ratio, in the design of thick compound cylinders.
Complex Temporal Dynamics of Mental Health Indicators: A Longitudinal Network Approach Perspective
Authors: Adamkovic Matus , Simsa Benjamin , Jozefiakova Bibiana , Mikulášková Gabriela , Babincak Peter , Baník Gabriel , Bočanová Jaroslava , Fedáková Denisa , Kačmariková Klára , Kačmár Pavol , Kentoš Michal , Majdáková Viktória , Vargová Lenka , Zibrínová Ľubica , Ropovik Ivan
Published: July 2024

Although mental disorders are long considered complex dynamic systems, our understanding of the mutual interactions and temporal patterns of their symptoms remains limited. In this longitudinal study, we examined the structure and dynamics of four key mental health indicators—depression, anxiety, PTSD, and insomnia—in a representative sample of the Slovak population (effective N = 3,874) over 10 waves spanning three and a half years. For each of the constructs, a longitudinal panel network model was estimated. The temporal relationships between symptoms were mostly weak, with the autoregressive effects typically being stronger. In depression, anxiety, and insomnia, some causal chains and feedback loops were identified. In all constructs, both contemporaneous and between-person networks showed dense connections. The findings provide critical insights into the complexity of mental health development, offering potential targets for interventions and prevention strategies.
Cryptocurrency in Heterodox Economic Theory and Institutional Practice
Authors: Eichacker Nina
Published: July 2024

While cryptocurrencies have existed since 1990, they have come to increasing prominence after 2009, when BitCoin was created. Since 2009, a proliferation of cryptocurrencies has emerged, prompting both debate and dramatic flurries of economic activity. While some argue that cryptocurrencies may present an alternative to state-backed fiat currencies, others characterize them as volatile financial assets that are used to exploit particularly vulnerable demographic groups. This chapter examines cryptocurrencies through two lenses: a historic-institutionalist account of how they have developed as both a financial asset and an alternative to the traditional centralized financial system based on banks, and a Keynesian analysis of crypto currencies as financial assets particularly prone to the generation of bubbles and crashes. It considers both the ecological and economic fallout from the creation of these assets, as well as lessons that traditional financial institutions may learn from cryptocurrencies and the institutions through which purchasers may access these assets.
Un pouvoir en réseau. Le patriciat académique de Genève et Bâle (1890–1957)
Authors: Benz Pierre
Published: July 2024

Cet article s’intéresse au cas peu étudié des savants-patriciens à Bâle et Genève. Dans la première moitié du 20e siècle, les pratiques d’héritage, de transmission de fonctions académiques entre générations, mais aussi les alliances entre familles, vont de pair avec l’occupation de fonctions de pouvoir académique et des exigences de formation, notamment à l’étranger. Dans une première partie, l’article présente les grandes évolutions de la composition de l’élite académique de 1890 à 1957 selon trois indicateurs : la proportion des postes professoraux occupés par des descendants de familles patriciennes, le lieu du doctorat et l’occupation de fonctions de recteur des Universités. Dans une seconde partie, il montre la dimension collective du pouvoir des savants-patriciens au travers de l’étude de certaines dynasties professorales et des liens familiaux qui lient les sphères académique, économique et politique.
Road User Video Evidence of Road Traffic Offences: Preliminary Analysis of Operation Snap Data and Suggestions for a Research Agenda
Authors: Farrell Graham , Lovelace Robin , O'Hern Steve
Published: July 2024

This study uses data from Operation Snap (OpSnap), the UK police’s national system to receive road users’ video evidence of road traffic offences. Data from one police force area for 39 months (January 2021 to March 2024) (N = 20,364 records) is analysed. Half were submitted by vehicle drivers (49.8%), a third by cyclists (34.7%), 7.2% by pedestrians, 2.2% by horse riders, 0.2% by motorcyclists, and 5.8% were unknown. We estimate that, relative to road distance travelled, cyclists were 20 times more likely to submit video evidence than vehicle drivers. The most common offences overall were driving ‘without reasonable consideration to others’ or ‘without due care and attention’. Half (53.5%) of reported cases resulted in the recommended disposal of an educational course, % no further action 12.6% conditional offer, and 1.6% resulted in court appearance. A research agenda using OpSnap data is outlined that could emerge if national datasets are compiled and responsibly opened-up and made available for research and policy-making: data-driven research should identify hotspot locations and other correlates of dangerous and antisocial road use at regional, and local levels; research projects should investigate disposal-related decision-making, video quality, and the role of supporting evidence; offence concentration (recidivism, repeat submitters of evidence, spatial hotspots) and case progression including court cases should be explored with reference to new video evidence. We conclude that datasets derived from publicly-uploaded video submission portals have the potential to transform evidence-based policy and practice locally, nationally and internationally.
Renewable Energy Sources in Poland in 2014-2023 and the Perspective of their Development until 2030. A Contribution to the Discussion
Authors: Świdyńska Natalia
Published: July 2024

This article discusses the state of renewable energy sources (RES) in Poland, comparing it with the one in the EU-27. It has been emphasised that the Polish government is obliged carry out the EU policy, also in terms of energy targets. Thus, Poland is committed to achieving a 31.5% share of RES in all energy generation sources by year 2030. The aim of this study has been to determining the state of RES in Poland and determining the prospects for their further development based on it. The state of RES development in Poland has been determined on the basis of generally available statistical data (Poland Statistics GUS). Eurostat was the source of data pertaining to the European Union. According to preliminary data, in 2023, the share of RES in Poland was 27%, compared to 44% in the EU-27. Having determined the state of RES development in Poland, we were able to identify prospects for their future development. The prognosis made in this study substantiated the conclusion that the set target of 31.5% share of RES in all energy sources will not be achieved in Poland. The Polish economy is progressing in the right direction, and the RES share is increasing year to year, but the pace of this progress is too slow. Some ways of increasing the RES contribution to overall energy generation as well as certain obstacles to the growth of RES are implicated in this article. Attention has been drawn to the impact of the COVID-19 pandemic and war in Ukraine on the energy sector.
Salvaging the transected hypoglossal nerve using descendens hypoglossi in patients undergoing hypoglossal-facial nerve anastomosis for facial palsy: a randomized clinical trial
Authors: Tang Yinda , Chen Zheng , Zhu Jin , Zhao Hua , Ying Tingting , Wang Baimiao , Zhu Wanchun , Wang Haopeng , Cai Xiaomin , Shen Yiman , Zhou Ping , Chang Bowen , Xia Wenzheng , Wei Xiangyu , Wang Xueyi , Zhang Xin , Zhong Jun , Wang Xinjun , Fu Xudong , Wang Dengbin , Liu Ming , Zhang Weituo , Li Shiting
Published: July 2024

OBJECTIVE

Hypoglossal-facial nerve anastomosis (HFA) is the most commonly used surgical treatment for severe facial palsy that does not respond to conservative treatments. A major complication of HFA is the loss of tongue function. The authors aimed to evaluate whether anastomosing the transected hypoglossal nerve using the ramus descendens hypoglossi could prevent tongue deviation and dysfunction in patients undergoing HFA.

METHODS

In this randomized trial, adult patients with severe peripheral facial palsy (House-Brackmann grade V or VI) who did not respond to at least 6 months of conservative treatment were randomized at a 1:1 ratio to undergo either HFA alone (control group) or HFA plus anastomosis between the hypoglossal nerve and descendens hypoglossi (intervention group). The primary endpoint was tongue deviation angle at 12 months. Key secondary endpoints included tongue disability (chewing difficulty, swallowing defect, and articulation defect), tongue disability index (TDI; range 1–4, with a higher score indicating more severe disability), and facial nerve function.

RESULTS

Twenty patients were enrolled (10 in each group). At 12 months, the tongue deviation angle was significantly lower in the intervention group than in the control group (7.8° ± 5.1° vs 23.6° ± 9.6°, p < 0.001). Although not statistically significant, the intervention group had lower rates of chewing difficulty (1/10 vs 3/10, p = 0.58), swallowing defect (1/10 vs 5/10, p = 0.14), and articulation defect (2/10 vs 6/10, p = 0.17). TDI was significantly lower in the intervention group (1.5 ± 0.6 vs 2.5 ± 0.3, p < 0.001). The percentage of the patients achieving House-Brackmann grade II or III was 80% in each group.

CONCLUSIONS

Anastomosis of the descendens hypoglossi to the transected hypoglossal nerve attenuated tongue deviation in patients undergoing HFA for facial palsy, without compromising facial nerve function.

Clinical trial registration no: ChiCTR2000034372 (Chinese Clinical Trials Registry).
Which interbody device minimized nonunion, subsidence, and reoperation after anterior cervical discectomy and fusion? A systematic review and meta-analysis comparing allograft versus polyetheretherketone cage
Authors: Kim Chul-Ho , Lee Dong-Ho , Hwang Chang Ju , Cho Jae Hwan , Park Sehan
Published: July 2024

OBJECTIVE

Nonunion and significant subsidence after anterior cervical discectomy and fusion (ACDF) are associated with poor clinical outcomes, which occasionally lead to revision surgery. Allograft and polyetheretherketone (PEEK) cages are the two most commonly used interbody spacer devices for ACDF. Although studies have been conducted to compare the efficacies of these two interbody materials, the question remains regarding the superiority of one over the other. Therefore, the authors conducted a systematic review and meta-analysis to compare nonunion, subsidence, and reoperation rates after ACDF using allograft and PEEK cages as interbody devices.

METHODS

In this systematic review and meta-analysis, the authors systematically searched the MEDLINE, EMBASE, and Cochrane Library databases for studies published prior to November 2023 that compared the efficacy and safety of allograft and PEEK cages for ACDF. A pooled analysis was designed to identify differences in nonunion, subsidence, and reoperation rates between the two interbody devices.

RESULTS

Ten studies involving 1462 patients (allograft, 852 patients; PEEK cage, 610 patients) were included. The pooled analysis demonstrated that allograft had a significantly lower rate of nonunion compared to that of PEEK cages (OR 0.33, 95% CI 0.14–0.79; p = 0.01). Furthermore, the reoperation rate due to nonunion was significantly higher with PEEK cages compared to that with allograft (OR 0.28, 95% CI 0.11–0.71; p < 0.01), whereas the reoperation rate due to overall causes did not display significant results (OR 0.38, 95% CI 0.11–1.29; p = 0.12). The incidence of significant subsidence (OR 0.66, 95% CI 0.28–1.55; p = 0.34) and the mean amount of subsidence (standard mean difference 0.03, 95% CI −0.42 to 0.47; p = 0.90) did not demonstrate significant differences between allograft and PEEK cages.

CONCLUSIONS

Overall, the current meta-analysis suggests the advantages of allograft over PEEK cages used for ACDF, due to an enhanced fusion rate and minimized revision risk, with no increase in the risk of subsidence.
Novel method of iliac accessory rods for rod fracture prevention in adult deformity surgery: a case series of 82 patients with outcomes and complications
Authors: Berlin Connor , Ben-Israel David , Sardi Juan P. , Park Brian J. , Yen Chun-Po , Shaffrey Mark E. , Ibrahim Sufyan , Smith Justin S.
Published: July 2024

OBJECTIVE

Primary rod fracture after surgery for adult spinal deformity (ASD) is a leading cause of revision, with recent prospective multicenter fracture rates reported at 11%–14% by 2 years. Consequently, the addition of supplemental rods has been explored to reduce fractures. Here the authors describe their experience with a novel iliac accessory rod technique in which each accessory rod anchors to an independent iliac bolt caudally via lateral connector, and attaches to the primary rod rostrally via side-to-side connector.

METHODS

This retrospective, single-center case series included patients who underwent thoracolumbar/lumbar fusion for ASD between March 2019 and August 2023. Data on baseline demographics, radiographic parameters, surgical characteristics, complications, rod fracture, and revision rates were collected. Paired, 2-tailed t-tests were used to compare pre- and postoperative radiographic outcomes. Rod fracture rates were compared to prior investigations via chi-square goodness of fit testing. The technique for iliac accessory rod placement is described.

RESULTS

The study consisted of 82 patients (mean age 66 years, 51% female, 26% with prior fusion) with a median follow-up of 2 years (IQR 28–104 weeks). A total of 50 patients (61%) had ≥ 2-year follow-up. Each surgery involved an average of 4 posterior column osteotomies and 8 segments. Iliac accessory rods were cobalt chromium and were placed bilaterally in 87% of constructs. Postoperative alignment improved significantly in the following parameters: maximum coronal Cobb angle, fractional curve, sagittal vertical axis, lumbar lordosis, thoracic kyphosis, and pelvic incidence to lumbar lordosis mismatch (p < 0.001 for all comparisons). Of 50 patients with ≥ 2-year follow-up, rod fracture occurred in 1 (2.0%), which was incidentally found and required no intervention. The present rod fracture rate was significantly lower than the authors’ historically reported institutional rate of 21% for traditional dual-rod constructs, and the 11%–14% reported in recent prospective multicenter studies that used traditional and supplemental rod constructs (p < 0.05 for all comparisons). Reoperation occurred in 12 patients (14.6%); 7 (8.5%) for proximal junctional kyphosis and 5 (6.1%) for wound complication.

CONCLUSIONS

Here the authors describe their experience with a novel iliac accessory rod technique to prevent rod fracture in patients undergoing surgery for ASD. The 2-year rod fracture rate (2.0%) in this study is significantly lower than the authors’ historical dual-rod fracture rate, and other prospective multicenter investigations. Future studies with longer follow-up are needed to determine the durability of this technique.
Aplikasi Media Pembelajaran Pendidikan Agama Islam Mengggunakan Card Sord
Authors: Riyadi Tamrin Ahmad , Hastuty Ade
Published: July 2024

Islamic religious education is an important aspect of forming students' character and morals, including at the early childhood education level. In the current digital era, the use of technology can be an effective means of learning Islamic religious education that is interactive and attracts attention. The aim of this research is to create a card sort application to introduce hijaiyah letters to young children. This research method uses quantitative research using the PHP programming language. The research was conducted for three months at the Pilot State Early Childhood Education School, Sidenreng Rappang Regency. The results of the research show that the learning process using card sort can introduce material about recognizing hijaiyah letters and punctuation and how to pronounce letters correctly in video form, making it easier for children to understand letter material.
Identifikasi Plat Kendaraan Berbasis Aplikasi Python
Authors: Putra Rezki Dwi , Suwardoyo Untung
Published: July 2024

Detecting motor vehicle plates is currently manual and does not utilize technology to determine the identity of vehicle ownership. This research aims to test an application that can identify the identity of vehicle ownership. The type of research used is descriptive, using a Python-based programming application. The research results show that the plate detection accuracy rate is 80% for reading objects at a distance of 10 cm to 40 cm.
Metode Clustering K-Means Pada Penjualan Handphone
Authors: Risnayanti Risnayanti , Mughaffir Mughaffir
Published: July 2024

Cell phone sales are an important part of the fast-growing technology industry. With the diversity of models and increasing consumer preferences, careful market analysis is becoming increasingly important. The aim of this research is to find relevant market segments and purchasing patterns. The research method uses K-Means clustering analysis by combining sales data with various attributes, such as brand, price, and technical specifications. The research was conducted for  months at a mobile phone store in Pinrang city. The results of the research found that the K-Means clustering method managed to identify different purchases and market segments in mobile phone sales because it could provide specific features, such as preference for a particular brand, lower prices, or specific technical features to improve marketing and consumer satisfaction.