bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Panic Disorder Classification Using SMOTE and Random Forest
Authors: Nurmalasari Dini , Yuliantoro Heri R , Qudsi Dini Hidayatul
Published: December 2024

Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life. Early detection and intervention are crucial for managing panic disorder and improving the well-being of those affected. Technology plays a pivotal role in facilitating early detection through data-driven approaches that employ algorithms to identify patterns of behavior or symptoms associated with panic disorder. Accurate classification of panic disorder is crucial for effective diagnosis and treatment. However, machine learning models trained on imbalanced datasets, such as those containing panic disorder patients, are prone to overfitting, leading to poor generalization performance. This study investigates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing overfitting in panic disorder dataset classification using the Random Forest algorithm. The results demonstrate that SMOTE significantly improves the classification performance of Random Forest. By mitigating overfitting and improving generalization to unseen data, SMOTE increases accuracy by 15 percentage points. Before using SMOTE, the accuracy was 82%, and after using SMOTE it is 97%. The findings underscore the promise of SMOTE as a tool for boosting the performance of machine learning algorithms in classifying panic disorder from imbalanced data.
Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights
Authors: Kurniawan Taufik , Hermawanti Laily , Safriandono Achmad Nuruddin
Published: December 2024

Kanker paru-paru tetap menjadi salah satu penyebab kematian utama di seluruh dunia, dan deteksi dini melalui metode yang akurat dan andal sangat penting untuk meningkatkan prognosis pasien. Studi ini mengusulkan model klasifikasi kanker paru-paru yang mengintegrasikan XGBoost dengan SHapley Additive exPlanations (SHAP) dan teknik Random Over Sampling (ROS) untuk mengatasi masalah ketidakseimbangan data. Dengan menggunakan optimasi hiperparameter melalui Optuna, model yang dihasilkan menunjukkan kinerja yang unggul, dengan akurasi rata-rata 96,84%, presisi 99,23%, recall 94,51%, skor F1 96,74%, spesifisitas 99,17%, dan AUC 96,84% dalam evaluasi validasi silang 10 kali lipat. Analisis SHAP memberikan interpretabilitas yang signifikan, mengidentifikasi fitur-fitur utama seperti jenis kelamin, kebiasaan merokok, dan tanda-tanda fisik jari kuning sebagai faktor-faktor yang paling memengaruhi prediksi model. Hasil penelitian ini menunjukkan bahwa model yang diusulkan tidak hanya akurat, tetapi juga dapat ditafsirkan, memberikan kontribusi signifikan untuk mendukung pengambilan keputusan klinis yang lebih baik dalam diagnosis kanker paru-paru.
Betta Fish Identification System Based On Convolutional Neural Network
Authors: Saputra Gilang Ardhi , Agastya I Made Artha
Published: December 2024

This study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data.
Text Data Security Using LCG and CBC with Steganography Technique on Digital Image
Authors: Wildan Muhammad , Ashari Wahid Miftahul
Published: December 2024

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.
Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies
Authors: . Rezki , Lapatta Nouval Trezandy , Ardiansyah Rizka , . Wirdayanti , Angreni Dwi Shinta
Published: December 2024

This study aims to segment donors using the Recency, Frequency, Monetary (RFM) model and the K-Means algorithm to optimize fundraising strategies. The RFM model is used to measure donor engagement through three dimensions: Recency (the last time a donation was made), Frequency (the frequency of donations), and Monetary (the amount of donations). By utilizing RFM scores, donors are then grouped using the K-means algorithm to generate more specific donor segments. This study was conducted using donation data from a non-profit organization, focusing on strategies to improve donor loyalty and donation frequency. The segmentation results identified several key segments, including Loyal Donors, New Donors, Potential Donors, and Low-Priority Donors. Each segment exhibits different donation behavior characteristics and requires a different strategic approach. The implementation of these segmentation results is expected to help the organization design more effective communication strategies and donation programs, as well as improve donor retention and lifetime value. Additionally, this study identifies the potential for enhancing the analytical model for broader applications in the future. This research contributes to non-profit organizations by offering a more efficient approach to managing donor relationships.
Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome
Authors: Pranatha Made Doddy Adi , Setiawan Gede Herdian , Maricar M Azman
Published: December 2024

Classifying the faces of individuals with Down Syndrome poses a significant challenge in image processing and genetic anomaly detection. This study leverages the ResNet34 architecture and transfer learning methods to improve classification accuracy for Down Syndrome facial recognition. Three experiments were conducted, varying the batch size, learning rate, and number of epochs. In the first experiment, the model achieved an accuracy of 82.83%, precision of 0.8362, recall of 0.8350, and an F1 score of 0.8348, showing promising performance but falling short of the target accuracy of 85%. The second experiment yielded the best results, with an accuracy of 87.88%, precision of 0.8956, recall of 0.8956, and an F1 score of 0.8956, indicating an optimal balance between correct predictions and errors. The third experiment resulted in the lowest accuracy, at 80.47%, with a precision of 0.8272, recall of 0.8249, and an F1 score of 0.8247, signifying a decline in performance compared to the other trials. Among the three experiments, the best configuration was achieved in the second trial, as the high recall value is crucial in medical contexts to ensure that as many individuals with Down Syndrome are correctly detected as possible, minimizing the risk of serious consequences due to false negatives.
Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset
Authors: Sabella Cindy Dinda , Pristyanto Yoga
Published: December 2024

Air pollution is an urgent global environmental problem, with significant impacts on public health and ecosystem stability. This research aims to develop an air quality classification model using the Global Air Pollution dataset from Kaggle, which consists of 23,463 rows of data and 12 features, including important variables such as Air Quality Index (AQI), PM2.5, NO2, and O3. Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. The SVM model showed an improvement of 94.89% to 99.32%, while Random Forest recorded an accuracy of 96.87% with no significant improvement after tuning. Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. It is hoped that the integration of this model into a real-time air quality monitoring system can help make more responsive and precise decisions in dealing with air pollution problems.
Implementation of the Naive Bayes Classifier Algorithm for Classifying Toddler Nutritional Status
Authors: Kamil Muhammad Insan , Wibowo Adityo Permana
Published: December 2024

This research addresses the pressing issue of malnutrition among toddlers in Indonesia, aiming to classify their nutritional status using the Naive Bayes Classifier (NBC). The study utilizes a dataset comprising 958 records from Puskesmas Cilandak and categorizes nutritional status into six class labels: good nutrition, at risk of excess nutrition, excess nutrition, obesity, undernutrition, and severe malnutrition. The methodology includes data preprocessing techniques such as class weighting to tackle class imbalance and Principal Component Analysis (PCA) for effective feature extraction. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, achieving an impressive accuracy of 85.76% when class weighting is applied, which significantly enhances the recall and F1 scores for minority classes. The findings highlight the critical importance of robust preprocessing and evaluation metrics in improving machine learning models for public health applications. Furthermore, they suggest that further exploration of alternative algorithms and dataset expansion could yield more comprehensive insights into the classification of toddler nutritional status.
Implementation of MobileNet Architecture for Skin Cancer Disease Classification
Authors: Faudyta Haniifa Aliila , Sinaga Jesica Trivena , Subhiyakto Egia Rosi
Published: December 2024

As the number of occurrences of skin cancer increases year, it becomes more and more crucial to identify the disease accurately and effectively. This study aims to implement and evaluate the MobileNet architecture for classifying nine types of skin lesions using the ISIC 2020 dataset and to compare MobileNet's performance with other CNN architectures, such as VGG-16 and LeNet, in terms of accuracy and computational efficiency. The study also investigates the impact of image preprocessing on model accuracy. The methodology comprises data collection, preprocessing, and model development, leveraging transfer learning from MobileNet pre-trained on ImageNet. Data preprocessing involves resizing images to 224 x 224 pixels and normalizing pixel values. To augment the dataset, techniques such as rotation, zooming, horizontal flipping, and brightness and contrast adjustment are applied. To address class imbalance, oversampling is used to obtain 500 images per class. The model architecture includes Global Average Pooling, a Dense layer with 1024 units and ReLU activation, and a Dropout layer with a 0.2 probability. Various training scenarios with batch sizes (8, 16, 32, 64) and learning rates (0.001, 0.0001) are evaluated, incorporating dropout and ReLU activations. The optimal performance was achieved with oversampling, dropout, and a learning rate of 0.0001, yielding a training accuracy of 99.64% and a validation accuracy of 86.89% after oversampling, resulting in 3,600 training and 900 validation images with an 80:20 data split. The results suggest overfitting due to dataset limitations. Future work should focus on fine-tuning and ensemble methods to improve validation performance.
Linear phased array of capacitive feed printed dipole antennas for 5G mm-wave communication systems
Authors:   Southern federal university 347922, Russia, Taganrog, Nekrasov line, 44, Gevorkyan A.V.
Published: December 2024

The paper presents the design of a previously developed capacitive feed printed dipole array element. A comparison of its frequency characteristics of the VSWR as part of an infinite antenna array and as part of a 4 × 4 broadside antenna array is shown. In the frequency range from 25 to 26 GHz, the VSWR of the infinite array element did not exceed 1.3, and for the finite array does not exceed 1.6 (for the central elements is 1.5)). The aim of this work was to develop a linear phased array antenna based on a previously developed element and to evaluate the stability of its VSWR frequency characteristics. An 8-element linear phased array with scanning in the H-plane in the ±45° angle sector was developed. All but the edge elements have good repeatability of the characteristics (the spread of values does not exceed 0.10 for the VSWR ≤ 1.5 and 0.15 for the other values of the VSWR). At the same time, in the range from 25 to 26 GHz, in the broadside, the values of VSWR ≤ 1.42 and VSWR ≤ 2.06 are at a scanning angle equal to ± 45 °. The isolation between array elements is no more than -17.5 dB.
On the Propagation of Piecewise Holomorphic Signals Through a Cosmic Maser
Authors:   Samara State Technical University 443100, Russia, Samara, Molodogvardeyskaya str., 244, Bukhman N.S.
Published: December 2024

The propagation of piecewise holomorphic (discontinuous) signals through the region of selective (frequency) amplification (cosmic maser) has been studied. It is shown that in this case, an amplified signal is excited at the maser radiation frequency compared to the original one, even in the case when the carrier frequency of the piecewise holomorphic signal differs by several orders of magnitude from the maser radiation frequency. Typical signal intensity gain values for a typical OH maser are about 40 dB. This makes it possible to consider space masers as natural "detectors" of radio and video signals of artificial origin.
Competencias emocionales en universitarios: un estudio de teoría fundamentada
Authors: Gutiérrez Cervantes Juan Carlos , Lozano Rodríguez Armando , Mercado Varela Martín Alonso
Published: December 2024

El presente estudio tuvo como objetivo analizar las prácticas de competencias emocionales (ce) en estudiantes universitarios con el fin de comprender a profundidad el funcionamiento de este fenómeno. La muestra se integró por 39 estudiantes universitarios del sur de Sonora. Mediante entrevistas semiestructuradas con final abierto, grupo focal y técnica de validación de los encuestados (member checking), se realizaron procesos de codificación simple, axial y selectiva. Emergieron seis categorías: fisiología de las emociones, racionalización de las emociones, autorregulación, aceptación de las emociones, empatía y autonomía. Se concluyó que las prácticas de ce se reflejan en todos los ámbitos de la persona.
Violencias contra las mujeres y su derecho a la protección internacional en México
Authors: Pérez Vera Amarilis
Published: December 2024

El objetivo de este artículo es mostrar cómo las diferentes violencias que se ejercen contra las mujeres migrantes en busca de refugio podrían obstaculizar de manera parcial o definitiva su acceso a la protección internacional en México. Se presenta un caso emblemático; el impacto de la pandemia de covid-19 en el retroceso de los derechos de las mujeres, y el ámbito normativo vigente en México para garantizar que las mujeres migrantes y refugiadas accedan a los derechos humanos, la justicia y la equidad. Finalmente, se exponen algunas de las dificultades que todavía enfrentan específicamente las mujeres refugiadas y se mencionan algunas propuestas para mitigar este grave problema.
Actitud ambiental en estudiantes universitarios: un estudio con enfoque de género
Authors: Vargas Ramos Catalina , Durán Badillo Tirso
Published: December 2024

El objetivo de este artículo es describir las actitudes ambientales y establecer las diferencias por género en estudiantes de la Licenciatura en Seguridad, Salud y Medio Ambiente. En un estudio descriptivo y transversal en 81 estudiantes se aplicó la escala de actitudes hacia temas ambientales. La muestra estudiada presentó una actitud negativa en la facilitación, la energía y el transporte. En el análisis por género las mujeres mostraron una mayor actitud positiva en la escala global y en las dimensiones de valoración, energías, reciclaje y contaminación. Se recomienda el análisis de las políticas públicas y de la disposición de los recursos económicos para facilitar el cuidado ambiental.
Optimización y su relación con la logística
Authors: Peralta-Abarca Jesús del Carmen , Moreno Bernal Pedro , Bonilla Sánchez Felipe de Jesús , Hernández Aguilar José Alberto
Published: December 2024

La logística es un área crucial en el transporte y distribución de bienes y productos. Los modelos de ruteo vehicular aplicados en logística son una herramienta esencial para mejorar la eficiencia y reducir los costos operativos en la distribución de productos. Los modelos de optimización se basan en el problema de ruteo vehicular, el cual puede abordarse mediante algoritmos computacionales que permiten considerar factores como la capacidad de los vehículos, la distancia entre los puntos de partida y de entrega, los tiempos de espera, y las restricciones de los conductores, los clientes y los vehículos. Asimismo, permiten considerar la trazabilidad de los productos, ayudando a las empresas y a los clientes a monitorear el movimiento de los productos desde su origen hasta su destino final.
Análisis del rendimiento en Matemáticas I en preparatorias federalizadas de Ciudad Victoria, Tamaulipas
Authors: Boeta González Leopoldo Jesús , Boeta González Susana
Published: December 2024

El desarrollo de competencias en matemáticas ha tenido modificaciones a lo largo del tiempo, por lo que es necesario realizar cambios y adaptaciones relacionadas con el sistema educativo, estudiantes, contexto y profesores. El estudio aquí presentado se realizó con el objetivo de explicar la relación entre el rendimiento académico y el interés en la asignatura de Matemáticas I, con una metodología cuantitativa por medio de estudios de casos y mediante el análisis de los sujetos de las tres preparatorias federalizadas de Ciudad Victoria, Tamaulipas, para obtener así datos contextualizados y de validez científica. Los resultados brindaron información sobre las áreas de oportunidad institucionales, que facilitarían al estudiante el aprendizaje, su tránsito académico y su desarrollo posterior.
War, Displacement, and Birth Spacing: An Analysis of the Syrian Refugees in Lebanon
Authors:   Asia-Europe Perspective Association, Kim Donghui , Go Sun
Published: December 2024

We analyze the relationship between population displacement and childbirth, focusing on the forced displacement by war. The micro data from the 2015-2016 Surveys of Syrian Refugees and Host Communities in Lebanon by World Bank are used for the survival analysis of birth spacing between children within household. If displacement delays a migrant’s childbirth, the birth interval will be extended. If displacement leads to greater fertility, the birth interval will be shortened.
Birth spacing of Syrian refugees and non-refugees in Lebanon are compared before and after the year of migration using a difference-in-differences type specification in the survival analysis. Our results show that the birth interval of Syrian refugees increased after displacement. This is consistent with the disruption hypothesis that migrants tend to delay childbirth right after migration when they face new environment and challenges. When the years since displacement, rather than the post-displacement indicator, is used in the survival model specification, refugees’ birth spacing further increased as the years since displacement grow. This result is different from previous findings in the literature about the disruption hypothesis that delayed childbirth is compensated by greater fertility in the long run.
Several challenges of this research still need attention and follow-up research. The Syrian Civil War had not ended during the survey period, and refugees were still in great uncertainty and economic distress. If refugees move to developed countries and settle in the better environment, the refugees may show a compensation behavior of increasing fertility. The polygamy in the Middle East, increased divorce, separation, and death of the spouse by war, out-of-wedlock births, adoption may also have affected the analysis and caused a bias in the estimation result. If infant mortality increased during the war, there could be a further bias in the result. More detailed data are required for addressing such issues and identifying the causal link between displacement and fertility.
Comparison of Perceptions of Face-to-Face and Untact Classes according to College Student Commuting Hours
Authors:   Asia-Europe Perspective Association, Eom Sanghyun
Published: December 2024

The importance of Untact instruction is being emphasized due to COVID-19 and a decrease in the school-age population. This paper investigated learners' common perceptions of face-to-face and Untact instruction, and analyzed the difference in perceptions between school hours and the two teaching methods.
In order to understand learners' perceptions of face-to-face and Untact classes, 339 responses were analyzed by subject classification. As a result of the content analysis, the perception of face-to-face and Untact classes was classified into ‘Economics’, ‘Interaction’, ‘Self management’, ‘Learning effectiveness’, ‘Learning method’ ‘Learning environment’ and 'none'. As a result of frequency analysis on content analysis, interaction and learning effects were found to be remarkable in face-to-face classes, and Untact classes were found to be economical.
In order to compare the difference in perception of face-to-face and non-face-to-face classes according to school hours, chi-Square verification and Bonferroni post-hoc were performed. As a result of the analysis, there were statistically significant differences in economic feasibility among the factors of face-to-face class shortcomings and the advantages of Untact class. According to the interview results, although face-to-face classes feel superior to Untact classes in terms of interaction and learning effects, students with long commuting hours prefer Untact classes due to economic factors.
The limitation of this study is that due to the geographical isolation of the university campuses, the perception of face-to-face and Untact is evident and Since two teaching methods were applied in one semester, it is a short experimental period to understand the characteristics of each teaching method.
Finally, in a rapidly changing era, it was suggested to look back on past education and improve it as a way to apply it to modern society.
Counter-Scapegoating Inoculation: Lasting Effects on Manipulation Resistance, Trustworthiness Perception, and Sharing Intentions
Authors: Orosz Gabor , Faragó Laura , Salamon Janos , Goldberg Beth , Roozenbeek Jon , Krekó Péter
Published: December 2024

A key feature of successful vaccines is their ability to confer long-term protection, a principle equally relevant to counter-misinformation and counter-manipulation strategies. Balancing scalability with enduring effects is critical. This study tested a six-minute intervention comprising a one-minute inoculation video framed by a definition, a testimonial, and a saying-is-believing exercise to build resistance against scapegoating manipulations. The intervention improved scapegoating recognition, reduced trust in related content, and decreased sharing intentions one month later. This approach offers a framework for designing inoculation strategies to enhance their long-term impact.
Guidelines for implementing ChatGPT in writing academic research papers
Authors: O’Grady Laura
Published: December 2024

This article provides a historical context on the time-honoured tradition of writing academic research articles commencing at the undergraduate level through to the level of a faculty position by outlining the three generational shifts: first by searching for articles and writing the document by hand, second by using computers to aid in the process and the third using Artificial Intelligence (AI). AI tools such as ChatGPT are currently being used by students to write papers for course work instead of conducting the assignment on their own. Outlined are ways in which academics and educators can work with AI by integrated it into the teaching process using it as an instructional tool, applications in academic publishing and how it can aid the peer-review process
How Might Interoceptive Accuracy Training Work?
Authors: Murphy Jennifer , Bird Geoff
Published: December 2024

With growing interest in interoceptive training to enhance the perception of internal bodily signals, there is a need to consider the mechanisms by which training may improve performance on tests of interoceptive accuracy (i.e., tests designed to measure how well signals from the body can be perceived). In this brief paper we use the example of cardiac interoceptive accuracy training to outline several possible mechanisms by which such training may result in improvement on tests of cardiac interoceptive accuracy. We show that under many of these mechanisms, evidence of improvement on tasks that claim to measure cardiac interoceptive accuracy does not reflect improvement in the perception of cardiac signals. We provide several recommendations to mitigate the potential influence of factors unrelated to interoceptive accuracy, enabling it to be determined that an improvement in interoceptive accuracy has occurred following training.
Dissociating the Ethical/unethical, Pro/anti-organizational Sensitivities and Overall Action/inaction Preference of Unethical Pro-organizational Behavior
Authors: Liu Chuanjun , Zou Lemei
Published: December 2024

Previous studies have highlighted the dilemmatic nature of UPB, characterized by paradoxical emotional responses and behavioral patterns. Underlying the paradox of UPB, there are three different psychological processes, Ethical/unethical sensitivity (E parameter), Pro-/anti-organizational sensitivity (P parameter) and overall Action/inaction preference (A parameter). A typical UPB can be attributed to lower E, higher P, higher A, or any combination of these factors. However, no existing measures effectively dissociate the three psychological processes. Present work addresses this gap by developing and validating a novel measure, the EPA model by applying the methodology in moral dilemma study. The structural scenarios were constructed and validated (Study 1, n=749). Validity and reliability of the EPA model were further tested (Study 2, n=150). With applying the EPA model in a multi-wave survey (Study 3, n=1097), we found that employees with stronger tendencies toward moral decoupling, disengagement and justification are more likely to engage in UPB. It can be explained by their lower ethical sensitivity, higher pro-organizational sensitivity, and higher overall action/inaction preference. The relationships between organizational identification, social exchange, future focus and UPB remain ambiguous across multiple methodologies, warranting further investigation. Future studies can use the EPA model to clarify the three psychological processes of UPB. Theoretical and methodological implications were discussed.
Nutrifii™ Vinali® protects type 1 collagen against methylglyoxal-induced damage
Authors: West Brett
Published: December 2024

Reactive dicarbonyls, such a methylglyoxal, are byproducts of sugar metabolism and can damage tissue via interactions with biomolecules. Type 1 collagen is a target of methylglyoxal attack. As this type of collagen constitutes a large portion of the supporting collagen in the human body, glycation by reactive dicarbonyls can result in long-term adverse health effects. Dietary factors may help mitigate this by scavenging dicarbonyls. To evaluate the potential of a dietary supplement, Nutrifii™ Vinali®, to protect type 1 collagen, an in vitro methylglyoxal scavenging test was performed. This supplement was able to prevent glycation at approximately the same degree as aminoguanidine, a positive anti-glycation control. Some of this activity may be due, at least in part, to the presence of bioflavonoids, such as hesperidin, that are potent dicarbonyls scavengers. The results of this study suggest that Vinali may be helpful in providing long-term protection of type 1 collagen within the body and, consequently, maintain the health of major organs in the body including the skin.
Reconceptualizing Authorship and Accountability in the Age of AI: A Legal-based Theoretical Framework for AI-Generated and Mediated Communication
Authors: Towne Bryce Petofi
Published: December 2024

The emergence of advanced artificial intelligence (AI), including large language models (LLMs) and multimodal generative AI, has transformed human communication by enabling the autonomous creation of original content of the information/message(s). This shift challenges traditional notions of authorship, originality, intellectual property (IP) rights, and legal accountability, which have been predominantly human-centric. This paper presents a legally grounded framework categorizing AI’s roles in communication into three types: AI-Generated Communication (AIGC), Human Artificial Intelligence Communication (HAIC), and Artificial Intelligence-Mediated Communication (AI-MC). Drawing on legal principles from the U.S. Copyright Act, the Berne Convention, and international laws, the framework assigns responsibilities and accountability to human stakeholders such as users, developers, and operators, addressing legal uncertainties surrounding AI-generated content of the information/message(s).Through a literature review, the study identifies deficiencies in existing models, highlighting the need for integrating legal and ethical considerations into AI communication roles. The proposed framework emphasizes transparency and human oversight, ensuring ethical AI deployment across various domains including education, healthcare, law, politics, finance, and social media. By adopting a spectrum-based approach to autonomy and originality, the framework aims to provide a nuanced understanding of AI’s contributions, potentially offering a robust tool for policymakers and practitioners to ensure AI-driven communication is legally compliant and ethically responsible.
Empowering or Disempowering? The Impact of Coach-created Motivational Climates on Engagement and Team Cohesion in Rhythmic Gymnastics
Authors: Laroëre Bianca Maria , Horová Eliška , Mudrak Jiri , Crossan William , Třebický Vít
Published: December 2024

Rhythmic gymnastics is a complex aesthetic sport in which coaches’ approach to athletes plays a crucial part. Despite coaches’ essential role in shaping athletes’ experience, the fragmentary research on the coach-created psychosocial climate in this sport limits our understanding of how athletes perceive the climate. This study investigates rhythmic gymnasts' perceptions of coach-created motivational climate and its relationships with athletes’ motivational and social outcomes. Eighty-eight rhythmic gymnasts completed an online battery of standardised questionnaires related to engagement, team cohesion, and the five dimensions of the perceived coaching climate. Autonomy-supportive and task-involving climates were key positive factors of athletes' engagement and team cohesion, both explaining over 44% of the observed variance. K-means cluster analysis revealed two participant types: "empowered" and "disempowered"; the first one accounting for 65% of our participants. The "empowered" participants reported highly perceived task-involving, autonomy-supportive, socially supportive, and low ego-involving and controlling climates, whereas the “disempowered” gymnasts reported the opposite. Furthermore, "empowered" gymnasts exhibited higher engagement and team cohesion compared to “disempowered”. These results are in line with previous findings and suggest that an empowering coach-created motivational climate may be an important factor in motivational and social outcomes among competitive athletes. Emphasising athletes' autonomy and task orientation while minimising the ego-involving climate and control might enhance athletes’ positive sports experience.
Decoding Ransomware Behavior Through Algorithmic Pattern Recognition for Classification
Authors: Akana Eric , Braithwaite Alistair , Blakeney Mathilda , Fothergill Florence , MacFarland George
Published: December 2024

The study presents a novel approach to ransomware classification through entropy-based behavioral analysis, offering a robust and scalable framework for identifying complex ransomware families. Behavioral Pattern Entropy Mapping, the central methodology, focuses on extracting and clustering entropy-driven features from ransomware operations, enabling accurate classification without relying on traditional signature-based methods. By analyzing intrinsic operational characteristics, the framework effectively differentiates between polymorphic and metamorphic ransomware variants such as LockBit and BlackCat, which are known for evading conventional detection techniques. The method demonstrated high accuracy and adaptability across various datasets, with consistent clustering results validated through statistical metrics. Dimensionality reduction techniques, including principal component analysis, optimized computational efficiency while preserving the integrity of critical behavioral features. Analysis of entropy thresholds highlighted a balance between classification sensitivity and specificity, addressing trade-offs through improved clustering consistency. The automated feature extraction pipeline eliminated human dependencies, ensuring unbiased and reproducible classification outcomes. Memory usage, execution patterns, and network traffic anomalies were examined, providing deeper insights into ransomware behaviors and operational distinctions among families. Experimental results confirmed the framework’s ability to scale efficiently while maintaining high performance across diverse datasets. The methodology fills significant gaps in existing approaches, emphasizing adaptability and precision in responding to evolving ransomware threats. Through a focus on entropy as a core metric, it offers a reliable solution for addressing the challenges of modern cybersecurity landscapes.
Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond (Second Volume)
Authors: Fujita Takaaki , Smarandache Florentin
Published: December 2024

The second volume of "Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization" presents significant advancements in uncertain combinatorics through methodologies such as graphization and hyperization. It seamlessly integrates fuzzy, neutrosophic, soft, and rough set theories with combinatorics and graph theory to effectively model and manage uncertainties. This integration facilitates the exploration of novel structures, including hypergraphs and superhypergraphs, breaking new ground in both theoretical and practical applications across mathematical and real-world systems. The chapters span a diverse range of topics, beginning with an in-depth analysis of relationships among graph types such as fuzzy, neutrosophic, and plithogenic graphs, with a focus on their applications. The work also investigates advanced graph structures, including directed and bidirected superhypergraphs as well as soft and rough superhypergraphs, emphasizing their role in managing uncertainty. Computational challenges, such as partitioning and optimization in superhypergraphs, are addressed, with classical problems like the Traveling Salesman Problem adapted to this new context. Algorithmic approaches specific to Plithogenic Graphs are introduced, addressing complexity and validity. The volume further examines "bunch graphs," where nodes represent groups rather than individual entities, offering a robust model for collaboration and competition. Concepts like even-hole-free and planar graphs are extended to uncertain frameworks, exploring their relevance to uncertainty modeling. Additionally, new graph classes such as Turiyam Neutrosophic Soft Graphs and General Plithogenic Soft Graphs are introduced, tailored for handling uncertainty in diverse applications. The volume concludes with a comprehensive survey of trees, forests, and paths in fuzzy and neutrosophic contexts, providing valuable classifications and insights into graph structures under uncertainty. Overall, this work serves as a foundational framework for advancing uncertain combinatorics, setting the stage for future research and practical applications.
Ni-Co-O anodes for the alkaline oxygen evolution reaction: Multistage electrode optimization and plasma-assisted activity enhancement enabled by a coherent workflow
Authors: Vinayakumar Vineetha , Wagner Timo , Marcks Christian , Johny Jacob , Wartner Garlef , F. Tesch Marc , Spanos Ioannis , Ghafari Aliakbar , Jain Adarsh , Prymak Oleg , Sanjuán Ignacio , Suhail Odungat Ahammed , Anwar Osama , Chatwani Mohit , Jose Aneeta , Chanda Vimanshu , Knop-Gericke Axel , Andronescu Corina , K Mechler Anna , Wöhrl Nicolas , Segets Doris
Published: December 2024

Ni-Co-based oxides (Ni-Co-O) are promising oxygen evolution reaction (OER) catalysts due to their adaptable structures, affordability, abundance, and strong electrochemical performance. While various studies explored the structure-activity relationships and OER mechanisms in these catalysts, comprehensive anode development requires a systematic workflow covering all stages of catalyst optimization. Here, we present an integrated approach for characterizing commercial Ni-Co-O nanomaterials using a combination of analytics during processing, in operando, and post-catalysis to gain insights into parameters influencing anode performance. Key findings include the critical impact of catalyst ink optimization through solvent matrix screening, enabling high-quality electrode layers via ultrasonic spray coating on Ni plates. These Ni-Co-O layers, tested as anodes in alkaline OER, were further enhanced by plasma post-processing, resulting in binder-free surfaces with increased porosity and wettability compared to untreated samples. Notably, plasma treatment also promoted a more redox reversible catalyst structure, improved Fe uptake efficiency, and led to a significantly enhanced OER activity by reducing the overpotential by ~43 mV at 100 mA/cm² compared to untreated samples. Our study highlights the importance of a holistic evaluation approach, from catalyst powder to post-mortem analysis, that enabled us to discover how plasma post-processing can yield binder-free electrodes with superior electrochemical properties.
Self-Efficacy is Key: Examining the Role of Motivation to Engage in Healthy Lifestyle Behaviors for Dementia Prevention in Midlife
Authors: Simone Stephanie , Kaplan Marina , Giovannetti Tania
Published: December 2024

Modifiable risk factors account for nearly half of dementia cases, with the greatest impact on dementia prevention in midlife (ages 45-64). Little is known about what motivates middle-aged adults to engage in healthy behaviors for dementia risk reduction. This study examined associations between motivation to make lifestyle changes for dementia risk reduction and engagement in health behaviors associated with dementia risk in 347 middle-aged adults. Multivariate linear regressions examined associations between motivation and engagement in health behaviors. Greater self-efficacy and higher education significantly predicted greater physical and cognitive activity and better sleep quality. Greater perceived barriers and general health motivation, lower self-efficacy, and younger age significantly predicted greater perceived loneliness. Self-efficacy consistently predicted engagement in healthy behaviors associated with dementia risk reduction in midlife. Thus, incorporating empirically supported strategies to increase self-efficacy in lifestyle interventions for dementia prevention may increase long-term adherence and overall success of dementia prevention efforts.
Normative Extended High Frequency Audiometry in Young, Middle, and Older Populations
Authors: Dorey Claire
Published: December 2024

Objectives: Elevated thresholds in the extended high frequency hearing range have been associated with functional hearing deficits, such as speech-in-noise performance. However, unlike the standard frequency range, there are a lack of recommendations from organizations such as the International Standards Organization (ISO) and World Health Organization (WHO) regarding normative data in the extended high frequencies limiting clinical implications of this data. In the present study, we report proposed normative data in the extended high frequency region in over 726 participants evaluated on typical clinical equipment using methodology from both the ISO and WHO. We hypothesize that extended high frequency will worsen with age and show excellent test-retest reliability. Design: Audiometric thresholds were collected from 726 adult participants at all measurable frequencies between .25 and 16 kHz. Data were analyzed using the equations reported by the 2017 ISO 7029 report and by calculating a 4-frequency extended high frequency pure tone average in alignment with the WHO. Results: Males had worse hearing than females when analyzed using a 4-frequency PTA, but there was no significant difference in the age-related rate of decline. Extended high frequency thresholds (N = 129) showed excellent test-retest reliability (ICC > 0.9, p < 0.001). Extended high frequency 4-frequency PTAs in participants with normal hearing in the standard frequency range had a range of -11.25 to 92.5 dB.Conclusions: Adaptation of norms in the extended high frequency range is feasible due to agreement among the literature and stability of test-retest measurements. Male extended high frequency thresholds are worse than women, but the rate at which hearing declines does not differ between sexes. Future research in this area should take into account normative data and functional hearing performance.
Evolution of the Concept of Sensitivity and its Measurement: The Highly Sensitive Person Scale-Revised
Authors: Pluess Michael , Aron Elaine N. , Kähkönen Jenni Elise , Lionetti Francesca , Yuanyuan Huang , Tillman Teresa , Greven Corina , Aron Arthur
Published: December 2024

Sensitivity to environmental influences varies among individuals, with some more responsive to both negative and positive experiences. Individual differences in sensitivity are linked to psychological, physiological, and genetic factors and can be measured using the Highly Sensitive Person (HSP) scale. Over three decades, the conceptualization of sensitivity has shifted from a unidimensional to a multidimensional trait, necessitating a revision of the original scale. This study introduces and validates the revised HSP scale (HSP-R) using a UK and USA sample of 1,000 adults. Exploratory factor analysis on a calibration subsample (n = 600) yielded a 6-factor, 18-item structure, which was confirmed in a validation subsample (n = 400). The HSP-R measures six dimensions of sensitivity: Sensitivity to Details, Depth of Processing, Social Sensitivity, Sensitivity to Positive Experiences, Emotional Reactivity, and Overstimulation. It demonstrates robust psychometric properties, aligns with theoretical frameworks, and provides incremental validity beyond the Big Five traits.
Role of Teacher’s Feedback in Master’s Level Thesis Writing
Authors: Basaula Asmita
Published: December 2024

This study explores the pivotal role of teacher’s feedback and guidance in facilitating the accomplishment of thesis writing, identifying and analyzing common benefits or challenges faced by masters level students incorporating teacher feedback into their thesis writingprocess. This study is employed based on qualitative research principles, using a narrative inquiry approach. For this, I collected the data taking unstructured interview with the three purposively selected students, involved in Masters level thesis writing from TribhuvanUniversity (TU) affiliated universities. The collected information was thematically analyzed and interpreted using inductive approach of data analysis. The findings of the study reveal that the teacher feedback on the completion of the master’s thesis assumes many-sided traits. First, it greatly enhances the students’ academic writing skills by providing them with definite and workable guidelines on improving their writing skills. These include feedback on structure, coherence, argumentation, and proper use of academic language—all prime necessities for writing a good quality thesis. Second, teacher feedback enables knowledge acquisition through social interaction. This is usually followed by feedback discussion between the teacher and the learner, whereby learners can clarify their understanding of difficult concepts or get guidelines pertaining to the research methodology or even fine-tune ideas. Such interaction not only helps in the acquisition of knowledge but also in developing critical thinking skills. Thirdly, the research showed that teacher feedback begets self-invention—a process wherein learners began to believe more in themselves, and their sense of academic identity became stronger.This is significant in the context of writing a thesis, where independent research by students is expected, and they are supposed to add original ideas in the subject area. This research finally concludes that the relationship between the teacher and the student during the thesis writing process is, in itself, one of transformation. It is not only the quality of the thesis that is affected by this bond, but it has a telling effect on the overall academic and personal growth of the student. Teachers give constructive feedback, thus influencing the waya student approaches learning, research, and writing. Indeed, the findings of this study point to the importance of the nature of the communicative relationship between the teacher and student, and it is suggested that institutions be aware of the necessity of having a context that fosters positive, supportive relationships between students and their supervisors.