bluuebunny/crossref_metadata_2025 huggingface dataset:
Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning
Authors: Wang Zhixiang , Li Qingqing , Wang Yiran , Qian Linxue , Hu Xiangdong , Liu Dong
Published: February 2025

Objective: To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore the role of multifeature fusion.

Methods: Data from 137 breast cancer postoperative patients with new nodules from January 2016 to April 2024 were analyzed. Clinical, ultrasound, immunohistochemistry, and surgical features were combined. Multiple machine learning models, including support vector machine (SVM), random forest, gradient boosting, AdaBoost, and XGBoost, were trained and tested. Model performance was evaluated using stratified ten‐fold cross‐validation. Ablation experiments assessed the impact of different feature combinations on diagnostic performance.

Results: The SVM model performed best, with an AUC of 0.8664, an accuracy of 0.8099, a sensitivity of 0.565, and a specificity of 0.9267. Ablation experiments indicated that multifeature fusion significantly improved diagnostic performance, especially when combining clinical, ultrasound, immunohistochemistry, and surgical features. Gradient boosting and random forest models showed slightly inferior performance, while AdaBoost had balanced but lower effectiveness.

Conclusion: Machine learning, particularly the multifeature fusion SVM model, shows significant potential in diagnosing new nodules after breast cancer surgery. It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes, include multicenter data, and explore advanced algorithms to further enhance diagnostic performance.
Deterministic Role of FOXF2 in Organ-Specific Macrometastasis Transition
Authors: Bado Igor L.
Published: February 2025

The mechanisms contributing to metastasis tropism in breast cancer are far from being understood. The ability of cancer cells to adapt to new microenvironments plays a crucial role in that process. In this issue of Cancer Research, Jiang and colleagues explored the tumor-intrinsic differences that affect seeding and tissue colonization by focusing on two major sites of breast cancer metastasis: the lung and bone. They used genetic approaches and metastasis modeling to assess the role of forkhead box F2 (FOXF2) in remodeling the tumor microenvironment and promoting metastasis outgrowth. Their findings suggest that FOXF2 promotes a micrometastasis-to-macrometastasis transition in bones through the direct activation of NF-κB and BMP4 signaling pathways, independently of tumor subtype. In contrast, FOXF2 was found to hinder the ability of basal models to colonize the lung microenvironment through inhibition of TGFβ signaling. Collectively, these findings underscore the subtype-specific and tissue-dependent roles of FOXF2 in organotropism.

See related article by Jiang et al., p. 644
Figure 7 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Tumor growth was better controlled when CAN-2409 + prodrug treatment was combined with anti–CTLA-4 antibody therapy compared with CAN-2409 + prodrug alone. A, MC38 tumor–bearing mice were treated with or without CAN-2409 i.t. at day 10, followed by 4 days of i.p. administration of prodrug. Anti–CTLA-4 or isotype control were treated i.p. on day 10, 13, and 16. Tumor growth was monitored, and tumors were collected on day 17. B, Tumor weight quantification (left) and tumor growth inhibition rate (right) at study endpoint. Quantification of CD3+ cells (C), CD8+ T cells (D), CD4+ T cell (E) and Treg (F) by flow cytometry. G and H, Characterization of TAA-specific CD8+ T cells for PD1+ (G), GZB+ (H), GZB+CX3CR1+ (I) and Ki67+ (J). K, MC38tumor–bearing mice were treated with or without CAN-2409 i.t. at day 8, followed by 3 days of i.p. administration of prodrug. Anti–CTLA-4 or isotype control was treated i.p. on day 8, 11, and 14. Tumor growth was monitored, and tumors were collected on day 18. Tumor growth inhibition after treatment was calculated. Quantification of Treg (L) and CX3CR1-Ki67+ CD8+ T cells (M) in tumor dLNs. N = 5–6 mice per group. One-way ANOVA with Tukey correction, *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.
Novel similarity measures for Fermatean fuzzy sets and its applications in image processing
Authors: Romisa Romisa , Vashist Shruti
Published: February 2025

Digital imaging is growing in our day-to-day life ranging from selfies to medical imaging. The extended applications of the field open doors for the researchers in the present-day context. The extraction of useful information from digital images is crucial because it depends on the various characteristics of the image. Fuzzy theory provides a better understanding of the image characteristics and, thus extracts meaningful information, even under uncertain situations. The present study reports the Fermatean fuzzy sets (FFSs) application in image processing while proposing similarity measures. These similarity measures highlight the perfect and precise results from an image while using multiple parameters of the image for information extraction. The study concludes that the proposed similarity measures provide a better estimation of data from an image used in image processing problems.
New family of error-correcting codes based on genetic algorithms
Authors: Bellfkih El Mehdi , Nouh Said , Chemseddine Idrissi Imrane , Louartiti Khalid , Mouline Jamal
Published: February 2025

This paper introduces a novel error-correcting code (ECC) construction and decoding approach utilizing genetic algorithms (GAs). Classical ECCs often struggle with efficiency in correcting multiple errors due to time-consuming matrix-based encoding and decoding processes. Our GA-based method optimizes generator vectors to maximize the minimum distance between codewords, enhancing error correction capabilities. Specifically, we construct a new family of ECCs with code length 31, dimension 12, and minimum distance 7, reducing complexity from O(kn) to O(k(n−k)) by encoding message blocks with vectors instead of matrices. In the decoding phase, the GA effectively corrects errors in received codewords. Experimental results show that at a signal-to-noise ratio (SNR) of 7.7 dB, our method achieves a bit error rate (BER) of 10−5 after only 9 generations of the GA. These results demonstrate improved error correction and decoding performance compared to traditional methods. This study contributes an innovative approach using GAs for error correction, offering simpler encoding and robust performance in coding schemes.
Recommender system for dengue prevention using machine learning
Authors: Kajornkasirat Siriwan , Hnusuwan Benjawan , Puttinaovarat Supattra , Puangsuwan Kritsada , Kaewsuwan Nawapon
Published: February 2025

The study aimed to develop a recommender system for dengue prevention using environmental factors and mosquito larvae data. Data were collected from 100 households in Surat Thani, Thailand using mosquito larval survey in January 2020. Data mining techniques: frequent pattern growth (FP-Growth) and Apriori algorithms were used to find association rules and to compare accuracies for selecting a suitable model. The recommender system was designed as a web application. FP-Growth is more suitable for these data than Apriori algorithm. The factors associated with dengue infection, including community area, densely populated area, and agricultural area. Most areas where mosquito larvae are found are community areas and agricultural areas. Aedes larvae were found most in water containers with dark colors and without a lid. Aedes larvae were also found in small water jars, large water jars, cement tanks, and plastic tanks. The recommender system should be useful to dengue vector prevention and to health service communities, in planning and operational activities.
Performance assessment of time series forecasting models for simple network management protocol-based hypervisor data
Authors: Afrianto Yuggo , Munadi Rendy , Setyorini Setyorini , Widyanto Toto
Published: February 2025

Time series forecasting is vital for predicting trends based on historical data, enabling businesses to optimize decisions and operations. This paper evaluates forecasting models for predicting trends in simple network management protocol (SNMP)-based hypervisor data, essential for resource allocation in cloud data centers. Addressing non-stationary data and dynamic workloads, we use PyCaret to compare classical models like autoregressive integrated moving average (ARIMA) with advanced methods such as auto ARIMA. We assess 30 models on metrics including CPU utilization, memory usage, and disk reads, using synthetic and real-time datasets. Results show the naive forecaster model excels in CPU and disk read predictions, achieving low root mean squared errors (RMSE) of 0.71 and 869,403.35 for monthly and daily datasets. For memory usage predictions, gradient boosting with conditional deseasonalisation and detrending outperforms others, recording the lowest RMSE of 679,917.6 and mean absolute scaled error (MASE) of 4.46 on weekly datasets. Gradient boosting consistently improves accuracy across metrics and datasets, especially for complex patterns with seasonality and trends. These findings suggest integrating gradient boosting and naive forecaster models into cloud system architectures can enhance service quality and operational efficiency through improved predictive accuracy and resource management.
Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques
Authors: Idouglid Lahcen , Tkatek Said , Elfayq Khalid
Published: February 2025

The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Enhancing financial cybersecurity via advanced machine learning: analysis, comparison
Authors: Odette Boussi Grace , Gupta Himanshu , Hossain Syed Akhter
Published: February 2025

The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95%.
Optimizing deep learning models from multi-objective perspective via Bayesian optimization
Authors: Mohamad Rom Abdul Rahman , Jamil Nursuriati , Ibrahim Shafaf
Published: February 2025

Optimizing hyperparameters is crucial for enhancing the performance of deep learning (DL) models. The process of configuring optimal hyperparameters, known as hyperparameter tuning, can be performed using various methods. Traditional approaches like grid search and random search have significant limitations. In contrast, Bayesian optimization (BO) utilizes a surrogate model and an acquisition function to intelligently navigate the hyperparameter space, aiming to provide deeper insights into performance disparities between naïve and advanced methods. This study evaluates BO's efficacy compared to baseline methods such as random search, manual search, and grid search across multiple DL architectures, including multi-layer perceptron (MLP), convolutional neural network (CNN), and LeNet, applied to the Modified National Institute of Standards and Technology (MNIST) and CIFAR-10 datasets. The findings indicate that BO, employing the tree-structured parzen estimator (TPE) search method and expected improvement (EI) acquisition function, surpasses alternative methods in intricate DL architectures such as LeNet and CNN. However, grid search shows superior performance in smaller DL architectures like MLP. This study also adopts a multi-objective (MO) perspective, balancing conflicting performance objectives such as accuracy, F1 score, and model size (parameter count). This MO assessment offers a comprehensive understanding of how these performance metrics interact and influence each other, leading to more informed hyperparameter tuning decisions.
Event detection in soccer matches through audio classification using transfer learning
Authors: Utsav Gadhia Bijal , Modasiya Shahid S.
Published: February 2025

Addressing the complexities of generating sports summaries through machine learning, our research aims to bridge the gap in audio-based event detection, particularly in soccer games. We introduce an extended ResNet-50 deep learning approach for soccer audio, emphasizing key moments from large soccer content archives through the use of transfer learning. The proposed model accurately classifies soccer audio segments into two categories: i) events, representing crucial in-game occurrences and ii) no events, denoting less impactful moments. The model involves complete audio preprocessing, the implementation of proposed model using transfer learning and the classification of events. The model’s reliability is validated using the dataset soccer action dataset compilation (SADC), involves dataset creation by football fans. Comparative analysis with pre-trained models such as VGG19, DesNet121, and EfficientNetB7 demonstrates the superior performance of the extended ResNet-50 based approach. Results across different epochs reveal consistently high accuracy, precision, recall, and F1-score, emphasizing the proposed model's effectiveness in event detection through audio classification. The paper concludes that the proposed model offers a robust solution for detecting an event from audio of soccer sports providing valuable insights for fans, analysts, and content creators to identify interested moments from soccer game with low failure.
Adaptive kernel integration in visual geometry group 16 for enhanced classification of diabetic retinopathy stages in retinal images
Authors: Hiri Mustafa , Ourdani Nabil , Chrayah Mohamed , Alsadoon Abeer , Aknin Noura
Published: February 2025

Diabetic retinopathy (DR) is a major cause of vision impairment globally, with early detection remaining a significant challenge. The limitations of current diagnostic methods, particularly in identifying early-stage DR, highlight a pressing need for more accurate diagnostic technologies. In response, our research introduces an innovative model that enhances the visual geometry group 16 (VGG16) architecture with adaptive kernel techniques. Traditionally, the VGG16 model deploys consistent kernel sizes throughout its convolutional layers. In this study, multiple convolutional branches with varying kernel sizes (3×3, 5×5, and 7×7) were seamlessly integrated after the 'block5_conv1' layer of VGG16. These branches were adaptively merged using a softmax-weighted combination, enabling the model to automatically prioritize kernel sizes based on the image's intricate features. To combat the challenge of imbalanced datasets, the synthetic minority over-sampling technique (SMOTE) was employed before training, harmonizing the distribution of the five DR stages. Our results are promising, showing a training accuracy above 94.17% and a validation accuracy over 90.24%, our model significantly outperforms traditional methods. This study represents a significant stride in applying adaptive kernels to deep learning for precise medical imaging tasks. The model's accuracy in classifying DR stages highlights its potential as a valuable diagnostic tool, paving the way for future enhancements in DR detection and management.
Two-dimensional Klein-Gordon and Sine-Gordon numerical solutions based on deep neural network
Authors: Nouna Soumaya , Nouna Assia , Mansouri Mohamed , Tammouch Ilyas , Achchab Boujamaa
Published: February 2025

Due to the well-known dimensionality curse, developing effective numerical techniques to resolve partial differential equations proved a complex problem. We propose a deep learning technique for solving these problems. Feedforward neural networks (FNNs) use to approximate a partial differential equation with more robust and weaker boundaries and initial conditions. The framework called PyDEns could handle calculation fields that are not regular. Numerical exper- iments on two-dimensional Sine-Gordon and Klein-Gordon systems show the provided frameworks to be sufficiently accurate.
Automatic detection of dress-code surveillance in a university using YOLO algorithm
Authors: Tantra Benjamin Jason , Widjaja Moeljono
Published: February 2025

Dress-code surveillance is a field that utilizes an object detection model to en- sure that people wear the proper attire in workplaces and educational institutions. The case is the same within universities, where students and staff must adhere to campus clothing guidelines. However, campus security still enforces univer- sity student clothing manually. Thus, this experiment creates an object detection model that can be used in the campus environment to detect if students are wear- ing appropriate clothing. The model developed for this research has reached an f1-score of 45% with an overall 51.8% mean accuracy precision. With this, the model has reached a satisfactory state with room for further improvements.
Enhancing emotion recognition model for a student engagement use case through transfer learning
Authors: Qarbal Ikram , Sael Nawal , Ouahabi Sara
Published: February 2025

Distance education has been prevalent since the late 1800s, but its rapid expansion began in the late 1990s with the advent of the online technological revolution. Distance learning encompasses all forms of training conducted without the physical presence of learners or teachers. While this mode of education offers great flexibility and numerous advantages for both students and teachers, it also presents challenges such as reduced concentration and commitment from students, and difficulties in course supervision for teachers. This article aims to study student engagement on distance learning platforms by focusing on emotion detection. Leveraging various existing datasets, including the Facial Expression Recognition 2013 (FER2013), the Karolinska Directed Emotional Faces (KDEF), the extended Cohn-Kanade (CK+), and the Kyung Hee University Multimodal Facial Expression Database (KMU-FED), the proposed approach utilizes transfer learning. Specifically, it exploits the large number and diversity of images from datasets like FER2013, and the high-quality images from datasets like KDEF, CK+, and KMU-FED. The model can effectively learn and generalize emotional cues from varied sources by combining these datasets. This comprehensive method achieved a performance accuracy of 96.06%, demonstrating its potential to enhance understanding of student engagement in online learning environments.
Developing a website for English-speaking practice to English as a foreign language learners at the university level
Authors: Fauzi Iwan , Asi Natalina
Published: February 2025

This study explored the adaptation of the ADDIE instructional model in designing and developing a website for the speaking practice of EFL students at the university level. The feasibility of the website was measured through the evaluation of independent experts from three aspects of rating: web design, instructional content, and language usage. Six lecturers and 64 EFL students were invited to evaluate the website. Two lecturers have expertise in multimedia and informatics, while the four others are two experts in instructional content of English teaching and two lecturers in English linguistic expertise. The assessments exposed that the web is easy to use by students and very practical in supporting students for learning; the content of learning material in the website has manifested the syllabus of English-speaking skill on the specified level; and the language used by the website is matched with the level of students’ language proficiency. Therefore, this study successfully developed a prototype of a web-based language learning product that helps students practice English speaking at the intermediate level.
Classification of upper gastrointestinal tract diseases using endoscopic images
Authors: Tran Thanh Hai , Nguyen Van-Tuan , Dao Viet-Hang , Nguyen Phuc-Binh , Nguyen Thanh-Tung , Vu Hai
Published: February 2025

Automatic classification and disease detection in medical images, aided by machine learning, provide crucial support to prevent overlooked instances and ensure prompt treatment of diseases. Despite impressive achievements in the field of polyp detection from endoscopic images, classification of other diseases, such as reflux esophagitis, esophageal cancer, gastritis, gastric cancer, and duodenal ulcer, is still subject to significant limitations and remains a challenging area of study because of their different and more challenging characteristics. This paper proposes a method to roughly classify the diseases from the whole images by deep learning. In particular, we focus on identifying hard samples from the training dataset and enriching them with some fundamental augmentation techniques. We then employ a cutting-edge model, specifically ResNet, for the final classification stage. Additionally, we enhance the original ResNet’s loss function by incorporating another loss function called focal loss. These modifications play a crucial role in boosting the accuracy of the ResNet model. Our proposed method outputs the disease category and corresponding heat map showing the area of interest. It achieved very promising accuracy (99.55%) for the classification of five lesions on our self-collected dataset. It serves a dual purpose. Firstly, it aids in the training of novice endoscopists, enabling them to gain valuable experience. Secondly, it offers a rapid solution for annotating extensive volumes of endoscopic image data at the label level.
DualVitOA: A dual vision transformer-based model for osteoarthritis grading using x-ray images
Authors: Ruiyun Qiu , Abdul Rahim Siti Khatijah Nor , Jamil Nursuriati , Hamzah Raseeda
Published: February 2025

Knee osteoarthritis (OA) is a primary factor contributing to reduced activity and physical impairment in older individuals. Early identification and treatment of knee OA can assist patients in delaying the advancement of the condition. Currently, knee OA is detected early using X-ray images and assessed based on the Kellgren-Lawrence (KL) grading system. Doctors' assessments are subjective and can vary among different doctors. The automatic knee OA grading and diagnosis can assist doctors and help doctors reduce their workload. A new novel network called dual-vision transformer (ViT) OA is proposed to automatically diagnose knee OA. The network utilizes pre-processing technologies to process the data before doing classification operations using the Dual-ViT network. The suggested network outperformed neural networks like ResNet, DenseNet, visual geometry group (VGG), inception, and ViT in terms of accuracy and mean absolute error (MAE), and achieved an accuracy of 78.4 and MAE of 0.471, demonstrating its effectiveness.
Security in smart cities using YOLOv8 to detect lethal weapons
Authors: Rodriguez-Rosas Ederson , Castillo-Turpo Aron , Acuna-Condori Kevin , Paiva-Peredo Ernesto
Published: February 2025

The increase in the illegal use of lethal weapons at a global level has reached
worrying figures, resulting in an increase in assaults and armed robberies. Based on the above, closed circuit television (CCTV) surveillance systems emerge as an alternative solution. Therefore, the use of artificial intelligence is explored in order to detect the presence of lethal weapons in images accurately. In this study, a convolutional neural network model YOLOv8 is trained. A database including 4104 images with the presence of lethal weapons is generated. The Google Colab platform is used for the training phase, since it provides us with a free graphic processing unit (GPU), and the YOLOv8x and YOLOv8n models are used for comparison. The results indicate an advantage when using the YOLOv8 models, and when comparing them with similar models already proposed in the studied literature, we can conclude that our model stands out with an accuracy of 89.56% in the detection of lethal weapons. In conclusion, we were able to obtain a model capable of detecting lethal weapons in CCTV images, in addition to being able to be used in applications that require real-time detection. 
Sign language emotion and alphabet recognition with hand gestures using convolution neural network
Authors: Patil Varsha K. , Pawar Vijaya R. , Patil Aditya , Bairagi Vinayak
Published: February 2025

American sign language (ASL) is a special means of interaction for hard-of-hearing individuals and has precise conventional rules. Since the general public does not know these sign language protocols, there is a need to have an efficient automatic sign-emotion recognition system. The objective of this paper is, to develop a framework that recognizes standard hand gestures. The gesture represents emotions and alphabet. This paper covers the methodology, results and performance factors, for experimentations. This experimentation of ASL-based alphabet and emotion recognition is novel as till now many efforts of alphabets categorization are done but this is the new direction of research where emotions, such as together’, ‘happy’, ‘peace, ’sad’, ‘confused’, and ‘love’ are captured and automatically classified with hand signs. We mention our approach to increase ‘accuracy’, wherein we capture images and regions of interest (ROI). In this article, a specifically designed convolution neural network (CNN), is used to identify emotions from hand gestures and the addition of ROI enhances accuracy. The captured hand gesture dataset of the size of 94,000 images. “peace” sign emotion has the highest recognition rate (‘98.95%’). Alphabet’s “P” and “Q” sign ASL alphabets have the maximum recognition rate of signs. In all, very impressive accuracy of “92%” and above is detected. The limits of the experimentation are as mentioned i) there is no repeatability of accuracy for the same hand gesture; ii) The distance and angle of hand gestures with camera are crucial factors for an experiment; and iii) the alphabet recognition system is not working for the alphabets “J” and “Z”.
An Assessment of How Domain Experts Evaluate Machine Learning in Operational Meteorology
Authors: Harrison David R. , McGovern Amy , Karstens Christopher D. , Bostrom Ann , Demuth Julie L. , Jirak Israel L. , Marsh Patrick T.
Published: February 2025

As an increasing number of machine learning (ML) products enter the research-to-operations (R2O) pipeline, researchers have anecdotally noted a perceived hesitancy by operational forecasters to adopt this relatively new technology. One explanation often cited in the literature is that this perceived hesitancy derives from the complex and opaque nature of ML methods. Because modern ML models are trained to solve tasks by optimizing a potentially complex combination of mathematical weights, thresholds, and nonlinear cost functions, it can be difficult to determine how these models reach a solution from their given input. However, it remains unclear to what degree a model’s transparency may influence a forecaster’s decision to use that model, or if that impact differs between ML and more traditional (i.e., non-ML) methods. To address this question, a survey was offered to forecaster and researcher participants attending the 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE) with questions about howparticipants subjectively perceive and compare machine learning products to more traditionally derived products. Results from this study revealed few differences in how participants evaluated machine learning products compared to other types of guidance. However, comparing the responses between operational forecasters, researchers, and academics exposed notable differences in what factors the three groups considered to be most important for determining the operational success of a new forecast product. These results support the need for increased collaboration between the operational and research communities.
Torsional behavior of peripheral vascular stents: The role of stent design parameters
Authors: Shen Xiang , Chen Jiahao , Xu Yue , Liu Qiang , He Zewen , Wang Lei , Sun Peng , Zhu Hongfei , Yan Hengfeng
Published: February 2025

Torsional performance is a critical evaluation criterion in the design of peripheral vascular stents, enabling them to adapt to the deformation of the vessel to reduce damage to the vascular wall and thus avoiding in-stent restenosis (ISR). Therefore, this study employed the finite element method (FEM) to investigate the impact of stent design parameters on the torsional behavior of self-expanding peripheral vascular stents. These parameters included stent diameter and thickness, as well as the length and width of struts and links. Results revealed that among all parameters, strut length and width significantly influence the stent torsional performance, whereas link width has a lesser effect. Notably, increasing strut length and decreasing strut width were found to significantly reduce the required torque, with the twist metric (TM) reduced by approximately 86.3% when strut length increased from 1.2 to 2.8 mm. Moreover, reductions in stent diameter and thickness, alongside an increase in link length, further contributed to a decrease in TM, thereby enhancing the stent torsional performance. This study may provide insights for better peripheral stent design and clinical decision of stent choice.
Supporting student-centred learning with mobile technology: UTAUT and Hofstede’s dimensions of culture / Apoyo al aprendizaje centrado en el alumno mediante la tecnología móvil: la UTAUT y las dimensiones culturales de Hofstede
Authors: Gan Chin Lay , Bong Yok Han , Goh Gerald Guan Gan , Liew Tze Wei
Published: February 2025

This research aims to study the pivotal factors that encourage higher education students to use mobile technology to promote student-centred learning. A mobile technology–enhanced student-centred learning (MTSCL) model was proposed by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Hofstede’s national cultural dimensions. This study recruited 153 Malaysian higher education students via purposive and snowballing sampling methods. Data were analysed using partial least square-structural equation modelling (PLS-SEM). The findings showed that effort expectancy and long-term orientation predict mobile technology adoption intention. Mobile technology can accelerate technology adoption in the classrooms of tertiary education to support student-centred, lifelong and continuous learning.
Patient-reported outcomes of quality of life before and after autologous hematopoietic stem cell transplantation for multiple sclerosis
Authors: Mitrache Desaga Ann-Christine , Noui Yassine , Silfverberg Thomas , Carlson Kristina , Burman Joachim
Published: February 2025

Background:

Autologous hematopoietic stem cell transplantation (AHSCT) is a therapeutic intervention for multiple sclerosis (MS) that has gained increased attention in the last decade. The impact of this intervention on the quality of life (QoL) of patients with MS remains unclear.

Objective:

The aim of this study was to investigate the impact of AHSCT on QoL in patients with MS using Multiple Sclerosis Impact Scale (MSIS-29) scores.

Methods:

In this observational retrospective cohort study, patients with relapsing-remitting MS treated with AHSCT in Sweden from 2004, when the first transplant was performed, until 31 December 2019, were considered for participation. Anonymized outcome data were extracted from the Swedish MS registry in May 2022.

Results:

Out of 213 patients assessed for eligibility in the study, 96 were included in the final analysis. After a median follow-up of 5.2 (IQR 3.2–6.8) years, 58% improved, 14% remained unchanged and 28% worsened in the physical domain of the MSIS-29. In the psychological domain, 63% improved, 18% remained unchanged and 19% worsened. Improvements in both domains occurred early, within the first year following intervention.

Conclusions:

Treatment intervention with AHSCT is associated with a clinically meaningful improvement in QoL.
Language teachers’ attitudes to working with multilingual refugee learners: Do teacher Openness to Experience, Intercultural Sensitivity and plurilingualism matter?
Authors: Rokita-Jaśkow Joanna , Nosidlak Katarzyna , Wolanin Agata , Król-Gierat Werona
Published: February 2025

This article aims to explore second language (L2) teachers’ attitudes to working with multilingual refugee learners in the context of a sudden shift towards an increasingly multilingual environment caused by the influx of Ukrainian refugee children in 2022 to Polish schools. The teachers have been prepared to teach predominantly monolingual classes and have not obtained any training to meet the needs of migrant and multilingual learners; thus, they had to rely on their personal resources facing this suddenly increasingly multilingual and multicultural environment. A cross-sectional survey study was designed to identify how attitudes of teachers of English and Polish as a foreign language ( n = 70) developed towards teaching multilingual refugee learners and to what extent these correlated with teacher Openness to Experience, Intercultural Sensitivity and teachers’ plurilingualism. The quantitative analysis reveals mixed attitudes overall, despite generally positive attitudes to working with Ukrainian refugee learners, and correlating positively with Openness to Experience and plurilingualism, but not Intercultural Sensitivity. These might derive from the complexity of the teaching experience, which goes beyond mere language teaching, and for which the teachers have not received sufficient preparation.
Outcomes of a Community-Driven Planning Process to Create Comprehensive Health and Wellness Plans in Rural Schools
Authors: Ingman Benjamin C. , Loecke Carla , Belansky Elaine S.
Published: February 2025

AIM (Assess, Identify, Make it happen) is a community-engaged, data-driven, strategic planning process for school districts to develop and implement comprehensive health and wellness plans (CHWPs) aligned with the Whole School, Whole Community, Whole Child (WSCC) model. AIM was facilitated in 21 rural school districts and an 81-item survey was administered to participants completing this process (n = 236). Those surveyed indicated that the benefits of AIM were more pronounced than challenges. Top benefits included developing a shared commitment and long-term mindset to promote health and wellness in schools. Top challenges included fitting AIM into already busy schedules and getting buy-in from all task force members. The most common health problems prioritized in district CHWPs were poor mental health and alcohol tobacco and other drugs. These results support the effectiveness of AIM as a method for developing community-engaged, data-driven CHWPs with rural school districts. Future efforts to create school plans to promote health should account for the benefits and challenges noted in this work. Furthermore, approaches designed for rural and historically under-resourced schools are necessary to promote the equitable implementation of the WSCC model.
Feature Importance in Health Insurance Data Using Variance-Based Sensitivity Analysis
Authors: Spiller Jakob C , Hasan Md Nahid
Published: February 2025

Variance-based sensitivity analysis serves as a crucial tool for assessing the variability of inputs on the output of complex mathematical models. In this study, we examine Sobol indices, a class of variance-based sensitivity analysis, to quantify the importance of each input variable on the overall variability of the model output. We apply Sobol indices within the framework of a regression model to identify the most importance features (also known as predictors) in predicting total medical expenses charged per year for the health insurance plan. Our findings reveal that “smoker” emerged as the most important feature impacting health insurance charges, followed by “age” and “bmi” as the second and third most important features, respectively. This application not only demonstrates the effectiveness of Sobol’s indices in regression models but also suggests possible model simplifications.
Taking Chances: The Analysis of Simplified Parcheesi
Authors: Ahmed Aden O
Published: February 2025

Parcheesi is a compelling example of a two-player, strictly competitive game of perfect information with chance moves. In this game, there are only two possible outcomes: W (a win for Player I and a loss for Player II) or L (a loss for Player I and a win for Player II). The primary motivation for a rational player is to maximize the probability of winning. Parcheesi is played between Player I (White) and Player II (Black) on a (2 m + 1) ×  n board. The central cell in the first column of the board is shaded, marking the winning square. The objective is to be the first to reach this shaded square by following specific routes and rules. In this paper, we will focus on three board configurations: the 3 × 2, 3 × 3, and 5 × 2 boards. For the first two configurations, we will provide brief solutions. A comprehensive analysis will be dedicated to the 5 × 2 board. Finally, we will conclude with a conjecture regarding the general (2 n + 1) × 2 board configuration.
How anaesthetic drugs are selected to account for patient and surgical factors: A case report
Authors: Scott Bevan Michael
Published: February 2025

The patient’s health and nature of the surgery are important considerations when selecting anaesthetic drugs. This report considers the anaesthetic drugs selected for a patient undergoing day case diagnostic laparoscopy for chronic pelvic pain. Appropriate drug selection for patients with a suspected history of cancer is crucial. Anaesthetic agent selection requires a balance between possible postoperative complications and long-term survival. Total intravenous anaesthesia was selected for its favourable long-term survival rate, its effect on reducing immunosuppression, and to aid with providing a bloodless field. Metaraminol was selected to prevent profound hypotension and to provide normotension when assessing bleeding. Sugammadex has a high binding affinity for steroidal hormones, potentially contributing to contraceptive failure. Neostigmine was selected as the reversal agent due to the childbearing age of the patient. A muscle relaxant was selected to improve surgical conditions via a pneumoperitoneum. Rocuronium was selected due to its rapid onset and rapid reversibility with sugammadex. A thorough understanding of anaesthetic drugs is required if they are to be used appropriately. Inappropriate anaesthetic drug selection may provide sub-optimal operating conditions, contribute to unplanned pregnancy, worsen patient outcomes, and delay discharge.
Efficacy and safety of sequential cutting of nasobiliary tubes as an alternative to stent placement after nasobiliary drainage for the treatment of hilar malignant biliary stricture: a retrospective cohort study
Authors: Chen Wangyang , Huang Peiyao , Gu Xinyi , Liu Xiaodie , Ying Xiuzhiye , Yang Jianfeng
Published: February 2025

Background:

The rising incidence of hilar malignant bile duct strictures poses challenges for diagnosis and treatment. While endoscopic treatment is essential for relieving obstruction, it carries a high risk of postoperative cholangitis. Sequential cutting of nasobiliary tubes as an alternative to stent placement after nasobiliary drainage may emerge as an effective strategy to mitigate these complications.

Objectives:

This study aimed to evaluate the efficacy and safety of nasobiliary tube cutting after nasobiliary drainage versus conventional stent placement in reducing postoperative cholangitis in patients with hilar malignant biliary strictures.

Design:

A retrospective cohort study.

Methods:

From 2015 to 2023, 208 patients were divided into two groups: nasobiliary tube cutting group ( n = 103) and conventional stent group ( n = 105). The primary outcome was postoperative cholangitis, with secondary outcomes including drainage success, bilirubin reduction, re-interventions, complications, and hospital stay.

Results:

Cholangitis occurred in 11.7% of the nasobiliary tube cutting group and 26.7% of the conventional stent group ( p = 0.006). Successful drainage was achieved in 84 patients (81.6%) in the nasobiliary tube cutting group and 78 patients (74.3%) in the conventional stent group, with no statistically significant difference ( p = 0.207). Patients whose total bilirubin reduction >50% within 7 days were significantly higher in the nasobiliary tube cutting group (48.5% vs 27.6%, p = 0.002). There were no significant differences between the groups in the number of endoscopic retrograde cholangiopancreatography procedures or re-interventions required ( p > 0.05). Except for postoperative cholangitis, other postoperative complications were comparable between the groups ( p > 0.05). The postoperative hospital stay was significantly longer in the nasobiliary tube cutting group, with a median duration of 10.0 (7.0, 14.0) days compared to 7.0 (5.5, 12.5) days in the conventional stent group ( p = 0.024). Bismuth-Corlette Type IV was identified as an independent risk factor for cholangitis (OR = 3.207, 95%CI: 1.253–8.210, p = 0.015).

Conclusion:

For patients with hilar malignant biliary stricture, sequential cutting of nasobiliary tubes as an alternative to stent placement after nasobiliary drainage may reduce the incidence of postoperative cholangitis and achieve early successful drainage, but it may result in a prolonged postoperative hospital stay. Bismuth-Corlette Type IV is an independent risk factor for the development of postoperative cholangitis.
Panendoscopy for Head and Neck Cancers: Detection of Synchronous Second Primary Cancers, Complications and Cost-Benefit Analysis: A Systematic Review
Authors: Bellavance Samuel , Khoury Michel , Bissada Eric , Ayad Tareck , Christopoulos Apostolos , Tabet Jean-Claude , Guertin Louis , Tabet Paul
Published: February 2025

Importance

In patients with head and neck squamous cell carcinoma (HNSCC), the discovery of a second synchronous primary cancer of the aerodigestive tract (SSPCA) significantly impacts management and prognosis. Recent advances in imaging have increasingly allowed for identifying SSPCA before performing panendoscopy, raising questions about the latter’s role.

Objective

To establish the incidence of SSPCA and panendoscopy’s impact on management. Complications and costs associated with panendoscopy were also assessed.

Design

Systematic review following the preferred reporting items for systematic reviews and meta-analysis guidelines.

Setting

Operating room panendoscopy.

Participants

Identifiable HNSCC undergoing initial staging workup.

Intervention

Panendoscopy under general anesthesia for SSPCA detection.

Main Outcome Measures

Incidence of SSPCA in HNSCC, change in management caused by panendoscopy, incidence of panendoscopy complications, costs for panendoscopy.

Results

51 studies were included (n = 19,914 patients). SSPCA was present in 6.4% (n = 467/7262) of all panendoscopies. Among patients who had a prior computed tomography (CT) of the neck and chest, a change in management resulting from SSPCA detected through panendoscopy occurred in only 1.1% of cases (n = 3/268), and in 0% of cases for those who had a positron-emission tomography-computed tomography (PET) (n = 0/544). The rate of major complications of panendoscopy was 0.7% (n = 58/8386). Only two recent studies in a private healthcare system reported panendoscopy costs ranging from $3802 USD to $17,296 USD.

Conclusions

The role of panendoscopy in the initial workup of HNSCC should be limited to confirming suspicious findings from initial CT or PET. The incidence of major complications for panendoscopy is low but carries a significant financial burden for patients in the private American healthcare system. More studies are needed to assess the cost-effectiveness of panendoscopies for SSPCA detection in a public healthcare system.

Relevance

Confirms the lack of benefit for systematic panendoscopy for SSPCA detection in HNSCC patients when initial workup includes a CT of the neck and chest or PET.